KR101875397B1 - System and methods for coping with doppler effects in distributed-input distributed-output wireless systems - Google Patents

Abstract

Systems and methods for compensating for the adverse effects of Doppler on the performance of DIDO systems are described. One embodiment of such a system adaptively adjusts active BTSs to different UEs by tracking channel condition changes using different selection algorithms. Other embodiments use channel prediction to estimate future CSI or DIDO precoding weights and thus eliminate errors due to outdated CSI.

Description

[0001] SYSTEM AND METHOD FOR COPING WITH DOPPLER EFFECTS IN DISTRIBUTED-INPUT DISTRIBUTED-OUTPUT WIRELESS SYSTEMS [0002]

Related Applications

This application is a continuation in part of commonly assigned pending U.S. patent applications, below.

U.S. Application No. 12 / 917,257, filed November 1, 2010, entitled " Systems And Methods To Coordinate Transmissions In Distributed Wireless Systems Via User Clustering "; U.S. Serial No. 12 / 802,988, filed June 16, 2010, entitled " Interference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO) U.S. Patent Application No. 12 / 802,976, filed June 16, 2010, entitled " System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements ", U.S. Patent No. 8,170,081 issued May 1, 2012 number; 12 / 802,974, filed June 16, 2010, entitled " System And Method For Managing Inter-Cluster Handoff Of Clients Which Traverse Multiple DIDO Clusters "; 12 / 802,989, filed June 16, 2010, entitled " System Based On Detected Velocity Of The Client ", entitled " System And Method For Managing Handoff Of A Client Between Different Distributed-Input-Distributed- ; U.S. Serial No. 12 / 802,958, filed June 16, 2010, entitled " System And Method For Power Control And Antenna Grouping In A Distributed-Input-Distributed-Output (DIDO) Network; 12 / 802,975, filed June 16, 2010, entitled " System And Method For Link Adaptation In DIDO Multicarrier Systems "; 12 / 802,938, filed June 16, 2010, entitled " System And Method For DIDO Precoding Interpolation In Multicarrier Systems "; 12 / 630,627, filed December 3, 2009, entitled " System and Method For Distributed Antenna Wireless Communications "; No. 12 / 143,503, filed June 20, 2008, entitled " System and Method For Distributed Input-Distributed Output Wireless Communications ", U.S. Patent No. 8,160,121, issued April 17, 2009; U.S. Patent Application No. 11 / 894,394, filed on August 20, 2007, entitled "System and Method for Distributed Input Distributed Output Wireless Communications," U.S. Patent No. 7,599,420, issued October 6, 2009; U.S. Patent Application No. 11 / 894,362, filed August 20, 2007, entitled "System and method for Distributed Input-Distributed Wireless Communications," U.S. Patent No. 7,633,994, issued Dec. 15, 2009; U.S. Patent Application No. 11 / 894,540, filed August 20, 2007, entitled "System and Method For Distributed Input-Distributed Output Wireless Communications," U.S. Patent No. 7,636,381, issued Dec. 22, 2009; U.S. Patent Application No. 11 / 256,478, filed October 21, 2005, entitled "System and Method For Spatial-Multiplexed Tropospheric Scatter Communications," U.S. Patent No. 7,711,030, issued May 4, 2010; U.S. Application Serial No. 10 / 817,731, filed April 2, 2004, entitled "System and Method For Enhancing Near Vertical Incidence Skywave (" NVIS ") Communication Using Space-Time Coding, U. S. Patent No. 7,885, 354, which is incorporated herein by reference.

Prior art multi-user wireless systems may include only a single base station or multiple base stations.

(E. G., 2.4 GHz < RTI ID = 0.0 > 802.11b, g < / RTI > or 802.11b) attached to a broadband wired Internet connection in an area where no other WiFi access point (e.g. a WiFi access point attached to a DSL in rural home) n protocols) is an example of a relatively simple multi-user wireless system that is a single base station shared by one or more users within its transmission range. If the user is in the same room as the wireless access point, the user will typically experience a high-speed link with little interruption in transmission (e.g., there may be packet loss from 2.4 GHz interferers, such as microwave ovens, There may be no packet loss from the spectrum sharing with other WiFi devices). In the event that the user is a halfway distance, or if there are a few obstacles in the path between the user and the Wi-Fi access point, the user will likely experience an intermediate rate link. If the user is approaching the edge of the range of the Wi-Fi access point, the user will likely experience a slow link, and if the channel change causes the signal SNR to fall below the usable level, a periodic drop-out You can. Finally, if the user is out of range of the Wi-Fi base station, the user will have no link at all.

When multiple users access the Wi-Fi base station simultaneously, the available data throughput is shared between them. Typically, different users require different throughputs for a Wi-Fi base station at a given time, but at a point when the aggregated throughput requirement exceeds the available throughput from the Wi-Fi base station to users, some or all of the users may request You will receive less data throughput than you have. In extreme situations where Wi-Fi access points are shared among a very large number of users, the throughput for each user may be lowered and worsened, and the data throughput for each user may be limited to long periods of no data throughput Lt; / RTI > can reach short bursts that are separated by time, and other users are served during that time. This " non-uniform " data transfer can compromise certain applications such as media streaming.

Adding additional Wi-Fi base stations in situations involving a large number of users would only be helpful to some extent. In the US 2.4 GHz ISM band there are three non-interfering channels that can be used for Wi-Fi, and when three Wi-Fi base stations in the same coverage area are configured to use different non-interfering channels, respectively, The aggregate throughput of the region will be increased by a factor of three. Beyond this, however, adding more Wi-Fi base stations in the same coverage area will not increase the aggregate throughput because they will begin to share the same available spectrum among them, thereby " alternating " Time-division multiplexed access (TDMA). This situation is often observed in coverage areas with high population densities, such as within multiple residential units. For example, a user in a large apartment building with a Wi-Fi adapter may serve other users in the same coverage area even if the user's access point is in the same room as the client device accessing the base station (e.g., Many other interfering Wi-Fi networks will probably experience very poor throughput. In such a situation, the link quality will probably be good, but the user will be interfered with neighboring Wi-Fi adapters operating in the same frequency band, so the effective throughput for the user will be low.

Current multi-user wireless systems, including both unlicensed spectrum and authorized spectrum such as Wi-Fi, suffer from several limitations. These include the coverage area, the downlink (DL) data rate, and the uplink (UL) data rate. An important goal of next generation wireless systems such as WiMAX and LTE is to improve the coverage area and DL and UL data rates through multiple-input multiple-output (MIMO) technology. MIMO improves link quality (resulting in wider coverage) or data rate (by creating multiple non-interfering spatial channels for all users), using multiple antennas at the transmitting and receiving sides of the wireless links. However, if sufficient data rates are available for all users (note that the terms "user" and "client" are used interchangeably herein), multiuser MIMO (MU-MIMO) It may be desirable to create non-interfering channels for multiple users (rather than a single user) using channel space diversity. For example, see references below.

G. Caire and S. Shamai, "On the achievable throughput of a multiantenna Gaussian broadcast channel," IEEE Trans. Info.Th., vol. 49, pp. 1691-1706, July 2003.

P. Viswanath and D. Tse, " Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality, " IEEE Trans. Info. Th., Vol. 49, pp. 1912-1921, Aug. 2003.

S. Vishwanath, N. Jindal, and A. Goldsmith, " Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels, IEEE Trans. Info. Th., Vol. 49, pp. 2658-2668, Oct. 2003.

W. Yu and J. Cioffi, " Sum capacity of Gaussian vector broadcast channels, " IEEE Trans. Info. Th., Vol. 50, pp. 1875-1892, Sep. 2004.

M. Costa, " Writing on dirty paper, " IEEE Transactions on Information Theory, vol. 29, pp. 439-441, May 1983.

M. Bengtsson, " A pragmatic approach to multi-user spatial multiplexing, " Proc. of Sensor Array and Multichannel Sign. Workshop, pp. 130-134, Aug. 2002.

K.-K. Wong, R. D. Murch, and K. B. Letaief, " Performance enhancement of multiuser MIMO wireless communication systems, " IEEE Trans. Comm., Vol. 50, pp. 1960-1970, Dec. 2002.

M. Sharif and B. Hassibi, " On the capacity of MIMO broadcast channel with partial side information, " IEEE Trans. Info.Th., vol. 51, pp. 506-522, Feb. 2005.

For example, MIMO 4x4 systems (i.e., four transmit antennas and four receive antennas), 10 MHz bandwidth, 16-QAM modulation, and forward error with rate 3/4 (producing a spectral efficiency of 3 bps / In forward error correction (FEC) coding, the ideal peak data rate achievable at the physical layer for all users is 4x30 Mbps = 120 Mbps, which is what is needed to transmit high-definition video content (which may require only about 10 Mbps) . In the ideal scenarios (i.e., independent, equally distributed (iid) channels), in the MU-MIMO systems with four transmit antennas, four users and a single antenna per user, May be shared among users, and channel space diversity may be used to create four parallel 30Mbps data links for users.

Different MU-MIMO schemes are described in, for example, 3GPP, " Multiple Input Multiple Output in UTRA ", 3GPP TR 25.876 V7.0.0, Mar. 2007; 3GPP, " Base Physical channels and modulation ", TS 36.211, V8.7.0, May 2009; And as part of the LTE standard as described in 3GPP, " Multiplexing and channel coding ", TS 36.212, V8.7.0, May 2009. However, these schemes can only provide up to twice the DL data rate improvement using four transmit antennas. In standard and proprietary cellular systems by companies such as ArrayComm (e.g., ArrayComm, " Field-proven results ", see http://www.arraycomm.com/serve.php?page=proof) Practical implementations of MIMO techniques have achieved DL data rate increases of up to about three times (using four transmit antennas) through space division multiple access (SDMA). An important limitation of MU-MIMO schemes in cellular networks is the absence of spatial diversity on the transmit side. Space diversity is a function of antenna spacing and multipath angular width in wireless links. In cellular systems employing MU-MIMO techniques, the transmit antennas of the base station are typically clustered together and have a limited area on antenna support structures (referred to herein as " towers " And only one or two wavelengths due to limitations on where the towers can be placed. Moreover, since cell towers are typically installed higher than obstacles (over 10 meters) to provide wider coverage, the multipath angular width is smaller.

Other practical problems of cellular system deployment include excessive cost (due to, for example, urban restrictions on antenna placement, real estate costs, physical obstacles, etc.) and limited availability of locations for cellular antenna placement, quot; backhaul ") < / RTI > In addition, cellular systems are often difficult to reach clients that are deeply located within a building due to losses from walls, ceilings, floors, furniture, and other obstacles.

In fact, the overall concept of a cellular architecture for a wireless wide area network is that a somewhat rigid arrangement of cellular towers, alternation of frequencies between adjacent cells, and the like, in order to prevent interference between transmitters (base stations or users) And often requires sectorization. As a result, a given sector of a given cell eventually becomes a shared block of DL and UL spectra between all users in the cell sector, and then this block is shared primarily among these users only in the time domain. For example, cellular systems based on both time division multiple access (TDMA) and code division multiple access (CDMA) share spectrum among users in the time domain. By overlaying such cellular systems with sectorization, perhaps 2-3 times the spatial domain benefit can be achieved. And then, by overlaying such cellular systems with an MU-MIMO system as described above, perhaps a space-time domain gain of 2-3 times again can be achieved. However, when cells and sectors of a cellular system are typically located in fixed locations, often indicated by the location where the towers can be placed, the user density (or data rate demand) at a given time is well suited to the tower / Even if such a limited benefit is not matched, it is difficult to use. Cellular smartphone users often experience this today's result of the user being able to talk on the phone or download the web page without any problems, and then suddenly, after driving (or even walking) to the new location, You will get angry or even lose access completely. However, on another day, the user may experience exactly the opposite phenomenon at each location. Assuming that the environmental conditions are the same, the user probably perceives that the user density (or data rate demand) is highly variable, but the total available spectrum (and hence the use of conventional techniques) shared among users at a given location The overall data rate) is mostly fixed.

In addition, prior art cellular systems rely on the use of different frequencies in different neighboring cells, typically three different frequencies. For a given amount of spectrum, this reduces the available data rate by a factor of three.

Thus, in short, prior art cellular systems may lose 3 times as much in spectrum utilization as a result of cellularization, possibly tripling spectrum utilization through sectorization, and possibly 3 times improvement through MU-MIMO technology, The use of pure spectrum can be 3 * 3/3 = 3 times. In addition, the bandwidth is typically divided among users in a time domain based on which sector of a cell belongs to a user at a given time. Although the data rate requirement of a given user is typically independent of the user's location, there is much more inefficiency that arises from the fact that the available data rate varies with the link quality between the user and the base station. For example, a user further away from a cellular base station will typically have a lower available data rate than a user closer to the base station. As the data rate is typically shared among all users in a given cellular sector, this results in all users having high data rate requirements from distant users with poor link quality (e.g., at the edge of the cell) , Because those users still require the same amount of data rate, but they will consume more of the shared spectrum to get it.

Other proposed spectrum sharing systems, such as those used by WiFi (e.g., 802.11b, g and n) and those proposed by White Spaces Coalition, share the spectrum very inefficiently, Simultaneous transmissions by base stations cause interference, and therefore systems use collision avoidance and sharing protocols. These spectrum sharing protocols are in the time domain and thus, when there are a large number of interfering base stations and users, regardless of how efficient each of the base stations themselves is in spectrum utilization, jointly the base stations can share time domain sharing of the spectrum Is limited. Similarly, other prior art spectrum sharing systems may be used in similar methods to mitigate interference between base stations (which are cellular base stations with antennas on towers or small base stations such as Wi-Fi access points (APs)). It depends. These methods include limiting transmission power from the base station to limit the range of interference, beamforming (through composite or physical means) to reduce the area of interference, user equipment, base station, or a plurality of both of these And / or MU-MIMO techniques using a clustered antenna of the base station. And, in the case of advanced or currently planned advanced cellular networks, many of these technologies are often used simultaneously.

However, even with advanced cellular systems, it is evident by the fact that a single user can only achieve about three times the increase in spectrum utilization compared to using a single spectrum, both of these techniques can be applied to aggregated data I have not increased the rate almost. In particular, as a given coverage area is scaled with respect to users, it becomes increasingly difficult to scale available data rates to accommodate the growth of users within a given amount of spectrum. For example, in cellular systems, to increase the aggregate data rate within a given region, cells are typically subdivided into smaller cells (often referred to as nanocells or femtocells). Where it is desired to place in a fairly structured pattern to prevent interference between neighboring cells using the same frequencies, while the places where the towers can be placed are limited and the towers provide coverage with minimal " blind zones " , Such small cells can be very expensive. Essentially, the coverage areas must be mapped, the available locations for deploying towers or base stations must be identified, and, if these constraints are given, the designers of the cellular system should be satisfied that they can do the best they can. Of course, if the user data rate requirement increases over time, the designers of the cellular system should try to remap the coverage area again, try to find locations for the towers or base stations, and once again, You have to work inside. Very often, there is no good solution at all, which causes blind spots in the coverage area or insufficient aggregate data rate capacity. That is, the stringent physical placement requirements of cellular systems to prevent interference between towers or base stations that use the same frequency cause significant difficulties and constraints in cellular system design, and often can not meet user data rate and coverage requirements I will not.

So-called prior art " cooperative " and " aware " wireless systems attempt to increase spectrum utilization in a given area by using intelligent algorithms within the radios, so they can minimize interference with each other and / You can "listen" to another spectrum that is potentially waiting to be emptied. Such systems are specifically proposed for use in such spectrums to increase the spectrum utilization of unauthorized spectra.

A mobile ad hoc network (MANET) (see http://en.wikipedia.org/wiki/Mobile_ad_hoc_network) is an example of a cooperative self-organizing network intended to provide peer-to-peer communication, And can potentially mitigate interference between concurrent transmissions that are outside the range of each other, in accordance with sufficiently low power communications. Although a large number of routing protocols have been proposed and implemented for MANET systems (see http://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols for a list of multiple routing protocols in a wide range of classes) Is that they are all techniques for routing (e.g., relaying) transmissions in a manner that minimizes transmitter interference in the available spectrum towards the goals of specific efficiency or reliability paradigms.

All of the prior art multi-user wireless systems attempt to improve spectrum utilization within a given coverage area by utilizing techniques to enable simultaneous spectrum use between base stations and multiple users. In particular, in both of these examples, techniques utilized for using the simultaneous spectrum between base stations and multiple users achieve simultaneous spectrum utilization by multiple users by mitigating interference between waveforms for multiple users. For example, if three base stations each use different frequencies to transmit to one of three users, interference is mitigated because three transmissions occur at three different frequencies. In the case of sectoring from a base station to three different users, each 180 degrees apart from the base station, beamforming prevents overlapping of three transmissions in any user, thereby mitigating interference.

When such techniques are augmented by MU-MIMO, for example, each base station has four antennas, it generates four non-interference spatial channels for users in a given coverage area, thereby increasing the downlink throughput by a factor of four . However, in this case still some technique must be used to mitigate interference between multiple simultaneous transmissions for multiple users in different coverage areas.

And, as described above, such conventional techniques (e.g., cellularization, sectorization) typically not only increase the cost and / or deployment flexibility of multi-user wireless systems, Lt; RTI ID = 0.0 > physical < / RTI > For example, in a cellular system, there may not be enough available locations to install more base stations to create smaller cells. And, in a MU-MIMO system, given a clustered antenna spacing at each base station location, limited spatial diversity asymptotically reduces the benefit of throughput when more antennas are added to the base station.

In addition, for multi-user wireless systems where user location and density are unpredictable, throughput is unpredictable (often rapidly changing), which is inconvenient to the user and may be unavoidable for some applications (e.g., Delivery) becomes impractical or degrades quality. Thus, prior art multi-user wireless systems still need a lot in connection with their ability to provide users with predictable and / or high quality services.

Despite the enormous sophistication and complexity developed over the prior art multi-user wireless systems over time, there are common themes, i.e., transmissions are distributed between different base stations (or ad hoc transceivers) The RF waveform transmissions from the base stations and / or different ad hoc transceivers are structured and / or controlled to prevent them from interfering with each other at a given user's receiver.

Or, in other words, if the user takes the case of simultaneously receiving transmissions from more than one base station or an ad hoc transceiver, interference from multiple simultaneous transmissions reduces the SNR and / or bandwidth of the signal to the user, (Or analog information) that would otherwise be received by the user.

Thus, in a multi-user wireless system, it is necessary to prevent or mitigate such interference with users from multiple base stations or ad hoc transceivers that transmit simultaneously on the same frequency, using one or more spectrum sharing approaches or the like. (E. G., Cellularization) of the physical locations of the base stations, limitations of the power output of the base stations and / or ad hoc transceivers (e.g., limitations of the transmission range), beamforming / sectoring, There are a number of prior art approaches to preventing such interference. In short, all of these spectrum sharing systems are multi-user wireless, which reduces or eliminates data throughput for the affected user when the multiple base stations and / or ad hoc transceivers transmitting simultaneously on the same frequency are received by the same user. We try to solve the limitations of systems. If most or all users in a multi-user wireless system are experiencing interference from multiple base stations and / or ad hoc transceivers (e.g., in the case of a malfunction of a component of a multi-user wireless system) Can lead to dramatically reduced or even nonfunctional situations.

Prior art multi-user wireless systems add complexity, introduce restrictions on wireless networks, and often require a given user experience (e.g., available bandwidth, latency, predictability, reliability) Which may be influenced by the use of spectrum by users. As the demand for aggregated bandwidth in the wireless spectrum shared by multiple users increases and the growth of applications that can rely on multi-user wireless network reliability, predictability and low latency for a given user increases, Of multi-user wireless technologies suffer from many limitations. In fact, due to the limited availability of spectrum suitable for certain types of wireless communications (e.g., at wavelengths that are efficient at passing through building walls), prior art wireless technologies have shown an increase in reliability, predictability, and low latency bandwidth It may be insufficient to meet the requirements of the present invention.

The prior art relating to the present invention describes beamforming systems and methods for null-steering in multi-user scenarios. Beamforming is initially used to dynamically adjust the phase and / or amplitude (i. E., Beamforming weights) of the signals supplied to the antennas of the array to provide a received signal-to- noise ratio, SNR). In multi-user scenarios, beamforming can be used to suppress interference sources and maximize the signal-to-interference-plus-noise ratio (SINR). For example, when beamforming is used at the receiver of the wireless link, the weights are calculated to produce nulls in the direction of the interfering sources. When beamforming is used at the transmitter in multi-user downlink scenarios, the weights are computed to remove the inter-user interference in advance and maximize the SINR for all users. Alternative techniques for multiuser systems, such as BD pre-coding, calculate precoding weights to maximize throughput on the downlink broadcast channel. All of the pending applications incorporated by reference herein describe the techniques described above (see commonly pending applications for specific citations).

로 가정)의 진폭을 나타내는 도면.
<도 28>
도 28은 클라이언트당 단일 안테나를 갖는 DIDO 2x2에 대한 채널 행렬 주파수 응답의 일 실시예를 나타내는 도면.
<도 29>
도 29는 (예를 들어,

을 갖는) 고주파 선택도에 의해 특성화되는 채널들에 대한 클라이언트당 단일 안테나를 갖는 DIDO 2x2에 대한 채널 행렬 주파수 응답의 일 실시예를 나타내는 도면.
<도 30>
도 30은 상이한 QAM 스킴들(즉, 4-QAM, 16-QAM, 64-QAM)에 대한 예시적인 SER을 나타내는 도면.
<도 31>
도 31은 링크 적응(link adaptation, LA) 기술들을 구현하기 위한 방법의 일 실시예를 나타내는 도면.
<도 32>
도 32는 링크 적응(LA) 기술들의 일 실시예의 SER 성능을 나타내는 도면.
<도 33>
도 33은

및

을 갖는 DIDO 2x2 시스템들에 대한 OFDM 톤 인덱스의 함수로서 수학식 28의 행렬의 엔트리들을 나타내는 도면.
<도 34>
도 34는

, M=N_t=2개의 송신 안테나 및 P의 변수에 대한 SER 대 SNR을 나타내는 도면.
<도 35>
도 35는 상이한 DIDO 차수들 및

에 대한 보간 방법의 일 실시예의 SER 성능을 나타내는 도면.
<도 36>
도 36은 수퍼 클러스터들, DIDO 클러스터들 및 사용자 클러스터들을 이용하는 시스템의 일 실시예를 나타내는 도면.
<도 37>
도 37은 본 발명의 일 실시예에 따른 사용자 클러스터들을 갖는 시스템을 나타내는 도면.
<도 38a 및 도 38b>
도 38a 및 도 38b는 본 발명의 일 실시예에서 이용되는 링크 품질 규준 임계치들을 나타내는 도면.
<도 39 내지 도 41>
도 39 내지 도 41은 사용자 클러스터들을 확립하기 위한 링크 품질 행렬들의 예들을 나타내는 도면.
<도 42>
도 42는 클라이언트가 상이한 DIDO 클러스터들에 걸쳐 이동하는 일 실시예를 나타내는 도면.
<도 43 내지 도 46>
도 43 내지 도 46은 본 발명의 일 실시예에서 구 어레이들의 해상도와 그들의 면적(A) 간의 관계들을 나타내는 도면.
<도 47>
도 47은 실제의 실내 및 실외 전파 시나리오들에서 MIMO 시스템들의 자유도들을 나타내는 도면.
<도 48>
도 48은 어레이 직경의 함수로서 DIDO 시스템들의 자유도들을 나타내는 도면.
<도 49>
도 49는 유선 또는 무선 접속들을 통해 통신하는 다수의 중앙 프로세서(centralized processor, CP) 및 분산 노드(distributed node, DN)를 포함하는 일 실시예를 나타내는 도면.
<도 50>
도 50은 CP들이 허가되지 않은 DN들과 제어 정보를 교환하고, 허가된 사용을 위해 주파수 대역들을 셧다운하도록 그들을 재구성하는 일 실시예를 나타내는 도면.
<도 51>
도 51은 전체 스펙트럼이 새로운 서비스에 할당되고, CP들이 제어 정보를 이용하여 모든 허가되지 않은 DN들을 셧다운하여, 허가된 DN들과의 간섭을 방지하는 일 실시예를 나타내는 도면.
<도 52>
도 52는 다수의 CP, 분산 노드 및 CP들을 DN들에 상호접속하는 네트워크를 포함하는 클라우드 무선 시스템의 일 실시예를 나타내는 도면.
<도 53 내지 도 59>
도 53 내지 도 59는 전파 환경에서 사용자 이동성 또는 변화들로 인한 도플러 효과들을 보상하기 위해 파라미터들을 적응적으로 재구성하는 다중 사용자(MU) 다중 안테나 시스템(multiple antenna system, MAS)의 실시예들을 나타내는 도면.
<도 60>
도 60은 복수의 BTS를 나타내는 도면으로서, 이들 중 일부는 양호한 SNR을 갖고, 이들 중 일부는 UE에 대해 낮은 도플러를 가짐.
<도 61>
도 61은 복수의 BTS-UE 링크에 대해 CP에 의해 기록된 SNR 및 도플러의 값들을 포함하는 행렬의 일 실시예를 나타내는 도면.
<도 62>
도 62는 본 발명의 일 실시예에 따른 상이한 시간들에서의 채널 이득(또는 CSI)을 나타내는 도면.BRIEF DESCRIPTION OF THE DRAWINGS A better understanding of the present invention can be obtained from the following detailed description taken in conjunction with the drawings.
&Lt; 1 >
BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a diagram illustrating a primary DIDO cluster surrounded by neighboring DIDO clusters in one embodiment of the present invention.
2,
Figure 2 illustrates frequency division multiple access (FDMA) techniques used in an embodiment of the present invention.
3,
3 illustrates time division multiple access (TDMA) techniques used in an embodiment of the present invention.
<Fig. 4>
Figure 4 illustrates different types of interference zones that are addressed in an embodiment of the present invention.
5,
Figure 5 illustrates a framework used in an embodiment of the present invention.
6,
6 is a graph showing SER as a function of SNR, assuming SIR = 10 dB for the target client in the interference region;
7,
7 is a graph showing a SER derived from two IDCI pre-coding techniques;
8,
8 illustrates an exemplary scenario in which a target client moves from a primary DIDO cluster to an interfering cluster;
9,
9 shows a signal-to-interference plus noise ratio (SINR) as a function of distance D;
<Fig. 10>
10 is a diagram illustrating symbol error rate (SER) performance of three scenarios for 4-QAM modulation in flat-fading narrowband channels;
11)
11 is a diagram illustrating a method for IDCI pre-coding according to an embodiment of the present invention.
12,
Figure 12 shows SINR variation as a function of distance of a client from the center of the main DIDO clusters in one embodiment;
13,
13 illustrates one embodiment for deriving a SER for 4-QAM modulation;
<Fig. 14>
Figure 14 illustrates an embodiment of the present invention in which a finite state machine implements a handoff algorithm.
<Fig. 15>
Figure 15 illustrates one embodiment of a handoff strategy in the presence of shadowing;
<Fig. 16>
Figure 16 is a diagram illustrating a hysteresis loop mechanism when switching between any two states in Figure 93;
<Fig. 17>
17 illustrates one embodiment of a DIDO system that includes power control;
18,
18 illustrates SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios.
19,
19 illustrates MPE power density as a function of distance from a source of RF radiation to different values of transmit power, in accordance with an embodiment of the present invention;
20A and 20B,
20A and 20B show different distributions of low power and high power DIDO dispersion antennas;
21A and 21B,
Figures 21A and 21B show two power distributions corresponding respectively to the configurations of Figures 20A and 20B.
22A and 22B,
Figures 22A and 22B show rate distributions for the two scenarios shown in Figures 99A and 99B, respectively.
23,
23 illustrates one embodiment of a DIDO system that includes power control;
<Fig. 24>
Figure 24 illustrates one embodiment of a method that is repeated across all antenna groups in accordance with a round-robin scheduling policy to transmit data.
25,
Figure 25 shows a comparison of the uncoded SER performance of power control using antenna grouping and the traditional eigenmode selection in U.S. Patent No. 7,636,381.
26A to 26C,
Figures 26A-26C illustrate three scenarios in which BD pre-coding dynamically adjusts precoding weights to account for different power levels over wireless links between DIDO antennas and clients.
<Fig. 27>
FIG. 27 is a graphical representation of the delayed or instantaneous PDP (top plot) and frequency domain (bottom plot) low frequency selective channels (DIP) for DIDO 2x2 systems

). &Lt; / RTI >
28,
28 illustrates one embodiment of a channel matrix frequency response for DIDO 2x2 with a single antenna per client;
29,
29 is a graph showing the relationship between (for example,

Lt; RTI ID = 0.0 &gt; 2x2 &lt; / RTI &gt; with a single antenna per client for channels characterized by high frequency selectivity
30,
30 shows an exemplary SER for different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM);
31,
31 illustrates one embodiment of a method for implementing link adaptation (LA) techniques.
<Fig. 32>
32 illustrates SER performance of one embodiment of link adaptation (LA) techniques.
33,
Figure 33

And

Lt; RTI ID = 0.0 > 28 < / RTI > as a function of the OFDM tone index for DIDO 2x2 systems having < RTI ID = 0.0 >
34,
Figure 34

, M = N _t = a diagram showing the SN versus SER for two transmit antennas and a variable of P;
<Fig. 35>
Figure 35 illustrates the different DIDO orders and

Lt; RTI ID = 0.0 &gt; SER &lt; / RTI &gt;
<Fig. 36>
36 illustrates one embodiment of a system that utilizes superclusters, DIDO clusters, and user clusters.
37,
37 illustrates a system having user clusters in accordance with one embodiment of the present invention.
38A and 38B,
Figures 38A and 38B show link quality metric thresholds used in an embodiment of the present invention.
&Lt; Figures 39 to 41 &
Figures 39-41 illustrate examples of link quality matrices for establishing user clusters.
42,
42 illustrates an embodiment in which a client moves across different DIDO clusters;
<Figs. 43 to 46>
Figures 43 to 46 illustrate relationships between the resolution of spherical arrays and their area (A) in an embodiment of the present invention.
47,
47 illustrates degrees of freedom of MIMO systems in actual indoor and outdoor propagation scenarios;
<Fig. 48>
Figure 48 shows degrees of freedom of DIDO systems as a function of array diameter.
49,
Figure 49 illustrates an embodiment that includes a centralized processor (CP) and a distributed node (DN) that communicate via wired or wireless connections.
<Fig. 50>
Figure 50 illustrates one embodiment in which CPs exchange control information with unauthorized DNs and reconfigure them to shut down frequency bands for authorized use.
51,
Figure 51 illustrates an embodiment in which the entire spectrum is assigned to a new service and CPs use control information to shut down all unauthorized DNs to prevent interference with authorized DNs.
52,
52 illustrates one embodiment of a cloud radio system that includes multiple CPs, distributed nodes, and networks interconnecting CPs to DNs;
<Figs. 53 to 59>
Figures 53-59 illustrate embodiments of a multiple user (MU) multiple antenna system (MAS) that adaptively reconfigure parameters to compensate for Doppler effects due to user mobility or changes in propagation environments. .
60,
60 shows a plurality of BTSs, some of which have good SNRs, some of which have low Doppler for UEs.
61,
61 illustrates one embodiment of a matrix including SNR and Doppler values recorded by a CP for a plurality of BTS-UE links;
62,
Figure 62 illustrates channel gain (or CSI) at different times according to an embodiment of the invention.

One solution to overcome many of the limitations of the prior art is one embodiment of Distributed-Input Distributed-Output (DIDO) technology. DIDO technology is described in the following patents and patent applications, all of which are assigned to the assignee of the present patent application and incorporated by reference. The present application is a continuation application (CIP) of these patent applications. Such patents and applications are sometimes referred to herein collectively as " Related Patents and Applications. &Quot;

13 / 232,996, filed September 14, 2011, entitled " Systems And Methods To Exploit Areas Of Coherence in Wirless Systems ";

13 / 233,006, filed September 14, 2011, entitled " Systems and Methods for Planned Evolution and Obsolescence of Multiuser Spectrum ";

U.S. Application No. 12 / 917,257, filed November 1, 2010, entitled " Systems And Methods To Coordinate Transmissions In Distributed Wireless Systems Via User Clustering ";

U.S. Serial No. 12 / 802,988, filed June 16, 2010, entitled " Interference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO)

12 / 802,976, filed June 16, 2010, entitled " System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements ";

12 / 802,974, filed June 16, 2010, entitled " System And Method For Managing Inter-Cluster Handoff Of Clients Which Traverse Multiple DIDO Clusters ";

12 / 802,989, filed June 16, 2010, entitled " System Based On Detected Velocity Of The Client ", entitled &quot; System And Method For Managing Handoff Of A Client Between Different Distributed-Input-Distributed- ;

U.S. Serial No. 12 / 802,958, filed June 16, 2010, entitled " System And Method For Power Control And Antenna Grouping In A Distributed-Input-Distributed-Output (DIDO) Network;

12 / 802,975, filed June 16, 2010, entitled " System And Method For Link Adaptation In DIDO Multicarrier Systems ";

12 / 802,938, filed June 16, 2010, entitled " System And Method For DIDO Precoding Interpolation In Multicarrier Systems ";

12 / 630,627, filed December 2, 2009, entitled " System and Method for Distributed Antenna Wireless Communications ";

U.S. Patent No. 7,599,420, filed August 20, 2007, entitled " System and Method for Distributed Input Distributed Output Wireless Communication, " issued October 6, 2009;

U.S. Patent No. 7,633,994, filed on August 20, 2007, entitled " System and Method for Distributed Input Distributed Output Wireless Communication, " issued December 15, 2009;

U.S. Patent No. 7,636,381, filed on August 20, 2007, entitled " System and Method for Distributed Input Distributed Output Wireless Communication, " issued December 22, 2009;

U.S. Serial No. 12 / 143,503, filed June 20, 2008, entitled " System and Method For Distributed Input-Distributed Output Wireless Communications ";

U.S. Serial No. 11 / 256,478, filed October 21, 2005, entitled " System and Method For Spatial-Multiplexed Tropospheric Scatter Communications ";

U.S. Patent No. 7,418,053, filed July 30, 2004, entitled " System and Method for Distributed Input Distributed Output Wireless Communication ", issued Aug. 26, 2008;

U.S. Application Serial No. 10 / 817,731, filed April 2, 2004, entitled "System and Method For Enhancing Near Vertical Incidence Skywave (" NVIS ") Communication Using Space-Time Coding.

To reduce the amount and complexity of the present patent application, the disclosures of some of the related patents and applications are not explicitly described below. For a detailed description of the disclosure, please refer to related patents and applications.

The following Section I (the disclosure of which is incorporated by reference in its entirety) discloses the use of a set of endnotes of its own designating prior art references and conventional applications assigned to the assignee of the present application Please note. End note quotations are listed at the end of Section I (just before the title of Section II). The quotations in Section II uses may have numerical representations of their quotations that overlap with those used in Section I, but such numeric representations identify different references (listed at the end of Section II). Thus, references identified by particular numeric representations may be identified within the section in which the numerical indicia is used.

I. INTRODUCTION FROM RELATED APPLICATIONS 12 / 802,988

1. Methods for eliminating intercluster interference

In the following, wireless RF communication systems and methods for generating positions within a space with a radio frequency (RF) energy of zero using a plurality of distributed transmit antennas are described. When M transmit antennas are used, it is possible to generate a maximum of (M-1) points of RF energy of zero within the predefined positions. In one embodiment of the present invention, the points of zero RF energy are wireless devices, and the transmit antennas know channel state information (CSI) between transmitters and receivers. In one embodiment, the CSI is calculated at the receivers and fed back to the transmitters. In another embodiment, the CSI is calculated at the transmitter through training from receivers, assuming the use of channel reciprocity. Transmitters can use CSI to determine interference signals to be transmitted simultaneously. In one embodiment, block diagonalization (BD) precoding at the transmit antennas is used to generate points of zero RF energy.

The systems and methods described herein differ from the conventional receive / transmit beamforming techniques described above. In fact, receive beamforming computes weights to suppress interference at the receiving end (via null-steering), while some embodiments of the present invention described herein may use one or more Lt; RTI ID = 0.0 &gt; of interference &lt; / RTI &gt; Unlike traditional transmit beamforming or BD pre-coding, which is designed to maximize signal quality (or SINR) or downlink throughput for all users, respectively, the systems and methods described herein may be used under certain conditions and / Minimizes the signal quality from the transmitters to generate points of zero RF energy in the client devices (sometimes referred to herein as " users &quot;). Moreover, with respect to distributed input dispersion output (DIDO) systems (as described in our related patents and applications), transmit antennas dispersed in space can have zero RF energy and / or a large number of maximum SINRs for different users (E. G., Higher channel space diversity) that can be used to generate points of view. For example, when using M transmit antennas, it is possible to generate a maximum of (M-1) points of RF energy. Alternatively, actual beamforming or BD multi-user systems are typically designed to have near-spaced antennas on the transmit side that limit the number of concurrent users that can be serviced over the air link for any number (M) of transmit antennas do.

)를 아는 것으로 가정한다. 간소화를 위해, 모든 사용자는 단일 안테나를 구비하는 것으로 가정되지만, 동일 방법은 사용자당 다수의 수신 안테나로 확장될 수 있다. K명의 사용자의 위치들에서 0 RF 에너지를 생성하는 사전 코딩 가중치들 (

)은 아래의 조건을 충족시키도록 계산된다.Consider a system that includes M transmit antennas and K users, where K < M. If the transmitter has CSI between M transmit antennas and K users (

). For simplicity, all users are assumed to have a single antenna, but the same method can be extended to multiple receive antennas per user. Pre-coding weights that produce zero RF energy at locations of K users (

) Is calculated to satisfy the following conditions.

은 모두 0 엔트리들을 갖는 벡터이고, H는 아래와 같이 M개의 송신 안테나로부터 K명의 사용자로의 채널 벡터들 (

)을 결합하여 얻어진 채널 행렬이다.here,

Is a vector with all 0 entries and H is the channel vectors from M transmit antennas to K users as follows

). &Lt; / RTI &gt;

In one embodiment, a singular value decomposition (SVD) of the channel matrix H is computed, and the precoding weight w is defined as a right singular vector corresponding to a null subspace (identified by a zero singular value) of H do.

The transmit antennas transmit the RF energy using the weight vectors defined above, while generating K points of zero RF energy at the positions of the K users, so that the signal received at the kth user is given by Given.

은 k 번째 사용자에서의 추가 백색 가우스 잡음(additive white Gaussian noise, AWGN)이다.here,

Is the additive white Gaussian noise (AWGN) at the kth user.

In one embodiment, the singular value decomposition (SVD) of the channel matrix H is computed and the precoding weight w is defined as the right singular vector corresponding to a null subspace (identified by a zero singular value) of H. [

In another embodiment, the wireless system is a DIDO system and the points of zero RF energy are generated to pre-remove interference to clients between different DIDO coverage areas. In U.S. Serial No. 12 / 630,627, a DIDO system is described that includes the following elements.

· DIDO clients

· DIDO dispersion antennas

DIDO base transceiver stations (BTS)

DIDO base station network (BSN)

All BTSs are connected via BSN to a number of distributed antennas serving a given coverage area called a DIDO cluster. In this patent application, a system and method for eliminating interference between adjacent DIDO clusters is described. As shown in FIG. 1, it is assumed that a primary DIDO cluster hosts a client (i.e., a user device served by a multi-user DIDO system) (or a target client) that is affected by interference from neighboring clusters.

In one embodiment, neighboring clusters operate at different frequencies in accordance with frequency division multiple access (FDMA) techniques similar to traditional cellular systems. For example, in the case of a frequency reuse factor of 3, the same carrier frequency is reused for every third DIDO cluster, as shown in FIG. In Figure 2, the different carrier frequencies are identified as F ₁ , F ₂ and F ₃ . Although this embodiment may be used in some implementations, such a solution causes loss of spectral efficiency because the available spectrum is divided into multiple subbands, and only a subset of DIDO clusters operate in the same subband . Moreover, this requires a complex cell scheme to associate different DIDO clusters with different frequencies to prevent interference. As with prior art cellular systems, such cellular plans require specific arrangements of antennas and limitations of transmit power to prevent interference between clusters using the same frequency.

In another embodiment, neighboring clusters operate in the same frequency band, but in different time slots according to the time division multiple access (TDMA) technique. For example, as shown in FIG. 3, DIDO transmissions are only allowed in time slots T ₁ , T _2, and T ₃ for certain clusters as shown. The time slots can be equally allocated to different clusters, so different clusters are scheduled according to the round robin policy. When different clusters are characterized by different data rate requirements (i.e. clusters in crowded urban environments, unlike clusters in rural areas with fewer clients per coverage area), different priorities are different clusters , So that more time slots are allocated to clusters with larger data rate demands. Although the TDMA as described above may be used in one embodiment of the present invention, the TDMA approach may require time synchronization across different clusters, and since interference clusters can not simultaneously use the same frequency, have.

In one embodiment, all neighboring clusters transmit simultaneously in the same frequency band and use spatial processing across the clusters to prevent interference. In this embodiment, the multi-cluster DIDO system includes a primary cluster (i) (as described in related patents and applications including applications 7,599,420, 7,633,994, 7,636,381, and 12 / 143,503, Transmitting concurrent non-interfering data streams in the same frequency band to multiple clients using traditional DIDO dictionary coding; (ii) interference elimination in neighboring clusters to prevent interference to clients located in the interference zones 8010 of FIG. 4 by generating points of zero radio frequency (RF) energy at the locations of the target clients Together we use DIDO dictionary coding. When the target client is in the interference zone 410, it will receive the sum of the RF including the data stream from the primary cluster 411 and the 0 RF energy from the interference clusters 412-413, RTI ID = 0.0 &gt; RF &lt; / RTI &gt; Thus, adjacent clusters can use the same frequency at the same time, where target clients in the interference zone do not experience interference.

In practical systems, the performance of DIDO pre-coding may include channel estimation errors or Doppler effects (which generate useless channel state information in DIDO distributed antennas); Intermodulation distortion (IMD) in multi-carrier DIDO systems; Such as time or frequency offsets. &Lt; RTI ID = 0.0 &gt; As a result of these effects, it may be impossible to achieve the points of zero RF energy. However, as long as the RF energy at the target client from the interference clusters can be ignored relative to the RF energy from the main cluster, the link performance at the target client is not affected by interference. For example, to demodulate 4-QAM arrays using forward error correction (FEC) coding to achieve a target bit error rate (BER) of 10 ^-6 , Noise ratio (SNR) is required. If the RF energy at the target client received from the interference cluster is 20 dB lower than the RF energy received from the main cluster, the interference can be ignored and the client can successfully demodulate the data within the predefined BER target. Thus, the term &quot; 0 RF energy &quot; as used herein does not necessarily mean that the RF energy from the interfering RF signals is zero. Rather, this means that the RF energy is sufficiently low compared to the RF energy of the desired RF signal, and therefore the desired RF signal can be received at the receiver. Moreover, although certain desirable thresholds of interference RF energy for a desired RF energy are described, the basic principles of the present invention are not limited to any particular threshold values.

There are different types of interference zones 8010, as shown in FIG. For example, "Type A" zones (as indicated by the letter "A" in FIG. 80) are affected by interference from only one neighbor cluster, while (as indicated by the letter "B" &Quot; Type B " zones describe interference from two or more neighboring clusters.

Figure 5 shows a framework used in an embodiment of the present invention. The dots represent the DIDO dispersion antennas, the crosses represent the DIDO clients, and the arrows represent the propagation directions of the RF energy. The DIDO antennas in the primary cluster transmit the pre-coded data signals to the MCs 501 in the cluster. In addition, the DIDO antennas in the interference cluster serve clients IC 502 in the cluster through traditional DIDO pre-coding. The green cross 503 represents the target client TC 503 in the interference area. The DIDO antennas in the primary cluster 511 transmit the pre-coded data signals via traditional DIDO pre-coding to the target client (black arrows). DIDO antennas in the interference cluster 512 generate zero RF energy (green arrows) towards the direction of the target client 503 using precoding.

The signal received at the target client k in any of the interference regions 410A, B of FIG. 4 is given by the following equation.

는 간섭 클러스터 c 내의 클라이언트들의 수이다. 더욱이,

은 클라이언트 장치들에서 M개의 송신 DIDO 안테나 및 N개의 수신 안테나를 가정할 때 클라이언트 k에서의 수신 데이터 스트림들을 포함하는 벡터이고;

은 주요 DIDO 클러스터 내의 클라이언트 k로의 송신 데이터 스트림들의 벡터이고;

은 주요 DIDO 클러스터 내의 클러스터 u로의 송신 데이터 스트림들의 벡터이고;

은 c 번째 간섭 DIDO 클러스터 내의 클러스터 i로의 송신 데이터 스트림들의 벡터이고;

은 클러스터 k의 N개의 수신 안테나에서의 추가 백색 가우스 잡음(AWGN)의 벡터이고;

은 주요 DIDO 클러스터 내의 클라이언트 k에서의 M개의 송신 DIDO 안테나로부터 N개의 수신 안테나로의 DIDO 채널 행렬이고;

은 c 번째 간섭 DIDO 클러스터 내의 클라이언트 k에서의 M개의 송신 DIDO 안테나로부터 N개의 수신 안테나로의 DIDO 채널 행렬이고;

은 주요 DIDO 클러스터 내의 클라이언트 k에 대한 DIDO 사전 코딩 가중치들의 행렬이고;

은 주요 DIDO 클러스터 내의 클라이언트 u에 대한 DIDO 사전 코딩 가중치들의 행렬이고;

은 c 번째 간섭 DIDO 클러스터 내의 클라이언트 i에 대한 DIDO 사전 코딩 가중치들의 행렬이다.Where K is the number of clients in the interfering zone 8010A, B, U is the number of clients in the primary DIDO cluster, C is the number of clients in the interfering DIDO clusters 412-413, Lt; / RTI &gt;

Is the number of clients in the interference cluster c. Furthermore,

Is a vector containing received data streams at client k, assuming M transmit DIDO antennas and N receive antennas at client devices;

Is the vector of transmitted data streams to client k in the primary DIDO cluster;

Is the vector of transmit data streams to cluster u in the primary DIDO cluster;

Is the vector of transmit data streams to cluster i in the c-th interfering DIDO cluster;

Is the vector of additional white Gaussian noise (AWGN) at the N receive antennas of cluster k;

Is a DIDO channel matrix from M transmit DIDO antennas to N receive antennas at client k in the primary DIDO cluster;

Is a DIDO channel matrix from M transmit DIDO antennas to N receive antennas at client k in the c-th interfering DIDO cluster;

Is the matrix of DIDO pre-coding weights for client k in the primary DIDO cluster;

Is a matrix of DIDO pre-coding weights for a client u in the primary DIDO cluster;

Is a matrix of DIDO precoding weights for client i in the c-th interfering DIDO cluster.

이고,

이다. M이 클러스터 내의 수신 안테나들의 총 수보다 큰 경우, 여분의 송신 안테나들은 간섭 구역 내의 타겟 클라이언트들에 대한 간섭을 사전 제거하거나, 제7,599,420호; 제7,633,994호; 제7,636,381호; 및 출원 제12/143,503호를 포함하는 관련 특허들 및 출원들에서 설명되는 다이버시티 스킴들을 통해 동일 클러스터 내의 클라이언트들에 대한 링크 강건성을 개선하는 데 사용된다.To simplify the notation and without loss of generality, it is assumed that all clients have N receive antennas and that there are M DIDO distributed antennas in every DIDO cluster,

ego,

to be. If M is greater than the total number of receive antennas in the cluster, the extra transmit antennas may be used to pre-remove interference to target clients in the interference region, or in US Pat. Nos. 7,599,420; 7,633,994; 7,636,381; And related patent applications, including application 12 / 143,503, and diversity schemes, as described in applications, to improve link robustness for clients in the same cluster.

The DIDO pre-coding weights are calculated to pre-remove inter-client interference in the same DIDO cluster. See, for example, U.S. Patent No. 7,599,420; 7,633,994; 7,636,381; And BD diagonalization (BD) precoding as described in related patents and applications including applications 12 / 143,503 and [7] can be used to eliminate inter-client interference, Is satisfied.

The precoding weighted matrices in neighboring DIDO clusters are designed to satisfy the following conditions.

을 계산하기 위해, M개의 송신 안테나로부터 간섭 클러스터 내의

개의 클라이언트 및 간섭 구역 내의 클라이언트 k로의 다운링크 채널이 추정되고, 사전 코딩 행렬이 간섭 클러스터 내의 DIDO BTS에 의해 계산된다. BD 방법이 간섭 클러스터들에서의 사전 코딩 행렬들을 계산하는 데 사용되는 경우, 이웃 클러스터들 내의 i 번째 클라이언트에 대한 가중치들을 계산하기 위해 아래의 유효 채널 행렬이 형성된다.Pre-coding matrices

, It is possible to obtain M transmit antennas within the interference clusters

The downlink channel to the client k and the client k in the interference area is estimated, and the precoding matrix is calculated by the DIDO BTS in the interference cluster. When the BD method is used to compute precoding matrices in the interference clusters, the following effective channel matrix is formed to calculate the weights for the i-th client in the neighbor clusters.

는 간섭 클러스터 c에 대한 채널 행렬

로부터 얻어진 행렬이며, 여기서는 i 번째 클라이언트에 대응하는 행들이 제거된다.here,

Lt; RTI ID = 0.0 &gt; c &lt; / RTI &

, Where the rows corresponding to the i-th client are removed.

Substituting Conditions 2 and 3 into 1, the received data streams for the target client k are obtained, where intra- and inter-cluster interference is eliminated.

은 사전 코딩된 데이터 스트림들을 그러한 클러스터들 내의 모든 클라이언트들로 전송하는 한편, 간섭 구역 내의 타겟 클라이언트에 대한 간섭을 사전 제거하도록 설계된다. 타겟 클라이언트는 그의 주요 클러스터로부터만 사전 코딩된 데이터를 수신한다. 다른 실시예에서는, 다이버시티 이득을 얻기 위해 동일한 데이터 스트림이 주요 및 이웃 클러스터들 양자로부터 타겟 클라이언트로 전송된다. 이 경우, 수학식 5의 신호 모델은 아래와 같이 표현된다.The pre-coding weights of Equation (1) calculated in neighboring clusters

Is designed to transmit pre-coded data streams to all clients in such clusters while pre-eliminating interference to target clients in the interference area. The target client receives only the pre-coded data from its primary cluster. In another embodiment, the same data stream is transmitted from both the primary and neighbor clusters to the target client to obtain diversity gain. In this case, the signal model of Equation (5) is expressed as follows.

는 c 번째 클러스터 내의 DIDO 송신기들로부터 간섭 구역 내의 타겟 클라이언트 k로의 DIDO 사전 코딩 행렬이다. 수학식 6의 방법은 이웃 클러스터들 간의 시간 동기화를 필요로 하며, 이는 대형 시스템들에서 달성하기 어려울 수 있지만, 그럼에도 불구하고, 다이버시티 이득 이익이 구현의 비용을 정당화하는 경우에는 사실상 가능하다는 점에 유의한다.here,

Is a DIDO precoding matrix from the DIDO transmitters in the c-th cluster to the target client k in the interference area. Although the method of Equation 6 requires time synchronization between neighboring clusters, which may be difficult to achieve in large systems, it is nevertheless practically possible if the diversity gain benefit justifies the cost of the implementation Please note.

We begin by evaluating the performance of the proposed method in relation to the symbol error rate (SER) as a function of signal to noise ratio (SNR). Assuming a single antenna per client without loss of generality, we define the following signal model and reconstruct equation (1) as follows.

Where INR is the interference-to-noise ratio defined as INR = SNR / SIR, and SIR is the signal-to-interference ratio.

을 계산하는, DIDO 클러스터간 간섭(inter-DIDO-cluster interference, IDCI)을 갖는 하나의 시나리오; 및 (ii) 타겟 클라이언트에 대한 IDCI를 제거하도록 가중치들

을 계산함으로써 IDCI를 제거하는 다른 시나리오를 고려한다. IDCI의 존재 시에 SER이 높고, 사전 정의된 타겟을 초과한다는 점에 주목한다. 이웃 클러스터에서의 IDCI 사전 코딩을 이용하여, 타겟 클라이언트에 대한 간섭이 제거되고, SNR>20 dB에 대해 SER 타겟들이 달성된다.Figure 6 shows SER as a function of SNR, assuming SIR = 10 dB for the target client in the interference area. Without loss of generality, the SER for 4-QAM and 16-QAM was measured without forward error correction (FEC) coding. The target SER is fixed at 1% for uncoded systems. This target corresponds to different values of SNR depending on the modulation order (i.e., SNR = 20 dB for 4-QAM and SNR = 28 dB for 16-QAM). Lower SER targets can be met for the same values of SNR when using FEC coding due to coding gain. Consider a scenario of two clusters (one primary cluster and one interference cluster) with two DIDO antennas per cluster and two clients (each with a single antenna). One of the clients in the primary cluster is located in the interference zone. Although flat-fading narrowband channels are assumed, the following results can be extended to frequency selective multicarrier (OFDM) systems where each subcarrier undergoes flat fading. Two scenarios, i. E., Without explaining the target client in the interference zone,

One scenario with DIDO inter-DIDO-cluster interference (IDCI); And (ii) removing the IDCIs for the target client

To account for other scenarios for removing the IDCI. It should be noted that in the presence of the IDCI the SER is high and exceeds the predefined target. Using IDCI pre-coding in the neighbor cluster, the interference to the target client is eliminated and SER targets are achieved for SNR> 20 dB.

The results of FIG. 6 assume IDCI pre-coding as in Equation 5. &lt; EMI ID = If the IDCI pre-coding in neighboring clusters is also used to pre-code data streams to the target client in the interference area as in Equation 6, an additional diversity gain is obtained. 7 illustrates two techniques, namely (i) "Method 1" using IDCI pre-coding in Equation 5; (ii) neighboring clusters also compare the SER derived from " Method 2 " using IDCI pre-coding in Equation 6 to transmit the pre-coded data stream to the target client. Method 2 generates about 3 dB gain compared to traditional IDCI pre-coding due to the additional array gain provided by the DIDO antennas in the neighboring clusters used to transmit the pre-coded data stream to the target client. More generally, the array gain of method 2 for method 1 is proportional to 10 * log 10 (C + 1), where C is the number of neighboring clusters and the factor "1" represents the dominant cluster.

The performance of the above method is then evaluated as a function of the location of the target client relative to the interference zone. Consider a simple scenario in which the target client 8401 moves from the primary DIDO cluster 802 to the interfering cluster 803, as shown in FIG. It is assumed that all DIDO antennas 812 in the primary cluster 802 meet condition 2 by eliminating intercluster interference using BD precoding. Assume the same path loss (i. E., DIDO antennas are placed in circles around the client) from the single interference DIDO cluster, the single receiver antenna at client device 801 and all DIDO antennas in the primary or interfering cluster. (Such as in typical urban environments) using a simple path loss model with a path loss index of 4 [11].

The following analysis is based on the following simplified signal model extending equation (7) to account for path loss.

Here, the signal-to-interference ratio (SIR) is derived as SIR = ((1-D) / D) ⁴ . In IDCI's modeling, three scenarios, i) an ideal case without an IDCI; (ii) an IDCI was pre-removed through BD pre-coding in an interference cluster to satisfy condition 3; iii) Consider a case with an IDCI that has not been pre-purged by a neighboring cluster.

9 shows the signal to interference plus noise ratio (SINR) as a function of D (i.e., when the target client moves from the primary cluster 802 towards the DIDO antennas 813 in the interference cluster 8403). SINR is derived as a ratio of signal power to interference plus noise power using the signal model of equation (8). For D _o = 0.1 and D = D _o is assumed that the SNR = 50 dB. In the absence of IDCI, the radio link performance is only affected by noise, and the SINR decreases due to path loss. In the presence of the IDCI (i.e., without IDCI pre-coding), the interference from the DIDO antennas in the neighboring cluster contributes to the reduction of the SINR.

Figure 10 shows symbol error rate (SER) performance of the above three scenarios for 4-QAM modulation in flat fading narrowband channels. These SER results correspond to the SINR of FIG. Assume a 1% SER threshold for un-coded (i.e., no FEC) systems corresponding to the SINR threshold SINR _T = 20 dB in FIG. The SINR threshold depends on the modulation order used for data transmission. Higher modulation orders are typically characterized by a higher SINR _T to achieve the same target error rate. With FEC, a lower target SER for the same SINR value can be achieved due to the coding gain. For IDCIs without pre-coding, the target SER is achieved only within the range D <0.25. In the case of IDCI pre-coding in the neighboring cluster, the range satisfying the target SER is extended to D < 0.6. Beyond that range, the SINR increases due to path loss, and the SER target is not satisfied.

One embodiment of a method for IDCI pre-coding is shown in FIG. 11 and consists of the following steps.

SIR estimation 1101: Clients estimate the signal power from the primary DIDO cluster (i.e., based on the received precoded data) and the interference plus noise signal power from neighboring DIDO clusters. In single carrier DIDO systems, the frame structure may be designed to have short periods of silence. For example, silence periods may be defined between training for channel estimation and pre-coded data transmission during channel state information (CSI) feedback. In one embodiment, the interference plus noise signal power from neighboring clusters is measured during silence periods from the DIDO antennas in the primary cluster. In real DIDO multi-carrier (OFDM) systems, null tones are typically used to prevent direct current (DC) offset and attenuation at the edges of the band due to filtering at the transmitting and receiving sides. In another embodiment using multi-carrier systems, the interference plus noise signal power is estimated from the null tones. Correction factors may be used to compensate for transmit / receive filter attenuation at the edge of the band. When the signal plus interference and noise power from the main cluster (P _S) and interference plus noise power (P _IN) from neighboring clusters is estimated, the client calculates the SINR as follows.

Alternatively, the SINR estimate is derived from a received signal strength indication (RSSI) used in conventional wireless communication systems to measure wireless signal power.

Note that the norm of Equation (9) may not be able to distinguish between noise and interference power levels. For example, in environments without interference, clients that are affected by shadowing (i.e., behind obstacles that attenuate signal power from all DIDO distributed antennas in the primary cluster) are not affected by intercluster interference It is possible to estimate a low SINR.

A more reliable criterion for the proposed method is the SIR calculated as follows.

Where P _N is the noise power. In practical multicarrier OFDM systems, the noise power (P _N ) in Equation (10) is estimated from the null tones assuming that all DIDO antennas from the primary and neighbor clusters use the same set of null tones. The interference plus noise power (P _IN ) is estimated from the silence period as described above. Finally, the signal plus interference and noise power (P _S) is derived from the data tones. From these estimates, the client computes the SIR of Equation (10).

Channel estimates 1102-1103 in neighbor clusters: If the estimated SIR of Equation (10) is below a predefined threshold (SIR _T ) determined at 8702 in Figure 11, then the client receives training signals from neighboring clusters Begin to listen. It is noted that SIR _T depends on the modulation and FEC coding scheme (MCS) used for data transmission. Different SIR targets are defined according to the MCS of the client. When DIDO dispersion antennas from different clusters are time synchronized (i.e., when they are locked on the same pulses per second (PPS) time basis), the client uses its training sequence at 8703 to estimate its channel estimates To the DIDO antennas in the clusters. The training sequence for channel estimation in neighbor clusters is designed to be orthogonal to the training from the main cluster. Alternatively, if DIDO antennas in different clusters are not time synchronized, orthogonal sequences (with good cross-correlation properties) are used for time synchronization in different DIDO clusters. When the client is locked to the time / frequency reference of neighboring clusters, channel estimation is performed at 1103.

IDCI Dictionary Coding 1104: If channel estimates are available in the DIDO BTS in neighboring clusters, the IDCI pre-coding is calculated to satisfy the condition of Equation (3). The DIDO antennas in neighboring clusters pre-cull the interference to clients in the interference zone 410 of FIG. 4, while transmitting the precoded data streams only to clients in their clusters. Note that when the client is located in the Type B interference zone 410 of FIG. 4, the interference to the client is generated by multiple clusters, and the IDCI pre-coding is performed simultaneously by all neighbor clusters.

Methods for handoff

Hereinafter, different handoff methods are described for clients traveling across DIDO clusters in which distributed antennas are located that are located in separate zones or that provide different types of services (i.e., low or high mobility services).

a. Handoff between adjacent DIDO clusters

In one embodiment, an IDCI pre-coder for eliminating the above-described inter-cluster interference is used as a baseline for handoff methods in DIDO systems. Traditional handoffs in cellular systems are designed to allow clients to seamlessly switch between cells served by different base stations. In DIDO systems, handoff enables clients to move from one cluster to another without loss of connectivity.

To illustrate one embodiment of a handoff strategy for DIDO systems, consider again the example of FIG. 8 with only two clusters 802 and 803. When a client 801 moves from a primary cluster (C1) 802 to a neighboring cluster (C2) 803, one embodiment of the handoff method dynamically calculates the signal quality in the different clusters, And selects the cluster that produces the lowest error rate performance.

Figure 12 shows the SINR change as a function of the distance of the client from the center of the clusters Cl. For 4-QAM modulation without FEC coding, consider a target SINR = 20 dB. The line identified by circles represents the SINR for the target client being served by the DIDO antennas in C1 when both C1 and C2 use DIDO pre-coding without interference elimination. The SINR decreases as a function of D due to path loss and interference from neighboring clusters. When the IDCI pre-coding is implemented in the neighboring cluster, the SINR loss is due only to the path loss (as shown by the line with triangles), because the interference is completely eliminated. Clients experience symmetric behavior when served from neighboring clusters. One embodiment of the handoff strategy is defined such that the algorithm switches between the different DIDO schemes to maintain the SINR over the predetermined target when the client moves from C1 to C2.

From the plot of FIG. 12, we derive the SER for the 4-QAM modulation of FIG. Note that by switching between the different pre-coding strategies, the SER remains in the predefined target.

One embodiment of a handoff strategy is as follows.

C1-DIDO and C2-DIDO pre-coding: When the client is located in C1 away from the interference area, both clusters (C1, C2) operate independently using traditional DIDO pre-coding.

C1-DIDO and C2-IDCI pre-coding: As the client moves towards the interference area, its SIR or SINR degrades. When the target SINR _T1 is reached, the target client begins to estimate the channel from all DIDO antennas in C2 and provides CSI to the BTS of C2. The BTS of C2 computes the IDCI pre-coding to be transmitted to all clients in C2, while preventing interference to the target client. As long as the target client is in the interference zone, it will continue to provide its CSI to both C1 and C2.

C1-IDCI and C2-DIDO pre-coding: As the client moves toward C2, its SIR or SINR continues to decrease until it reaches the target again. At this point, the client decides to switch to the neighboring cluster. In this case, C1 starts using the CSI from the target client to generate an interference of 0 towards its direction using IDCI pre-coding, while neighboring clusters use CSI for traditional DIDO pre-coding. In one embodiment, as the SIR estimate approaches the target, the clusters C1 and C2 alternate both DIDO and IDCI pre-coding schemes to enable the client to estimate the SIR in both cases. The client then selects the best scheme to maximize a given error rate performance criterion. When this method is applied, the bifurcation point for the handoff strategy occurs at the intersection of the triangular and rhomboid curves of FIG. One embodiment uses the modified IDCI pre-coding method described in Equation 6, in which a neighboring cluster also sends a pre-coded data stream to the target client to provide an array gain. Using this approach, the handoff strategy is simplified because the client does not need to estimate the SINR for both strategies at the fork.

C1-DIDO and C2-DIDO pre-coding: As the client moves from C2 to C2, the primary cluster C1 stops pre-elimination of interference to its target client via IDCI pre-coding, Switch back to traditional DIDO dictionary coding for all clients. This final divergence in our handoff strategy is useful to reduce unnecessary CSI feedback from the target client to C1, thus reducing the overhead for the feedback channel. In one embodiment, a second target SINR _T2 is defined. As the SINR (or SIR) increases over this target, the strategy switches to C1-DIDO and C2-DIDO. In one embodiment, the cluster C1 alternates between DIDO and IDCI pre-coding to enable the client to estimate the SINR. The client then selects a method for C1 that approaches closer to the target SINR _T1 from above.

The method described above calculates SINR or SIR estimates for different schemes in real time and uses them to select the optimal scheme. In one embodiment, the handoff algorithm is designed based on the finite state machine shown in FIG. The client keeps track of its current state and switches to the next state when SINR or SIR falls below or exceeds the predefined thresholds shown in FIG. As described above, in state 1201 both clusters C1 and C2 operate independently using traditional DIDO pre-coding, and the client is served by cluster C1; In state 1202, the client is served by cluster Cl, the BTS in C2 computes IDCI pre-coding, cluster C1 operates using traditional DIDO pre-coding; In state 1203, the client is served by cluster C2, the BTS in C1 computes IDCI pre-coding, cluster C2 operates using traditional DIDO pre-coding; In state 1204, the client is served by cluster C2, and both clusters C1 and C2 operate independently using traditional DIDO pre-coding.

In the presence of the shadowing effect, the signal quality or SIR may fluctuate around the thresholds as shown in FIG. 15 to cause iterative switching between successive states of FIG. Repetitive alteration of states is an undesirable effect because it causes a large overhead for the control channels between clients and BTSs to enable switching between transmission schemes. 15 shows an example of a handoff strategy in the presence of shadowing. In one embodiment, the shadowing coefficients are simulated according to a log-normal distribution with variance 3 [3]. Hereinafter, some methods for preventing the repetitive switching effect during the DIDO handoff are defined.

One embodiment of the present invention utilizes a hysteresis loop to cope with state switching effects. For example, when switching between "C1-DIDO, C2-IDCI" 9302 and "C1-IDCI, C2-DIDO" 9303 states (or vice versa) in FIG. 14, the threshold SINR _T1 is in the range A ₁ can be adjusted within. This method prevents repeated switching between states as signal quality oscillates around SINR _T1 . For example, FIG. 16 shows the hysteresis loop mechanism when switching between any two states of FIG. To switch from state A to B is SIR _{(SIR T1 + A 1/2} ) must be greater than, but in order to again switch from A to B SIR will fall down _{(SIR T1 -A 1/2)} .

In another embodiment, the threshold SINR _T2 is adjusted to prevent iterative switching between the first and second (or third and fourth) states of the finite state machine of FIG. For example, the range of values (A ₂ ) may be defined such that the threshold SINR _T2 is selected within that range depending on the channel condition and the shadowing effect.

In one embodiment, the SINR threshold is dynamically adjusted within the range [SINR _T2 , SINR _T2 + A ₂ ], depending on the variance of the shadowing predicted over the wireless link. The variance of the log-normal distribution can be estimated from the variance of the received signal strength (or RSSI) when the client moves from its current cluster to the neighboring cluster.

The above methods assume that the client triggers a handoff strategy. In one embodiment, the handoff decision is left to DIDO BTSs assuming that communication between multiple BTSs is enabled.

For simplicity, the methods described above are derived assuming that there is no FEC coding and 4-QAM. More generally, SINR or SIR thresholds are derived for different modulation coding schemes (MCSs), and the handoff strategy is associated with link adaptation (see, for example, US Patent No. 7,636,381) RTI ID = 0.0 &gt; downlink &lt; / RTI &gt;

b. Handoff between low and high Doppler DIDO networks

DIDO systems use closed-loop transmission schemes to precode data streams on the downlink channel. The closed loop schemes are uniquely limited by the latency through the feedback channel. In actual DIDO systems, computation time may be reduced by transceivers with high throughput, and most of the latency is introduced by the DIDO BSN when transmitting the CSI and baseband precoding data from the BTS to the distributed antennas . The BSN includes but is not limited to a digital subscriber line (DSL), a cable modem, a fiber optic ring, a T1 line, a hybrid fiber coaxial (HFC) network, and / But may be composed of various network technologies. Dedicated fiber optics typically have a low latency of less than 1 millisecond in very large bandwidth and potentially localized areas, but it is less widely deployed than DSL and cable modems. Currently, DSL and cable modem connections typically have a last-mile latency of 10-25 ms in the United States, but they are very widely deployed.

The maximum latency through the BSN determines the maximum Doppler frequency that can be accepted over the DIDO radio link without degrading the performance of the DIDO pre-coding. For example, in [1], at a carrier frequency of 400 MHz, networks with a latency of about 10 ms (ie, DSL) can tolerate client speeds of up to 8 mph (movement speed) (I. E., Fiber optic ring) can support a rate of up to 70 mph (i.e., freeway traffic).

Two or more DIDO subnetworks are defined according to the maximum Doppler frequency that can be accepted via the BSN. For example, a BSN with high latency DSL connections between the DIDO BTS and the distributed antennas may provide only low mobility or fixed wireless services (i.e., a low Doppler network), while a low latency fiber ring Low latency BSNs can tolerate high mobility (i.e., high Doppler network). It should be noted that most of the broadband users do not move when they use the broadband, and moreover they are not located near areas with many high-speed objects (for example, on the highway), perhaps moving sideways, Because such locations are typically desirable places to live in or operate an office. However, there are broadband users who use broadband at high speeds (e.g., while driving a car on a highway) or are located next to high speed objects (e.g., within a store located near a highway). To address these two different user Doppler scenarios, in one embodiment, a low Doppler DIDO network is typically used with relatively low power (i. E., 1 W to 100 W for indoor or rooftop installation) , Whereas a high Doppler network typically consists of fewer DIDO antennas with high power transmission (i. E., 100 W for rooftop or tower installation). Low Doppler DIDO networks typically serve a larger number of low Doppler users and can typically be served at lower access costs using low-cost, high latency broadband connections such as DSL and cable modems. High Doppler DIDO networks typically serve a smaller number of high Doppler users and can typically serve higher connection costs using more expensive low latency broadband connections such as fiber optics.

Such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA), to prevent interference between different types of DIDO networks (e.g., low Doppler and high Doppler) Multiple access techniques may be used.

Methods for allocating clients to different types of DIDO networks and enabling handoff between them are proposed below. Network selection is based on the type of mobility of each client. The speed (v) of the client is proportional to the maximum Doppler shift according to the following equation [6].

는 캐리어 주파수에 대응하는 파장이고,

는 송신기-클라이언트 방향을 지시하는 벡터와 속도 벡터 사이의 각도이다.Where f _d is the maximum Doppler shift,

Is a wavelength corresponding to the carrier frequency,

Is the angle between the velocity vector and the vector indicating the transmitter-client direction.

In one embodiment, the Doppler shift of all clients is calculated via blind estimation techniques. For example, Doppler shift can be estimated by transmitting RF energy to the client and analyzing the reflected signal, similar to Doppler radar systems.

는 모든 DIDO 안테나로부터의 클라이언트의 상대 거리에 의존할 수 있다는 점에 주목한다. 예를 들어, 이동 클라이언트 근처의 DIDO 안테나들은 멀리 떨어진 안테나들보다 큰 각속도 및 도플러 시프트를 생성한다. 일 실시예에서, 도플러 속도는 클라이언트로부터 상이한 거리들에 있는 다수의 DIDO 안테나로부터 추정되며, 평균, 가중 평균 또는 표준 편차가 클라이언트의 이동성에 대한 지시자로서 사용된다. 추정된 도플러 지시자에 기초하여, DIDO BTS는 클라이언트를 낮은 또는 높은 도플러 네트워크에 할당할지를 결정한다.In another embodiment, one or more DIDO antennas transmit training signals to a client. Based on these training signals, the client estimates the Doppler shift using techniques such as counting the zero-crossing rate of the channel gain or performing spectral analysis. For the fixed velocity v and the client locus, the angular velocity &lt; RTI ID = 0.0 &gt;

May depend on the relative distance of the client from all DIDO antennas. For example, DIDO antennas near a mobile client generate larger angular velocities and Doppler shifts than distant antennas. In one embodiment, the Doppler velocity is estimated from a number of DIDO antennas at different distances from the client, and an average, weighted average, or standard deviation is used as an indicator of the mobility of the client. Based on the estimated Doppler indicator, the DIDO BTS determines whether to assign the client to a low or high Doppler network.

The Doppler indicator is periodically monitored for all clients and sent to the BTS. When one or more clients change their Doppler rate (i. E., The client to the bus versus the client to the bus), such clients are dynamically reassigned to different DIDO networks that may tolerate their level of mobility.

Doppler of slow clients can be affected by being near (e.g., near a highway) near high speed objects, but Doppler is typically much smaller than the Doppler of the clients it is moving on. Thus, in one embodiment, the speed of the client is estimated (e.g., using a means such as monitoring the client location using GPS), the client is assigned to a low Doppler network if the speed is low, The client is assigned to a high Doppler network.

Methods for power control and antenna grouping

A block diagram of DIDO systems including power control is shown in Fig. First, one or more data streams (s _k ) for all clients (1, ..., U) are multiplied by the weights generated by the DIDO precoding unit. The pre-coded data streams are multiplied by a power scaling factor calculated by the power control unit based on input channel quality information (CQI). The CQI is fed back from the clients to the DIDO BTS or derived from the uplink channel assuming uplink downlink channel reciprocity. The U precoded streams for the different clients are then combined and multiplexed into M data streams (t _m ), one for each of the M transmit antennas. Finally, the streams _tm are transmitted to a digital / analog converter (DAC) unit, a radio frequency (RF) unit, a power amplifier (PA) unit and finally to the antennas.

The power control unit measures the CQI for all clients. In one embodiment, the CQI is the average SNR or RSSI. The CQI is different for different clients depending on path loss or shadowing. Our power control method adjusts the transmit power scaling factors (P _k ) for different clients and multiplies them with the pre-coded data streams generated for different clients. Note that one or more data streams may be generated for all clients depending on the number of receive antennas of the clients.

To evaluate the performance of the proposed method, the following signal model is defined, including path loss and power control parameters, based on equation (5).

는 경로 손실/쉐도잉 계수이다. 경로 손실/쉐도잉을 모델링하기 위해, 아래의 간이 모델을 사용한다.Where U is the number of clients and SNR = P _o / N _o, where P _o is the average transmit power, N _o is the noise power,

Is the path loss / shadowing coefficient. To model path loss / shadowing, use the simplified model below.

Where a = 4 is the path loss index, and the path loss is assumed to increase with the index of the clients (i.e., the clients are located at increasing distances from the DIDO antennas).

가 되도록 전력 제어 계수들이 선택되는 (경로 손실을 갖는) 동일 시나리오로부터 도출된다. 전력 제어 방법을 이용하여, 더 많은 전력이 더 높은 경로 손실/쉐도잉을 겪는 클라이언트들을 향하는 데이터 스트림들에 할당되어, 전력 제어를 갖지 않는 사례에 비해 (이 특정 시나리오의 경우) 9 dB SNR 이득이 발생한다.Figure 18 shows the SN versus SER for four DIDO transmit antennas and four clients in different scenarios. The ideal case yields P _k = 1 for all clients, assuming all clients have the same path loss (ie, a = 0). Plots with squares represent an example in which clients have different path loss coefficients and no power control. The curve with the dots

(With path loss) in which the power control coefficients are selected to be &lt; RTI ID = 0.0 &gt; Using the power control method, a 9 dB SNR gain (in this particular scenario) compared to the case where more power is allocated to data streams destined for clients experiencing higher path loss / shadowing, Occurs.

The Federal Communications Commission (FCC) (and other international regulatory agencies) define limits on the maximum power that can be transmitted from wireless devices to limit human exposure to electromagnetic (EM) radiation do. There are two types of restrictions: i) a "vocational / managed" restriction that allows people to fully understand radio frequency (RF) sources via barriers, warnings or labels; ii) There is a "general population / unmanaged" restriction where there is no management of exposure [2].

Different radiation levels are defined for different types of wireless devices. Generally, DIDO dispersion antennas used in indoor / outdoor applications [2]:

"Transmitting devices designed for use in non-stationary locations, typically used with radiation structures that are maintained at a distance of 20 cm or more from the user or the person's body in the vicinity of the person. it is suitable for the FCC category of "mobile" devices defined as "20 cm or more from the body of the user.

The EM emissions of the " moving " devices are measured with respect to the maximum permissible exposure (MPE) expressed in mW / cm &lt; 2 &gt;. Figure 19 shows the MPE power density as a function of distance from the source of RF radiation for different values of transmit power at the 700 MHz carrier frequency. The maximum allowable transmit power to meet FCC &quot; unmanaged &quot; limitations for devices that typically operate more than 20 cm away from the human body is 1 W.

Less restrictive power-spin constraints are defined for transmitters installed on a roof or building away from a "general population". For these rooftop transmitters, the FCC defines a looser radiation limit of 1000 W measured with respect to effective radiated power (ERP).

Based on the above FCC constraints, in one embodiment, two types of DIDO dispersion antennas for real systems are defined.

Low Power (LP) transmitters: have a maximum transmit power of 1 W and consumer grade broadband (e.g., DSL, cable modem, Fibre To The Home) backhaul connections at 5 Mbps, Indoor or outdoor) at any height.

High power (HP) transmitters: Having a transmit power of 100 W and a commercial grade broadband (e.g., fiber optic ring) backhaul (with substantially "unlimited" data rates over available throughput on DIDO wireless links) Antennas installed on a roof or building of 10 meters high.

LP transmitters with DSL or cable modem connections are good candidates for low Doppler DIDO networks (as described in the previous section), because their clients are mostly fixed or have low mobility. HP transmitters with commercial fiber optic connections may allow for higher client mobility and may be used in high Doppler DIDO networks.

To get a practical intuition on the performance of DIDO systems with different types of LP / HP transmitters, consider a practical case of DIDO antenna installation in Palo Alto, California. 20A shows a random distribution of N _LP = 100 low power DIDO dispersion antennas in Palo Alto. 20B, 50 LP antennas are replaced by N _HP = 50 high power transmitters.

Based on the DIDO antenna distributions of FIGS. 20A and 20B, coverage maps within the Palo Alto for systems using DIDO technology are derived. Figs. 21A and 21B show two power distributions respectively corresponding to the configurations of Figs. 20A and 20B. A received power distribution (expressed in dBm) is derived assuming a path loss / shadowing model for the urban environments defined by the 3GPP standard [3] at a carrier frequency of 700 MHz. Note that using 50% of the HP transmitters creates better coverage for the selected area.

Figures 22A and 22B show rate distributions for the above two scenarios. The throughput (expressed in Mbps) is derived based on power thresholds for different modulation coding schemes defined in the 3GPP long-term evolution (LTE) standard in [4,5]. The total available bandwidth is fixed at 10 MHz at the 700 MHz carrier frequency. Two different frequency allocation schemes, i) a 5 ㎒ spectrum allocated only to LP stations; ii) 9 MHz for HP transmitters and 1 MHz for LP transmitters are considered. Note that lower bandwidth is typically allocated for LP stations due to their DSL backhaul connection with limited throughput. Figures 22A and 22B show that it is possible to significantly increase the rate distribution when using 50% of the HP transmitters to increase the average per client data rate from 2.4 Mbps in Figure 22A to 38 Mbps in Figure 22B.

Algorithms are then defined to control the power transmission of LP stations to increase the throughput on the downlink channel of the DIDO systems of Figure 22B by allowing higher power at any given time. Note that the FCC limits for power density are defined based on the average over time as in [2].

는 MPE 평균 시간이고,

은 전력 밀도

을 갖는 복사선에 대한 노출 기간이다. "관리되는" 노출의 경우, 평균 시간은 6분인 반면, "관리되지 않는" 노출의 경우에는 30분까지 증가한다. 이어서, 수학식 14의 평균 전력 밀도가 "관리되지 않는" 노출의 경우의 30분 평균에 대한 FCC 제한을 충족시키는 한, 임의의 전원이 MPE 제한들보다 높은 전력 레벨들로 송신하는 것이 허용된다.here,

Is the MPE mean time,

Power density

Lt; RTI ID = 0.0 &gt; radiation. &Lt; / RTI &gt; For "managed" exposures, the average time is 6 minutes, whereas for "unmanaged" exposures it increases by 30 minutes. Then, any power source is allowed to transmit at power levels higher than the MPE limits, as long as the average power density in equation (14) meets the FCC limit for a 30 minute average in case of &quot; unmanaged &quot; exposure.

Based on this analysis, adaptive power control methods are defined to increase the instantaneous transmit power per antenna while keeping the average power per DIDO antenna below the MPE limits. DIDO systems with more transmit antennas than active clients are contemplated. This is a reasonable assumption where DIDO antennas can be designed as inexpensive wireless devices (similar to Wi-Fi access points) and can be deployed anywhere that a DSL, cable modem, fiber optic or other Internet connection exists.

A framework of DIDO systems with adaptive per-antenna power control is shown in FIG. The amplitude of the digital signal from the multiplexer 234 is dynamically adjusted using the power scaling factors S ₁ , ..., S _M before being transmitted to the DAC units 235. The power scaling factors are calculated by the power control unit 232 based on the CQI 233.

)보다 큰 전력 레벨(S_o)로 클라이언트들에 송신하는 N_a>K개의 활성 DIDO 안테나를 갖는다. 하나의 방법이 도 24에 도시된 라운드 로빈 스케줄링 정책에 따라 모든 안테나 그룹들에 걸쳐 반복된다. 다른 실시예에서, 상이한 스케줄링 기술들(즉, 비례-공정 스케줄링 [8])을 클러스터 선택을 위해 이용하여 에러 레이트 또는 처리량 성능을 최적화한다.In one embodiment, N _g DIDO antenna groups are defined. All groups contain at least as many DIDO antennas as the number of active clients (K). At any given time, only one group is MPE-restricted (

) With greater power level (S _o) has _a N> K active DIDO antenna to be transmitted to the client. One method is repeated across all antenna groups according to the round robin scheduling policy shown in FIG. In another embodiment, different scheduling techniques (i.e., proportional-to-process scheduling [8]) are used for cluster selection to optimize error rate or throughput performance.

Assuming round-robin power allocation, the average transmit power for all DIDO antennas is derived from: &lt; EMI ID = 14.0 &gt;

)을 충족시키도록 정의되는, 그룹들의 듀티 팩터(duty factor, DF)이다. 듀티 팩터는 아래의 정의에 따라 활성 클라이언트들의 수, 그룹들 및 그룹당 활성 안테나들의 수에 의존한다.Where t _o is the period during which the antenna group is active and T _MPE = 30 min is the average time defined by the FCC Guidelines [2]. The ratio in equation (15) indicates that the average transmit power from all DIDO antennas is less than the MPE limit

(Duty factor, DF) of the groups, which are defined to satisfy the duty factor (DF). The duty factor depends on the number of active clients, the number of active antennas per groups and the group according to the definition below.

The SNR gain (in dB) obtained in the DIDO systems, including power control and antenna grouping, is expressed as a function of the duty factor as follows.

Note that the gain of Equation 17 is achieved in exchange for the additional transmit power of G _dB over all DIDO antennas.

In general, the total transmit power from all N _a of all N _g groups is defined as:

Here, P _ij is the average transmission power per antenna given by the following equation,

S _ij (t) is the power spectral density for the ith transmit antenna in the jth group. In one embodiment, the power spectral density of (19) is designed to optimize the error rate or throughput performance for all antennas.

To obtain some intuition about the performance of the proposed method, consider 400 clients subscribing to the wireless Internet service provided through 400 DIDO distributed antennas and DIDO systems in a given coverage area. All Internet access will not always be fully utilized. It is assumed that 10% of the clients actively use the wireless Internet access at any given time. Then, 400 DIDO antennas can be divided respectively into N _g = 10 groups of N _a = 40 antennas, and all groups serve K = 40 active clients with a duty factor DF = 0.1 at any given time . The SNR gain resulting from this transmission scheme is G _dB = ₁₀ log ₁₀ (1 / DF) = 10 dB provided by the 10 dB additional transmit power from all DIDO antennas. It is noted, however, that the average transmit power per antenna is constant and within the MPE limit.

Figure 25 shows a comparison of the (uncoded) SER performance of the power control using antenna grouping and the traditional eigenmode selection in U.S. Patent No. 7,636,381. All schemes use BD pre-coding with four clients, and each client has a single antenna. SNR represents the ratio of the power per transmit antenna to the noise power (i.e., the transmit SNR per antenna). The curves displayed using DIDO 4x4 assume four transmit antennas and BD pre-coding. Curves with squares represent SER performance for BDs with two additional transmit antennas and eigenmode selection and produce a 10 dB SNR gain (at 1% SER target) compared to traditional BD precoding. Antenna grouping and power control using DF = 1/10 also generate 10 dB gain on the same SER target. Note that the eigenmode selection changes the slope of the SER curve due to the diversity gain, while our power control method shifts the SER curve to the left (with the same slope) due to the increase in the average transmit power. For comparison, a SER with a larger duty factor DF = 1/50 is shown to provide an additional 7 dB gain over DF = l / 10.

Note that our power control may have lower complexity than traditional eigenmode selection methods. In fact, the antenna IDs of all groups can be precomputed and shared among DIDO antennas and clients through search tables, so only K channel estimates at any given time are needed. For eigenmode selection, (K + 2) channel estimates are computed and additional computational processing is required to select the eigenmode that minimizes the SER for all clients at any given time.

Then, in some special scenarios, another method is described that includes DIDO antenna grouping to reduce CSI feedback overhead. Figure 26A illustrates one scenario in which clients (dots) are randomly distributed in one area covered by multiple DIDO distributed antennas (crosses). The average power for all transmit-receive wireless links can be calculated as:

Where H is the channel estimate matrix available in the DIDO BTS.

The matrices A in Figures 26A-26C are obtained numerically by averaging the channel matrices for 1000 cases. Two alternative scenarios are shown in Figures 26b and 26c, respectively, where clients are grouped together around a subset of DIDO antennas and receive power that can be ignored from distant DIDO antennas. For example, FIG. 26B shows two groups of antennas that produce a block diagonal matrix A. One extreme scenario is when all clients are very close to a single transmitter and the transmitters are far apart and the power from all other DIDO antennas can be ignored. In this case, the DIDO link is degenerated into a plurality of SISO links, and A is a diagonal matrix as shown in Fig. 26C.

In all three scenarios described above, the BD pre-coding dynamically adjusts the precoding weights to account for the different power levels across the wireless links between the DIDO antennas and clients. However, it is convenient to identify a plurality of groups in the DIDO cluster and perform DIDO pre-coding only in each group. Our proposed grouping method generates the following advantages.

Computational gain: DIDO precoding is computed only in all groups in the cluster. For example, when BD pre-coding is used, singular value decomposition (SVD) has complexity O (n ³ ), where n is the smallest dimension of the channel matrix H. If H can be reduced to a block diagonal matrix, SVD is computed for all blocks with reduced complexity. In fact, if the channel matrix is divided into two block matrices with dimensions n ₁ and n ₂ such that n = n ₁ + n ₂ , the complexity of the SVD is only O (n ₁ ³ ) + O (n ₂ ³ ) O (n ³ ). In the extreme case, if H is a diagonal matrix, the DIDO link is reduced to multiple SISO links, and no SVD calculation is needed.

Reduction of CSI feedback overhead: In one embodiment, when DIDO antennas and clients are divided into groups, CSI is calculated from clients to antennas only within the same group. In TDD systems, assuming channel reciprocity, antenna grouping reduces the number of channel estimates for calculating the channel matrix H. In FDD systems where CSI is fed back over a wireless link, antenna grouping also creates a reduction in CSI feedback overhead over wireless links between DIDO antennas and clients.

Multiple access techniques for DIDO uplink channels

In an embodiment of the invention, different multiple access techniques are defined for the DIDO uplink channel. These techniques may be used to feed CSI or transmit data streams from the clients to the DIDO antennas over the uplink. Hereinafter, the feedback of CSI and data streams as uplink streams is described.

Multiple-in-multiple-out (MIMO): The uplink streams are transmitted from the client to DIDO antennas over open-loop MIMO multiplexing schemes. This method assumes that all clients are time / frequency synchronized. In one embodiment, synchronization between clients is achieved through training from the downlink, and all DIDO antennas are assumed to be locked to the same time / frequency reference clock. Note that changes in delay spreading in different clients may create jitter between different client &apos; s clocks, which may affect the performance of the MIMO uplink scheme. After the clients have transmitted the uplink streams via the MIMO multiplexing schemes, the receiving DIDO antennas may use nonlinear (i.e., maximum likelihood, ML) or linear (i.e., zeros-forcing, least mean- To remove co-channel interference, and demodulate the uplink streams separately.

Time Division Multiple Access (TDMA): Different clients are assigned to different time slots. Every client sends its uplink stream when its time slot is available.

Frequency Division Multiple Access (FDMA): Different clients are assigned to different carrier frequencies. In multi-carrier (OFDM) systems, subsets of tones are assigned to different clients that simultaneously transmit uplink streams, thus reducing latency.

Code Division Multiple Access (CDMA): All clients are assigned to different pseudo-random sequences, and orthogonality between clients is achieved in the code domain.

In one embodiment of the invention, clients are wireless devices transmitting at much lower power than DIDO antennas. In this case, the DIDO BTS defines client subgroups based on uplink SNR information, thus minimizing interference between subgroups. Within all subgroups, using the multiple access techniques described above, orthogonal channels are created in time, frequency, space, or code domains, thereby preventing uplink interference between different clients.

In another embodiment, the above-described uplink multiple access techniques are used in conjunction with the antenna grouping methods presented in the previous section to define different client groups in a DIDO cluster.

System and method for link adaptation in DIDO multi-carrier systems

Link adaptation methods for DIDO systems that utilize time, frequency, and spatial selectivity of wireless channels are defined in U.S. Patent No. 7,636,381. Below, embodiments of the present invention for link adaptation in multi-carrier (OFDM) DIDO systems using time / frequency selectivity of wireless channels are described.

Simulates Rayleigh fading channels according to the exponentially decaying power delay profile (PDP) or Saleh-Valenzuela model in [9]. For the sake of simplicity, we assume a single cluster channel with a multipath PDP defined as follows.

는 채널 지연 확산(

)에 역비례하는 채널 응집 대역폭의 지시자인 PDP 지수이다.

의 낮은 값들은 주파수 플랫 채널들을 생성하는 반면,

의 높은 값들은 주파수 선택적 채널들을 생성한다. 수학식 21의 PDP는 정규화되며, 따라서 L개의 채널 탭 모두에 대한 전체 평균 전력은 아래와 같이 단일화된다.Where n = 0, ..., L-1 is the index of the channel tap, L is the number of channel taps,

Channel delay spread (

) &Lt; / RTI &gt; of the channel coherence bandwidth.

While the low values of &lt; RTI ID = 0.0 &gt;

&Lt; / RTI &gt; produce frequency selective channels. The PDP of Equation (21) is normalized, and thus the total average power for all of the L channel taps is unified as follows.

로 가정)의 진폭을 나타낸다. 첫 번째 첨자는 클라이언트를 지시하고, 두 번째 첨자는 송신 안테나를 지시한다. (

을 갖는) 고주파 선택적 채널들이 도 28에 도시된다.FIG. 27 is a graphical representation of the delayed or instantaneous PDP (top plot) and frequency domain (bottom plot) low frequency selective channels (DIP) for DIDO 2x2 systems

). &Lt; / RTI &gt; The first subscript indicates the client, and the second subscript indicates the transmit antenna. (

Lt; / RTI &gt; are shown in FIG.

The performance of DIDO pre-coding on frequency-selective channels is then described. Assuming the signal model of Equation (1) satisfying the condition of Equation (2), DIDO pre-coding weights are calculated via BD. The DIDO received signal model of Equation (5) is reconstructed using the condition of Equation (2) as follows.

는 사용자 k에 대한 유효 채널 행렬이다. 클라이언트당 단일 안테나를 갖는 DIDO 2x2의 경우, 유효 채널 행렬은 도 29에 도시된 주파수 응답과 더불어 그리고 도 28의 (예로서,

을 갖는) 고주파 선택성에 의해 특성화되는 채널들에 대해 하나의 값으로 축소된다. 도 29의 연속 라인은 클라이언트 1을 지시하는 반면, 점들을 가진 라인은 클라이언트 2를 지시한다. 도 29의 채널 품질 규준에 기초하여, 채널 조건들의 변화에 따라 MCS들을 동적으로 조정하는 시간/주파수 도메인 링크 적응(LA) 방법들이 정의된다.here,

Is the effective channel matrix for user k. In the case of DIDO 2x2 with a single antenna per client, the effective channel matrix, along with the frequency response shown in Figure 29,

Lt; RTI ID = 0.0 &gt; high-frequency &lt; / RTI &gt; selectivity). The contiguous line in FIG. 29 indicates the client 1, while the line with the dots indicates the client 2. Based on the channel quality criterion of FIG. 29, time / frequency domain link adaptation (LA) methods are dynamically defined that dynamically adjust MCSs according to changes in channel conditions.

AWGN and Rayleigh Fading We start by evaluating the performance of different MCSs on SISO channels. For simplicity, it is assumed that there is no FEC coding, but the following LA methods can be extended to systems that include FEC.

Figure 30 shows the SER for different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM). Without loss of generality, assume a target SER of 1% for uncoded systems. The SNR thresholds for satisfying such a target SER in AWGN channels are 8 dB, 15.5 dB and 22 dB, respectively, for the three modulation schemes. In Rayleigh fading channels, the SER performance of the modulation schemes is well known to be worse than AWGN [13] and the SNR thresholds are 18.6 dB, 27.3 dB, and 34.1 dB, respectively. Note that DIDO pre-coding converts a multi-user downlink channel into a set of parallel SISO links. Thus, the same SNR thresholds for SISO systems as in FIG. 30 are maintained per client for DIDO systems. Moreover, when instantaneous LA is performed, thresholds in AWGN channels are used.

The main idea of the proposed LA method for DIDO systems is to use low MCS orders to provide link robustness when the channel undergoes severe fades in the time domain or frequency domain (shown in FIG. 28). Alternatively, when the channel is characterized by a large gain, the LA method switches to higher MCS orders to increase spectral efficiency. One contribution of the present application as compared to U.S. Patent No. 7,636,381 is that adaptation is enabled using the effective channel matrix as a norm in equations (23) and (29).

A general framework of LA methods is shown in Figure 31 and is defined as follows.

CSI estimation: In 3171, the DIDO BTS calculates CSI from all users. Users may have single or multiple receive antennas.

DIDO pre-coding: At 3172, the BTS calculates DIDO precoding weights for all users. In one embodiment, the BD is used to calculate these weights. The pre-coding weights are calculated on a tone-by-tone basis.

Link quality criteria calculation: At 3173, the BTS calculates the frequency-domain link quality criteria. In OFDM systems, norms are computed from the DIDO precoding weights for CSI and all tones. In one embodiment of the invention, the link quality criterion is the average SNR for all OFDM tones. This method is defined as LA1 (based on average SNR performance). In another embodiment, the link quality criterion is the frequency response of the effective channel in equation (23). This method is defined as LA2 (based on tone-specific performance to utilize frequency diversity). When all clients have a single antenna, the frequency domain effective channel is shown in FIG. When clients have multiple receive antennas, the link quality criterion is defined as the Frobenius norm of the effective channel matrix for all tones. Alternatively, multiple link quality criteria are defined as singular values of the effective channel matrix of Equation (23) for all clients.

Bit-loading algorithm: At 3174, based on link quality standards, the BTS determines MCSs for different clients and different OFDM tones. For the LA1 method, the same MCS is used for all clients and all OFDM tones based on the SNR thresholds for the Rayleigh fading channels of FIG. In the case of LA2, different MCSs are assigned to different OFDM tones to utilize channel frequency diversity.

Pre-coded data transmission: At 3175, the BTS transmits pre-coded data streams from DIDO distributed antennas to clients using MCSs derived from a bit loading algorithm. Attaches one header to the pre-coded data and sends MCSs for the different tones to the clients. For example, if eight MCSs are available and OFDM symbols are defined using N = 64 tones, then the log ₂ (8) * N = 192 bits are needed to transmit the current MCS to all clients . Assuming that 4-QAM (2 bits / symbol spectral efficiency) is used to map such bits to symbols, only 192/2 / N = 1.5 OFDM symbols are required to map the MCS information. In another embodiment, multiple subcarriers (or OFDM tones) are grouped into subbands, and the same MCS is assigned to all tones in the same subband to reduce overhead due to control information. Furthermore, the MCS is adjusted based on the time variation of the channel gain (proportional to the cohesion time). In a fixed-wireless channel (characterized by a low Doppler effect), the MCS is recalculated for each fraction of the channel cohesion time, thus reducing the overhead required for control information.

32 shows the SER performance of the LA methods described above. For comparison, the SER performance in Rayleigh fading channels is plotted for each of the three QAM schemes used. The LA2 method adapts the MCSs to the variation of the effective channel in the frequency domain so that a 1.8 bps / ㎐ gain in spectral efficiency and a SNR (for SNR> 35 dB) for a low SNR (i.e., SNR = 20 dB) Lt; RTI ID = 0.0 &gt; dB &lt; / RTI &gt;

Multi carrier  Systems DIDO  System and method for pre-coding interpolation

The computational complexity of DIDO systems is limited primarily to central processors or BTSs. The computationally expensive task is the calculation of the pre-coding weights from their CSI for all clients. When BD pre-coding is used, the BTS must perform as many singular value decomposition (SVD) operations as there are clients in the system. One way to reduce complexity is through parallelism, in which case SVD is computed on a separate processor for each client.

In multi-carrier DIDO systems, each subcarrier experiences a flat fading channel, and the SVD is performed on every client over every subcarrier. Clearly, the complexity of the system increases linearly with the number of subcarriers. For example, in OFDM systems with a 1 MHz signal bandwidth, the cyclic prefix (L ₀ ) has at least eight channel taps (i.e., 8) to prevent intersymbol interference in outdoor urban macrocell environments with large delay spread Microsecond duration) [3]. The size of a fast Fourier transform ( _FFT ) (N _FFT ) used to generate OFDM symbols is typically set to a multiple of L ₀ to reduce the loss of data rate. For N _FFT = 64, the effective spectrum efficiency of the system is limited by the factor N _FFT / (N _FFT + L ₀ ) = 89%. Larger values of the N _FFT produce higher spectral efficiency at the expense of higher computational complexity in the DIDO pre-coder.

)의 사전 코딩 가중치들에 대한 조건은 수학식 2로부터 아래와 같이 도출된다.One way to reduce computational complexity in a DIDO precoder is to perform an SVD operation on a subset of tones (where pilot tones are called) and derive precoding weights for the remaining tones through interpolation. Weighted interpolation is an error source that causes inter-client interference. In one embodiment, by using optimal weighted interpolation techniques to reduce inter-client interference, it produces improved error rate performance and lower computational complexity in multi-carrier systems. In DIDO systems with M transmit antennas, U clients, and N receive antennas per client, a kth client (&lt; RTI ID = 0.0 &gt;

) &Lt; / RTI &gt; is derived from Equation (2) as follows.

는 시스템 내의 다른 DIDO 클라이언트들에 대응하는 채널 행렬들이다.here,

Are channel matrices corresponding to other DIDO clients in the system.

In one embodiment of the present invention, the objective function of the weighted interpolation method is defined as follows.

는 사용자 k에 대해 최적화될 파라미터들의 세트이고,

는 가중치 보간 행렬이고,

은 행렬의 프로베니우스 놈을 나타낸다. 최적화 문제는 아래와 같이 공식화된다.here,

Is a set of parameters to be optimized for user k,

Is a weighted interpolation matrix,

Represents the Provenius nucleus of the matrix. The optimization problem is formulated as follows.

은 최적화 문제의 가능한 세트이고,

는 최적 솔루션이다.here,

Is a possible set of optimization problems,

Is the optimal solution.

The objective function of Equation (25) is defined for one OFDM tone. In another embodiment of the present invention, the objective function is defined as a linear combination of Probeneus norms in equation (25) of matrices for all OFDM tones to be interpolated. In another embodiment, the OFDM spectrum is divided into subsets of tones, and the optimal solution is given by the following equation.

Where n is an OFDM tone index and A is a subset of tones.

는 파라미터들의 세트

의 함수로서 표현된다. 수학식 26 또는 27에 따라 최적 세트가 결정되면, 최적 가중치 행렬이 계산된다. 본 발명의 일 실시예에서, 주어진 OFDM 톤(n)에 대한 가중치 보간 행렬은 파일럿 톤들에 대한 가중치 행렬들의 선형 결합으로서 정의된다. 단일 클라이언트를 갖는 빔 형성 시스템들에 대한 가중치 보간 함수의 일례가 [11]에서 정의되었다. DIDO 다중 클라이언트 시스템들에서, 가중치 보간 행렬은 다음과 같이 표현된다.The weighted interpolation matrix &lt; RTI ID = 0.0 &gt;

Lt; / RTI &gt;

Lt; / RTI &gt; Once the optimal set is determined according to Equation 26 or 27, the optimal weighting matrix is calculated. In one embodiment of the invention, the weighted interpolation matrix for a given OFDM tone n is defined as a linear combination of weighting matrices for pilot tones. An example of a weighted interpolation function for beamforming systems with a single client is defined in [11]. In DIDO multi-client systems, the weighted interpolation matrix is expressed as:

, L₀는 파일럿 톤들의 수이고,

이며, 이때

이다. 이어서, 수학식 28의 가중치 행렬이 정규화되며, 따라서 모든 안테나로부터의 단일 전력 송신을 보증하기 위해

이다. N=1(클라이언트당 단일 수신 안테나)인 경우, 수학식 28의 행렬은 그의 놈에 대해 정규화되는 벡터가 된다. 본 발명의 일 실시예에서, 파일럿 톤들은 OFDM 톤들의 범위 내에서 균일하게 선택된다. 다른 실시예에서, 파일럿 톤들은 보간 에러를 최소화하기 위해 CSI에 기초하여 적응적으로 선택된다.here,

, L ₀ is the number of pilot tones,

Lt; / RTI &gt;

to be. Then, the weighting matrix of Equation (28) is normalized, and thus, to guarantee a single power transmission from all antennas

to be. For N = 1 (a single receive antenna per client), the matrix of equation (28) becomes a vector normalized to its norm. In one embodiment of the invention, the pilot tones are uniformly selected within the range of OFDM tones. In another embodiment, pilot tones are adaptively selected based on CSI to minimize interpolation errors.

It is noted that one important difference between the system and method of [11] and that proposed in this patent application is the objective function. Specifically, the systems of [11] assume multiple transmit antennas and a single client, and the related method is therefore designed to maximize the received SNR for the client by maximizing the product of the pre-coding weight and the channel. However, this method is not valid in multiple client scenarios because it causes inter-client interference due to interpolation errors. In contrast, our approach is designed to improve error rate performance for all clients by minimizing inter-client interference.

및

을 갖는 DIDO 2x2 시스템들에 대한 OFDM 톤 인덱스의 함수로서의 수학식 28의 행렬의 엔트리들을 나타낸다. 채널 PDP가

을 갖는 수학식 21의 모델에 따라 생성되며, 채널은 8개의 채널 탭만으로 구성된다. L₀는 채널 탭들의 수보다 크도록 선택되어야 한다는 점에 주목한다. 도 33의 실선들은 이상적인 함수들을 나타내는 반면, 점선들은 보간된 함수들이다. 보간된 가중치들은 수학식 28의 정의에 따라 파일럿 톤들에 대한 이상적인 가중치들과 매칭된다. 나머지 톤들에 대해 계산된 가중치들은 추정 에러로 인한 이상적인 사례의 근사치일 뿐이다.Figure 33

And

&Lt; / RTI &gt; as a function of the OFDM tone index for DIDO 2x2 systems having &lt; RTI ID = 0.0 &gt; Channel PDP

, And the channel is composed of only eight channel taps. Note that L ₀ must be selected to be greater than the number of channel taps. The solid lines in Figure 33 represent ideal functions, while the dotted lines are interpolated functions. The interpolated weights are matched with ideal weights for the pilot tones according to the definition of equation (28). The weights calculated for the remaining tones are only approximations of the ideal case due to the estimation error.

에 대한 포괄적인 검색을 통한 것이다. 검색의 복잡성을 줄이기 위해, 가능한 세트를 범위 [0,

] 내에 균일하게 P개의 값으로 양자화한다. 도 34는

, M=N_t=2개의 송신 안테나 및 가변 수의 P에 대한 SER 대 SNR을 나타낸다. 양자화 레벨들의 수가 증가함에 따라, SER 성능이 개선된다. P=10의 사례는 감소된 검색 수로 인한 훨씬 더 낮은 계산 복잡성 때문에 P=100의 성능에 접근한다는 점에 주목한다.One way to implement the weighted interpolation method is to use a possible set of &lt; RTI ID = 0.0 &gt;

Through a comprehensive search for. To reduce the complexity of the search, it is possible to set the range [0,

Into P values uniformly. Figure 34

, M = N _t = SNR for two transmit antennas and a variable number of Ps. As the number of quantization levels increases, SER performance is improved. It is noted that the case of P = 10 approaches performance of P = 100 due to the much lower computational complexity due to the reduced number of searches.

에 대한 보간 방법의 SER 성능을 나타낸다. 클라이언트들의 수가 송신 안테나들의 수와 동일하고, 모든 클라이언트가 단일 안테나를 구비하는 것으로 가정한다. 클라이언트들의 수가 증가함에 따라, 가중치 보간 에러들에 의해 생성되는 클라이언트간 간섭의 증가로 인해 SER 성능이 저하된다.Figure 35 illustrates the different DIDO orders and

The SER performance of the interpolation method is shown. It is assumed that the number of clients equals the number of transmit antennas, and that all clients have a single antenna. As the number of clients increases, SER performance degrades due to an increase in inter-client interference caused by weighted interpolation errors.

In another embodiment of the invention, the weighted interpolation functions of equation (28) and other weighted interpolation functions are used. For example, linear prediction autoregressive models [12] can be used to interpolate weights across different OFDM tones based on estimates of channel frequency correlation.

References

[1] A. Forenza and S. G. Perlman, "System and method for distributed antenna wireless communications", U.S. Pat. Application Serial No. 12 / 630,627, filed December 2, 2009, entitled "System and Method for Distributed Antenna Wireless Communications"

[2] FCC, "Evaluating compliance with FCC guidelines for human exposure to radiofrequency electromagnetic fields," OET Bulletin 65, Ed. 97-01, Aug. 1997

[3] 3GPP, "Spatial Channel Model AHG (Combined ad-hoc from 3GPP & 3GPP2)", SCM Text V6.0, April 22, 2003

[4] 3GPP TR 25.912, " Feasibility Study for Evolved UTRA and UTRAN ", V9.0.0 (2009-10)

[5] 3GPP TR 25.913, "Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN)", V8.0.0 (2009-01)

[6] W. C. Jakes, Microwave Mobile Communications, IEEE Press, 1974

[7] K. K. Wong, et al., "A joint channel diagonalization for multiuser MIMO antenna systems," IEEE Trans. Wireless Comm., Vol. 2, pp. 773-786, July 2003;

[8] P. Viswanath, et al., "Opportunistic beamforming using dump antennas," IEEE Trans. On Inform. Theory, vol. 48, pp. 1277-1294, June 2002.

[9] A. A. M. Saleh, et al., "A statistical model for indoor multipath propagation," IEEE Jour. Select. Areas in Comm., Vol. 195 SAC-5, no. 2, pp. 128-137, Feb. 1987.

[10] A. Paulraj, et al., Introduction to Space-Time Wireless Communications, Cambridge University Press, 40 West 20th Street, New York, NY, USA, 2003.

[11] J. Choi, et al., "Interpolation Based Transmit Beamforming for MIMO-OFDM with Limited Feedback," IEEE Trans. on Signal Processing, vol. 53, no. 11, pp. 4125-4135, Nov. 2005.

[12] I. Wong, et al., `` Long Range Channel Prediction for Adaptive OFDM Systems, '' Proc. of the IEEE Asilomar Conf. on Signals, Systems, and Computers, vol. 1, pp. 723-736, Pacific Grove, CA, USA, Nov. 7-10, 2004.

[13] J. G. Proakis, Communication System Engineering, Prentice Hall, 1994

[14] B.D. Van Veen, et al., `` Beamforming: a versatile approach to spatial filtering, '' IEEE ASSP Magazine, Apr. 1988.

[15] R.G. Vaughan, " On optimal combining at the mobile, " IEEE Trans. On Vehic. Tech., Vol. 37, n. 4, pp. 181-188, Nov. 1988

[16] F. Qian, "Partially adaptive beamforming for correlated interference rejection," IEEE Trans. On Sign. Proc., Vol. 43, n. 2, pp. 506-515, Feb. 1995

[17] H. Krim, et. " Two decades of array signal processing research, " IEEE Signal Proc. Magazine, pp. 67-94, July 1996

[19] W.R. Remley, " Digital beamforming system ", US Patent N. 4,003,016, Jan. 1977

[18] R.J. Masak, " Beamforming / null-steering adaptive array ", US Patent N. 4,771,289, Sep.1988

[20] K.-B.Yu, et. al., " Adaptive digital beamforming architecture and algorithm for nulling mainlobe and multiple sidelobe radar jammers while preserving monopulse ratio angle estimation accuracy ", US Patent 5,600,326, Feb.1997

[21] H. Boche, et al., &Quot; Analysis of different precoding / decoding strategies for multiuser beamforming ", IEEE Vehic. Tech. Conf., Vol. 2003

[22] M. Schubert, et al., &Quot; Joint 'dirty paper' pre-coding and downlink beamforming, vol. 2, pp. 536-540, Dec. 2002

[23] H. Boche, et al., "A general duality theory for uplink and downlink beamforming", vol.1, pp. 87-91, Dec. 2002

[24] K. K. Wong, R. D. Murch, and K. B. Letaief, "A joint channel diagonalization for multiuser MIMO antenna systems," IEEE Trans. Wireless Comm., Vol. 2, pp. 773-786, Jul 2003;

[25] Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, "Zero forcing methods for downlink spatial multiplexing in multiuser MIMO channels," IEEE Trans. Sig. Proc., Vol. 52, pp. 461-471, Feb. 2004.

II. Description of the disclosure from Related Application No. 12 / 917,257

In the following, wireless RF (radio frequency) communication systems and methods for suppressing interference to other users while generating wireless links for a given user using a plurality of cooperating distributed transmission antennas are described. The coordination between the different transmit antennas is made possible through user clustering. The user cluster is a subset of transmit antennas with a signal that can be reliably detected by a given user (i. E., Received signal strength above a noise or interference level). Every user in the system defines its own user cluster. The waveforms transmitted by the transmit antennas within the same user cluster coherently combine to generate RF energy at the location of the target user and to generate points of zero RF interference at the location of any other user reachable by such antennas .

이다. 송신기들은 M개의 송신 안테나와 K명 사용자 사이의 CSI (

)를 아는 것으로 가정한다. 간소화를 위해, 모든 사용자는 단일 안테나를 구비하는 것으로 가정하지만, 동일 방법은 사용자당 다수의 수신 안테나로 확장될 수 있다. 아래와 같이 M개의 송신 안테나로부터 K명의 사용자로의 채널 벡터들 (

)을 결합함으로써 얻어지는 채널 행렬 H를 고려한다.Consider a system with M transmit antennas in one user cluster and K users reachable by such M antennas,

to be. Transmitters use CSI between the M transmit antennas and K users

). For simplicity, all users are assumed to have a single antenna, but the same method can be extended to multiple receive antennas per user. Channel vectors from M transmit antennas to K users as follows

Quot;) &lt; / RTI &gt;

.

.

)은 아래의 조건을 충족시키도록 계산된다.The pre-coding weights that produce RF energy for user k and 0 RF energy for all other K-1 users (

) Is calculated to satisfy the following conditions.

는 행렬 H의 k 번째 행을 제거함으로써 얻어지는 사용자 k의 유효 채널 행렬이고,

은 모두 0의 엔트리들을 갖는 벡터이다.here,

Is the effective channel matrix of user k obtained by removing the k &lt; th &gt; row of matrix H,

Is a vector with entries of all zeros.

In one embodiment, the wireless system is a DIDO system that uses user clustering to create a wireless communication link for a target user, while pre-eliminating interference to any other users reachable by antennas located within the user cluster do. In U.S. Serial No. 12 / 630,627, a DIDO system is described that includes the following elements.

DIDO clients: user terminals having one or more antennas;

DIDO distributed antennas: transceiver stations that cooperatively operate to transmit pre-coded data streams to multiple users and suppress interference between users;

DIDO Base Transceiver Stations (BTS): A central processor that generates pre-coded waveforms for DIDO distributed antennas.

DIDO Base Station Network (BSN): A wired backhaul connecting a BTS to DIDO distributed antennas or other BTSs.

The DIDO distributed antennas are grouped into different subsets according to their spatial distribution with respect to the location of the BTSs or DIDO clients. As shown in Fig. 36, three types of clusters are defined.

The super-cluster 3640 is a set of DIDO distributed antennas connected to one or more BTSs, so the round-trip latency between all BTSs and each of the users is within the constraints of the DIDO pre-coding loop.

DIDO cluster 3641 is a set of DIDO distributed antennas connected to the same BTS. When a supercluster contains only one BTS, its definition coincides with the DIDO cluster.

User cluster 3642 is a set of DIDO distributed antennas that cooperatively transmit pre-coded data to a given user.

For example, BTSs are local hubs that are connected to other BTSs via BSN and to DIDO distributed antennas. BSNs include, but are not limited to, digital subscriber line (DSL), ADSL, VDSL [6], cable modem, fiber optic ring, T1 line, hybrid fiber coaxial (HFC) network and / And may be configured with various network technologies. All BTSs in the same supercluster share information about the DIDO pre-coding via the BSN, thus the round-trip latency is within the DIDO pre-coding loop.

37, the dots represent DIDO distributed antennas, the crosses are users, and the dashed lines represent user clusters for each of the users U1, U8. The method described below is designed to create a communication link for a target user U1 while generating points of zero RF energy for any other user U2-U8 in or outside the user cluster.

A similar method has been proposed in [5], where 0 RF energy points have been generated to eliminate interference in the overlapping regions between DIDO clusters. Additional antennas were needed to transmit signals to clients in the DIDO cluster while suppressing inter-cluster interference. One embodiment of the method proposed in the present application does not attempt to eliminate DIDO intercluster interference, but instead attempts to eliminate interference (or disregard) to any other client in its neighborhood &Lt; RTI ID = 0.0 &gt; interference &lt; / RTI &gt;

이 충족되도록 (도 37에 도시된 바와 같이) 사용자 클러스터에 추가적인 송신 안테나들이 추가될 수 있다.One idea associated with the proposed method is that users far enough away from the user cluster are not affected by radiation from the transmit antenna due to large path loss. Users within or near the user cluster receive interference-free signals due to pre-coding. Furthermore,

Additional transmit antennas may be added to the user cluster (as shown in Figure 37).

One embodiment of a method of using user clustering is comprised of the following steps.

a. Link quality measurement: The link quality between all DIDO distributed antennas and all users is reported as BTS. The link quality criterion consists of a signal-to-noise ratio (SNR) or a signal-to-interference plus noise ratio (SINR).

In one embodiment, DIDO dispersion antennas transmit training signals, and users estimate the received signal quality based on such training. The training signals are designed to be orthogonal in time, frequency or code domains so that users can distinguish between different transmitters. Alternatively, the DIDO antennas transmit narrowband signals (i.e., a single tone) at one particular frequency (i.e., a beacon channel), and users estimate link quality based on the beacon signal. As shown in FIG. 38A, one threshold is defined as the minimum signal amplitude (or power) above the noise level in order to successfully demodulate the data. Any link quality norm values below this threshold are assumed to be zero. The link quality criterion is quantized for a finite number of bits fed back to the transmitter.

In different embodiments, training signals or beacons are transmitted from users and the link quality is determined by the DIDO transmit antennas (as in FIG. 38B) assuming reciprocal between uplink (DL) and downlink . Path loss reciprocity is a problem in time division duplexing (TDD) systems and frequency division duplexing (FDD) systems where UL and DL frequency bands are relatively close (with UL and DL channels at the same frequency) It is a realistic assumption.

The information on link quality norms is shared between different BTSs via the BSN as shown in FIG. 37, so all BTSs know the link quality between all antennas / user couples across different DIDO clusters.

b. Definition of user clusters: The link quality norms of all radio links in DIDO clusters are entries for a link quality matrix that is shared among all BTSs over the BSN. An example of a link quality matrix for the scenario of FIG. 37 is shown in FIG.

The link quality matrix is used to define user clusters. For example, FIG. 39 shows a selection of a user cluster for user U8. A subset of transmitters (i.e., active transmitters) with non-zero link quality criteria for user U8 are first identified. These transmitters fill the user cluster for user U8. Then, a sub-matrix containing non-zero entries from transmitters in the user cluster to other users is selected. Since the link quality criteria are only used to select user clusters, they can be quantized using only two bits (i.e., to identify states above or below the thresholds in Figure 38), thus lowering the feedback overhead .

에 위배된다. 따라서, 하나 이상의 열을 하위 행렬에 추가하여 그러한 조건을 충족시킨다. 송신기들의 수가 사용자들의 수를 초과하는 경우, 여분의 안테나들은 다이버시티 스킴들(즉, 안테나 또는 고유 모드 선택)을 위해 사용될 수 있다.Another example for user U1 is shown in FIG. In this example, the number of active transmitters is less than the number of users in the sub-matrix,

. Thus, one or more columns are added to the sub-matrix to satisfy such conditions. If the number of transmitters exceeds the number of users, the extra antennas may be used for diversity schemes (i.e. antenna or eigenmode selection).

Another example for user U4 is shown in Fig. Note that a sub-matrix can be obtained as a combination of two sub-matrices.

c. CSI Reporting to BTSs: When user clusters are selected, the CSI from all transmitters in the user cluster to all users reached by such transmitters can be used by all BTSs. CSI information is shared between all BTSs via the BSN. In TDD systems, UL / DL channel reciprocity can be used to derive CSI from training over UL channels. In FDD systems, feedback channels from all users to BTSs are needed. To reduce the amount of feedback, only the CSI corresponding to non-zero entries in the link quality matrix is fed back.

d. DIDO Pre-coding: Finally, DIDO pre-coding is applied to all CSI sub-matrices corresponding to different user clusters (e.g., as described in related U.S. patent applications).

의 특이 값 분해(SVD)가 계산되고, 사용자 k에 대한 사전 코딩 가중치

가

의 널 하위 공간에 대응하는 우측 특이 벡터로서 정의된다. 대안으로서, M>K이고, SVD가 유효 채널 행렬을

로서 분해하는 경우, 사용자 k에 대한 DIDO 사전 코딩 가중치는 아래 수학식에 의해 주어진다.In one embodiment, the effective channel matrix

(SVD) &lt; / RTI &gt; of the user k is calculated, and the pre-coding weight

end

Is defined as a right singular vector corresponding to a null subspace of &lt; RTI ID = 0.0 &gt; Alternatively, if M &gt; K and SVD is the effective channel matrix

, The DIDO pre-coding weight for user k is given by the following equation.

는

의 널 하위 공간의 특이 벡터들인 열들을 갖는 행렬이다.here,

The

&Lt; / RTI &gt; is a matrix having columns that are singular vectors of a null subspace of &lt; RTI ID = 0.0 &gt;

의 널 하위 공간 내의 우측 특이 벡터는 0의 고유값에 대응하는 C의 고유 벡터와 동일하다는 점에 주목한다.From the basic linear algebra considerations,

Note that the right singular vector in the null subspace of e is equal to the eigenvector of C corresponding to the eigenvalue of zero.

로서 분해된다. 그러면,

의 SVD의 계산에 대한 하나의 대안은 C의 고유값 분해를 계산하는 것이다. 거듭제곱 방법과 같이 고유값 분해를 계산하기 위한 여러 방법들이 존재한다. C의 널 하위 공간에 대응하는 고유 벡터에만 관심이 있으므로, 반복에 의해 설명되는 역 거듭제곱 방법이 사용된다.Here, the effective channel matrix depends on the SVD

. then,

One alternative to the SVD computation is to compute the eigenvalue decomposition of C. There are several ways to calculate eigenvalue decomposition, such as the power method. Since we are only interested in the eigenvectors corresponding to the null subspaces of C, the inverse power method described by iteration is used.

)는 랜덤 벡터이다.Here, the vector in the first iteration

) Is a random vector.

)가 알려지면(즉, 0), 역 거듭제곱 방법은 수렴을 위해 한 번의 반복만을 필요로 하며, 따라서 계산 복잡성이 감소한다. 이어서, 사전 코딩 가중치 벡터를 다음과 같이 표현한다.The eigenvalues of the null subspace (

) Is known (ie, 0), the inverse power method requires only one iteration for convergence, thus reducing computational complexity. Next, the pre-coding weight vector is expressed as follows.

은 1과 동일한 실수 엔트리들을 갖는 벡터이다(즉, 사전 코딩 가중치 벡터는

의 열들의 합이다).here,

Is a vector with the same real entries as 1 (i.e., the pre-coding weight vector is

Lt; / RTI &gt;

DIDO pre-coding computation requires a matrix inversion. There are several numerical solutions to reduce the complexity of matrix inversion, such as Strassen's algorithm [1] or Coppersmith-Winograd's algorithm [2,3]. Since C is by definition Hermitian matrix, the alternative solution is to decompose C into its real and imaginary components and calculate the matrix inversion of the real matrix according to the method in [4, section 11.4].

Another feature of the proposed method and system is its reconfigurability. When the client moves between different DIDO clusters as in FIG. 42, the user cluster tracks its movements. That is, the subset of transmit antennas is continually updated as the client changes its position, and the effective channel matrix (and corresponding precoding weights) are recalculated.

The method proposed herein is valid within the supercluster of FIG. 36 because the links between BTSs over the BSN must have a low latency. To suppress interference in overlapping regions of different superclusters, it is possible to use our method in [5] to generate points of zero RF energy in the interference regions between DIDO clusters using additional antennas Do.

It should be noted that the terms " user " and " client " are used interchangeably herein.

References

[1] S. Robinson, "Toward an Optimal Algorithm for Matrix Multiplication", SIAM News, Volume 38, Number 9, November 2005.

[2] D. Coppersmith and S. Winograd, " Matrix Multiplication via Arithmetic Progression ", J. Symb. Comp. vol. 9, pp. 251-280, 1990.

[3] H. Cohn, R. Kleinberg, B. Szegedy, C. Umans, "Group-theoretic Algorithms for Matrix Multiplication", p. 379-388, Nov. 2005.

[4] W.H. Press, S.A. Teukolsky, W. T. Vetterling, B.P. Flannery " NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING ", Cambridge University Press, 1992.

[5] A. Forenza and S. G. Perlman, "Interference Management, Handoff, Power Control and Link Adaptation in Distributed-Input Distributed-Output (DIDO) 12 / 802,988, filed June 16, 2010.

Odenhammar, "VDSL2: Next important broadband technology", Ericsson Review No. 1, 2006.[6] Per-Erik Eriksson and

Odenhammar, " VDSL2: Next important broadband technology " 1, 2006.

III. Systems and methods for using coherent regions in wireless systems

The capacity of multiple antenna systems (MAS) in the actual propagation environments is a function of the spatial diversity available over the radio link. Space diversity is determined by the geometry of the transmit and receive antenna arrays, as well as the distribution of scattered objects within the wireless channel.

One popular model for MAS channels is the so-called clustered channel model, which defines groups of spawning devices as clusters located around transmitters and receivers. In general, the more clusters and the larger the angular width, the greater the spatial diversity and capacity achievable over the wireless links. Clustering channel models have been verified through actual measurements [1-2], and the variations of those models are different for indoor (ie, IEEE 802.11n technology group for WLANs [3]) and outdoor (3GPP for 3G cellular systems Technical Specification Group [4]) was adopted by wireless standards.

Other factors that determine spatial diversity in wireless channels include antenna element spacing [5-7], number of antennas [8-9], array aperture [10-11], array geometry [5,12,13] , Polarizations and antenna patterns [14-28].

An integrated model describing the characteristics of the propagation channel for spatial diversity (or degrees of freedom) of wireless links, as well as the effects of antenna array design, is presented in [29]. The received signal model of [29] is given by the following equation.

는 송신 신호를 설명하는 편파 벡터이고,

는 송신 및 수신 어레이들을 각각 설명하는 편파 벡터 위치들이고,

는 아래 수학식에 의해 주어지는 송신 및 수신 벡터 위치들 사이의 시스템을 응답을 설명하는 행렬이다.here,

Is a polarization vector describing a transmission signal,

Are the polarization vector positions describing the transmit and receive arrays, respectively,

Is a matrix describing the system response between the transmit and receive vector positions given by: &lt; EMI ID = 1.0 &gt;

는 각각 송신 및 수신 어레이 응답들이고,

는 송신 방향

과 수신 방향

사이의 복소수 이득들인 엔트리들을 갖는 채널 응답 행렬이다. DIDO 시스템들에서, 사용자 장치들은 단일 또는 다수의 안테나를 가질 수 있다. 간소화를 위해, 이상적인 등방성 패턴들을 갖는 단일 안테나 수신기들을 가정하며, 시스템 응답 행렬을 아래와 같이 다시 표현한다.here,

Are the transmit and receive array responses, respectively,

The transmission direction

And reception direction

Lt; / RTI &gt; is a channel response matrix with entries that are complex gains between. In DIDO systems, user devices may have single or multiple antennas. For simplicity, we assume single-antenna receivers with ideal isotropic patterns and re-express the system response matrix as:

만이 고려된다.Here,

Is considered.

From the Maxwell equations, and the far-field term of the Green function, the array response can be approximated as [29].

이고, P는 안테나 어레이를 정의하는 공간이고, 여기서here,

, P is the space defining the antenna array, where

이다. 편파되지 않은 안테나들에 대해, 어레이 응답의 연구는 위의 적분 커널의 연구와 동등하다. 이하, 상이한 타입의 어레이들에 대한 적분 커널들의 표현들에 대해 닫힌 것을 보여준다.Lt; / RTI &gt;

to be. For un-polarized antennas, the study of array response is equivalent to the study of the integral kernel above. Hereinafter, it is shown closed for expressions of integral kernels for different types of arrays.

Non-polarized linear arrays

For unpaired linear arrays of length L (normalized by wavelength) and antenna elements oriented along the z-axis and centered at the origin, an integral kernel is provided by the following equation [29].

When the above equations are expanded to the series of shifted dyads, the sinc function has a resolution of 1 / L, and the dimension of the subspace (ie, degree of freedom) that is array limited and approximately wave vector limited is .

이다. 브로드사이드 어레이(broadside array)들에 대해

인 반면, 엔드파이어(endfire)에 대해서는

이라는 점에 주목한다.here,

to be. About broadside arrays

, Whereas for endfire,

.

Non-polarized sphere arrays

The integral kernel for a spherical array of radius R (normalized by wavelength) is given by the following equation [29].

)이고, 자유도는 아래 수학식에 의해 주어진다.By decomposing the above function using the sum of the first kind of Gaussian functions, the resolution of the sphere arrays is 1 / (

), And the degree of freedom is given by the following equation.

이다.Where A is the area of the sphere array,

to be.

Coherence areas in wireless channels

The relationship between the resolution of the spherical arrays and their area (A) is shown in Fig. The middle spheres are spherical arrays of area A. The projection of the channel clusters on the unit spheres defines different scattering areas of a size proportional to the width of the clusters. The region of size 1 / A in each cluster, referred to as the " cohesion region ", represents the projection of the fundamental functions of the radiation field of the array and defines the resolution of the array in the wave vector domain.

43 and 44, it is noted that the size of the cohesion region decreases inversely with the size of the array. In fact, larger arrays can concentrate their energy on smaller areas and produce a greater number of degrees of freedom (D _F ). Note that the total number of degrees of freedom depends on the width of the cluster as shown in the definition above.

Figure 45 shows another example in which the array size covers an area much larger than that of Figure 44 to create additional degrees of freedom. In DIDO systems, the array aperture can be approximated by the entire area covered by all DIDO transmitters (assuming that the antennas are spaced apart by fractions of the wavelength). In addition, Figure 45 shows that DIDO systems can increase the number of degrees of freedom by reducing the size of the cohesion regions by dispersing the antennas in space. Note that these figures are created assuming ideal spherical arrays. In practical scenarios, the shape of randomly distributed DIDO antennas and resulting aggregation regions across large regions may not be as regular as in the Figures.

Figure 46 shows that as clusters of radio waves are scattered due to an increase in the number of objects between DIDO transmitters, more clusters are included in the radio channel as the array size increases. Thus, according to the definition above, it is possible to generate additional degrees of freedom by stimulating an increased number of basic functions (over the radiation field).

The multi-user (MU) multi-antenna systems (MAS) described in the present patent application utilize the cohesion region of radio channels to generate a plurality of simultaneous independent non-interfering data streams for different users. For given channel conditions and user distribution, the basic functions of the emission field are selected to generate independent and simultaneous wireless links for different users in a manner that all users experience uninterrupted links. When the MU-MAS knows the channel between all transmitters and all users, it adjusts the precoding transmissions based on such information to create individual cohesion zones for different users.

In one embodiment of the present invention, the MU-MAS uses non-linear dictionary coding such as dirty-paper coding [30-31] or TH (Tomlinson-Harashima) [32-33] precoding. In another embodiment of the present invention, the MU-MAS may be a block diagonalization as in our previous patent applications or zero-forcing beamforming (ZF-BF) [34] BD). &Lt; / RTI &gt;

To enable pre-coding, the MU-MAS needs knowledge of channel state information (CSI). The CSI can be used by the MU-MAS over the feedback channel or estimated over the uplink channel assuming that uplink / downlink channel mutuality is possible in time division duplex (TDD) systems. One way to reduce the amount of feedback needed for CSI is to use limited feedback techniques [35-37]. In one embodiment, the MU-MAS uses limited feedback techniques to reduce the CSI overhead of the control channel. In limited feedback techniques, codebook design is important. One embodiment defines a codebook from basic functions that span the emission field of the transmit array.

As users move in space, or when the propagation environment changes over time due to moving objects (such as people or cars), the positions and shape of the cohesion regions change. This is due to the Doppler effect in well-known wireless communications. The MU-MAS described in this patent application adjusts the pre-coding to continuously adapt the coherence regions for all users when the environment changes due to the Doppler effect. The adaptation of these coherent regions creates concurrent non-interfering channels for different users.

Another embodiment of the present invention adaptively selects a subset of the antennas of the MU-MAS system to create coherent regions of different sizes. For example, if the users are sparsely distributed in space (i.e., the rural area or the time when the use of radio resources is low), only a small subset of antennas is selected, as in FIG. 43, It is larger than the size. Alternatively, more crowded areas are selected to create small cohesion areas for users immediately adjacent to each other in populated areas (i. E., When the use of an urban area or radio service is greatest).

In one embodiment of the present invention, the MU-MAS is a DIDO system as described in the above-mentioned patent applications [0003-0009]. The DIDO system generates coherent regions for different users using linear or non-linear pre-coding and / or limited feedback techniques.

Numerical Results

We start by calculating the number of degrees of freedom in traditional multiple-input multiple-output (MIMO) systems as a function of array size. Unaligned linear arrays and two types of channel models, namely indoor channel models such as in the IEEE 802.11n standard for Wi-Fi systems and outdoor channel models such as in the 3GPP-LTE standard for cellular systems. The indoor channel mode in [3] defines the number of clusters in the range [2, 6] and each width in the range [15 ^o , 40 ^o ]. The outdoor channel model for the urban microcontroller defines angular widths in about six clusters and about 20 ^o base stations.

Figure 47 shows the degrees of freedom of MIMO systems in actual indoor and outdoor propagation scenarios. For example, considering linear arrays with ten antennas spaced one wavelength apart, the maximum degrees of freedom (or number of spatial channels) available over the air link is limited to about 3 for outdoor scenarios and 7 for indoor do. Of course, the indoor channels provide more degrees of freedom due to the greater angular width.

The degrees of freedom in the DIDO systems are then calculated. Consider an example in which antennas are distributed over 3D space, such as metropolitan scenario where DIDO access points may be distributed in different layers of adjacent buildings. Thus, we model DIDO transmit antennas (all connected together via fiber optic or DSL backbone) as a sphere array. It is also assumed that the clusters are uniformly distributed over the solid angle.

Figure 48 shows the degrees of freedom of DIDO systems as a function of array diameter. Note that for the same diameter as 10 wavelengths, about 1000 degrees of freedom are available in the DIDO system. In theory, it is possible to generate up to 1000 non-interfering channels for users. The increase in spatial diversity due to spaced antennas in space is a key to the multiplexing gain provided by DIDO over conventional MIMO systems.

] 내에 분산되는 것으로 가정하며, 클러스터들에 대한 입체각을

로서 정의한다. 예를 들어, 2층 빌딩들을 갖는 시외 시나리오들에서, 산란기들의 고도각은

일 수 있다. 이 경우, 파장의 함수인 자유도들의 수가 도 48에 도시된다.By way of comparison, DIDO systems are used to show degrees of freedom achievable in out-of-town environments. When the clusters are at an altitude angle [

], Assuming that the solid angle for the clusters is

. For example, in suburban scenarios with two-story buildings,

Lt; / RTI &gt; In this case, the number of degrees of freedom which is a function of the wavelength is shown in FIG.

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IV. System and method for planned evolution and degradation of multi-user spectrum

An increase in the demand for high-speed wireless services and an increase in the number of cellular telephone subscribers has been observed in digital voice (GSM [3-4], IS-95 CDMA [5]) from early analog voice services (AMPS [ ), Data traffic (EDGE [6], EV-DO [7]) and Internet browsing (Wi-Fi [8-9], WiMAX [10-11], 3G [12-13], 4G [ Supporting standards have radically revolutionized technology in the wireless industry for the last 30 years. This multi-year wireless technology growth was made possible by two key efforts:

i) The Federal Communications Commission (FCC) [16] has allocated a new spectrum to support emerging standards. For example, in first generation AMPS systems, the number of channels allocated by the FCC increased from the first 333 in 1983 to 416 in the late 1980s to support an increasing number of cellular clients. More recently, commercialization of technologies such as Wi-Fi, Bluetooth and ZigBee was possible through the use of the unlicensed ISM band, again assigned by the FCC in 1985 [17].

ii) The wireless industry has developed new technologies that utilize limited available spectrum more efficiently to support higher data rate links and an increased number of subscribers. One major revolution in the wireless world was the shift from analog AMPS systems to digital D-AMPS and GSM in the 1990s, which enabled much higher call volume for a given frequency band due to improved spectral efficiency. Other radical movements were made in the early 2000s by spatial processing techniques such as multiple input multiple output (MIMO), which improved the data rate by a factor of four over previous wireless networks and resulted in different standards (i.e. IEEE 802.11n, IEEE 802.16 for WiMAX, 3GPP for 4G-LTE).

Despite efforts to provide solutions for high-speed wireless access, the wireless industry provides high-definition (HD) video streaming to meet the growing demand for services such as gaming, (Including rural areas that are heavy and impractical places). At present, the most advanced wireless standard systems (i.e., 4G-LTE) can not provide data rate requirements and latency constraints to support HD streaming services, especially when the network is overloaded due to high volume concurrent links . Once again, the main drawbacks were limited spectral availability, and lack of spectrum-efficient technologies that could truly improve data rates and provide full coverage.

Recently, a new technique called distributed input distributed output (DIDO) [18-21] has emerged and has been described in our previous patent applications [0002-0009]. DIDO technology enables HD wireless streaming services in overloaded networks, ensuring increased orders of magnitude of spectrum efficiency.

At the same time, the US government has addressed the problem of lack of spectrum by launching a plan to free the spectrum of 500 ㎒ for the next decade. This plan was initiated on June 28, 2010, with the aim of enabling emerging wireless technologies to operate in new frequency bands and providing high-speed wireless coverage in urban and rural areas [22]. As part of this plan, on September 23, 2010, the FCC opened the VHF and UHF spectra of about 200 MHz for unauthorized use called "white space" [23]. One limitation to operating in these frequency bands is that it should not cause harmful interference to existing wireless microphone devices operating in the same band. Accordingly, as of July 22, 2011, the IEEE 802.22 activity group has been developing a cognitive radio technology (or spectrum) that dynamically monitors the spectrum and has key features that operate in the available bands to prevent harmful interference to coexisting wireless devices Detection) [24]. The debate to allocate part of the white space to authorized use, and to bring it to the spectrum auction, has only recently been achieved [25].

Coexistence of unauthorized devices within the same frequency bands and spectrum controversy for unlicensed versus unlicensed use have been two major challenges to FCC spectrum allocation schemes over the years. For example, in white space, coexistence between wireless microphones and wireless communication devices has become possible through cognitive radio technology. However, cognitive radio can only provide a fraction of the spectral efficiency of other technologies that use spatial processing such as DIDO. Similarly, the performance of WiFi systems has significantly degraded over the last decade due to the increase in the number of access points, and the use of Bluetooth / ZigBee devices that operate in the same unauthorized ISM band and generate uncontrolled interference. One disadvantage of unauthorized spectrum is the unregulated use of RF devices that will continue to contaminate the spectrum over the years. RF contamination also hinders unauthorized spectrum from being used for future authorized operations and thus limits important market opportunities for wireless broadband commercial services and spectrum auctions.

We propose new systems and methods that allow dynamic allocation of radio spectrum to enable coexistence and evolution of different services and standards. One embodiment of our method dynamically assigns qualifications to operate in certain portions of the spectrum to RF transceivers and provides degradation of the same RF devices to provide the following features:

i) Spectral reconfigurability to enable new types of radio operations (i.e., permit vs. unauthorized) and / or to meet new RF power emission limits. This feature enables spectral auctions whenever necessary without the need to plan for the use of unauthorized spectrum. This also enables the transmit power levels to be adjusted to meet the new power radiation levels enforced by the FCC.

ii) is capable of operating in the same band (i.e., white space and wireless microphones, Wi-Fi and Bluetooth / ZigBee), dynamically reassigning bands when new technologies are created, Coexistence of different technologies.

iii) systems move to more advanced technologies that can provide higher spectral efficiency, better coverage, and improved performance to support new types of services that require higher QoS (i.e., HD video streaming) The uninterrupted evolution of the wireless infrastructure.

A system and method for planned evolution and degradation of a multi-user spectrum is described below. One embodiment of the present system may include one or more central processors (CP) 4901-4904 and one or more distributed nodes (DN) 4911-4913 communicating via wired or wireless connections, as shown in Figure 49 ). For example, with respect to 4G-LTE networks [26], the central processor is an access core gateway (ACGW) that is connected to several Node B transceivers. Wi-Fi In this regard, the central processor is an Internet Service Provider (ISP), and distributed nodes are Wi-Fi access points that are connected to ISPs via modems or a direct connection to a cable or DSL. In another embodiment of the invention, the system comprises a distributed input distributed output (DIDO) system having one central processor (or BTS) and distributed nodes that are DIDO access points (or DIDO distributed antennas connected to the BTS via BSN) System [0002-0009].

The DNs 4911-4913 communicate with the CPs 4901-4904. The information exchanged from the DNs to the CP is used to dynamically adjust the configuration of the nodes to the evolved design of the network architecture. In one embodiment, the DNs 4911-4913 share their identification number with the CP. The CP stores the identification numbers of all the DNs connected through the network in search tables or in a shared database. Such search tables or databases may be shared with other CPs and such information is synchronized so that all CPs always have access to the most up-to-date information for all DNs on the network.

For example, the FCC may decide to allocate a portion of the spectrum for unauthorized use, and the proposed system may be designed to operate within such a spectrum. Then, due to lack of spectrum, it may be necessary for the FCC to allocate some of such spectrum to authorized carriers for commercial carriers (ie AT & T, Verizon or Sprint), defense or public safety. This coexistence will not be possible in traditional wireless systems because existing wireless devices operating in unlicensed bands will create harmful interference to authorized RF transceivers. In our proposed system, the distributed nodes exchange control information with the CPs (4901-4903) to adapt their RF transmissions to the evolution band scheme. In one embodiment, the DNs 4911-4913 were initially designed to operate over different frequency bands of the available spectrum. As the FCC assigns one or more portions of such spectrum to authorized operations, CPs exchange control information with unauthorized DNs and reconfigure them to shut down the frequency bands for authorized use, Do not interfere with the learned DNs. This scenario is shown in Figure 50 where unauthorized nodes (e.g., 5002) are indicated by solid circles, and authorized nodes are indicated by vacant circles (e.g., 5001). In another embodiment, the entire spectrum can be assigned to the new authorized service, and the CPs use the control information to shut down all unauthorized DNs, thereby preventing interference to the authorized DNs. This scenario is shown in Figure 51 where degenerated unauthorized nodes are covered with a cross.

As another example, to meet FCC exposure limits [27], it may be necessary to limit the power emissions for certain devices operating in a given frequency band. For example, the wireless system may be designed for fixed wireless links with DNs 4911-4913 initially connected to outdoor rooftop transceiver antennas. The same system can then be updated to support DNs with indoor portable antennas to provide better indoor coverage. The FCC exposure limits for portable devices are more restrictive than rooftop transmitters, perhaps due to the closer proximity to the human body. In this case, outdated DNs designed for outdoor applications can be reused for indoor applications as long as the transmit power settings are adjusted. In one embodiment of the present invention, the DNs are designed to have sets of predefined transmit power levels, and the CPs 4901-4903 send control information to the DNs 4911-4913 so that when the system is upgraded Selects new power levels, and thus meets FCC exposure limits. In another embodiment, the DNs are fabricated to have only one power emission setting, and those DNs that exceed the new power emission levels are remotely shut down by the CP.

In one embodiment, the CPs 4901-4903 periodically monitor all of the DNs 4911-4913 in the network to define their qualifications for operating as RF transceivers in accordance with certain standards. Non-current DNs are marked as degraded and may be removed from the network. For example, the DNs operating within the current power limit and frequency band remain active in the network, and all others are shut down. The DN parameters controlled by the CP are not limited to the power radiation and the frequency band; And may be any parameters that define the wireless link between the DN and the client devices.

In another embodiment of the invention, the DNs 4911-4913 can be reconfigured to enable coexistence of different standard systems within the same spectrum. For example, adjusting the power emissions, frequency bands, or other configuration parameters of certain DNs operating in conjunction with the WLAN can accommodate the adoption of new DNs designed for WPAN applications while avoiding deleterious interference.

As new wireless standards are developed to improve data rate and coverage in wireless networks, the Nodes 4911-4913 can be updated to support those standards. In one embodiment, the DNs are software defined radios (SDRs) with programmable computational capabilities such as FPGAs, DSPs, CPUs, GPUs and / or GPGPUs that execute algorithms for baseband signal processing. When a standard is upgraded, new baseband algorithms can be remotely uploaded from the CP to the DNs to reflect the new standard. For example, in one embodiment, the first standard is a CDMA sequence, which is then replaced by OFDM technology to support different types of systems. Similarly, the sample rate, power, and other parameters may be updated remotely to the DNs. These SDR features of DNs enable continuous upgrades of the network as new technologies are developed, improving overall system performance.

In another embodiment, the system described herein is a cloud wireless system comprising a plurality of CPs, distributed nodes, and networks interconnecting CPs and DNs. 52 depicts an example of a cloud wireless system where the nodes identified by solid circles (e.g., 5203) communicate with CP 5206, the nodes identified as vacant circles communicate with CP 5205, (5205 - 5206) completely communicate with each other via the network 5201. In one embodiment of the present invention, the cloud wireless system is a DIDO system, the DNs are connected to the CP, exchange information, periodically or immediately reconfigure system parameters, and dynamically adjust the changing conditions of the wireless architecture. In a DIDO system, the CP is a DIDO BTS, the distributed nodes are DIDO distributed antennas, the network is a BSN, and a number of BTSs are connected to each other via a DIDO central processor, as described in our previous patent applications [0002-0009] Respectively.

All of the DNs 5202-5203 in the cloud radio system may be grouped into different sets. Such sets of DNs may simultaneously generate non-interfering wireless links for a plurality of client devices, while each set may comprise different multiple access techniques (e.g., TDMA, FDMA, CDMA, OFDMA and / or SDMA) (E.g., QAM, OFDM) and / or coding schemes (e.g., convolutional coding, LDPC, TurboCode). Similarly, all clients may be served using different multiple access techniques and / or different modulation / coding schemes. Based on the active clients in the system and the standards they adopt for their wireless links, the CPs 5205-5206 can support such standards and dynamically select a subset of the DNs within the range of client devices.

References

[1] Wikipedia, "Advanced Mobile Phone System"

http://en.wikipedia.org/wiki/Advanced_Mobile_Phone_System

[2] AT & T, "1946: First Mobile Telephone Call"

http://www.corp.att.com/attlabs/reputation/timeline/46mobile.html

[3] GSMA, "GSM technology"

http://www.gsmworld.com/technology/index.htm

[4] ETSI, "Mobile technologies GSM"

http://www.etsi.org/WebSite/Technologies/gsm.aspx

[5] Wikipedia, "IS-95"

http://en.wikipedia.org/wiki/IS-95

[6] Ericsson, "The evolution of EDGE"

http://www.ericsson.com/res/docs/whitepapers/evolution_to_edge.pdf

[7] Q. Bi (2004-03). "A Forward Link Performance Study of the 1xEV-DO Rel. 0 System Using Field Measurements and Simulations" (PDF). Lucent Technologies.

http://www.cdg.org/resources/white_papers/files/Lucent%201xEV-DO%20Rev%20O%20Mar%2004.pdf

[8] Wi-Fi alliance, http://www.wi-fi.org/

[9] Wi-Fi alliance, "Wi-Fi certified makes it Wi-Fi"

http://www.wi-fi.org/files/WFA_Certification_Overview_WP_en.pdf

[10] WiMAX forum, http://www.wimaxforum.org/

[11] C. Eklund, R. B. Marks, K. L. Stanwood and S. Wang, "IEEE Standard 802.16: A Technical Overview of the WirelessMAN ™ Air Interface for Broadband Wireless Access"

http://ieee802.org/16/docs/02/C80216-02_05.pdf

[12] 3GPP, "UMTS", http://www.3gpp.org/article/umts

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, J. Karlsson, M. Meyer, S. Parkvall, J. Torsner, and M. Wahlqvist "Technical Solutions for the 3G Long-Term Evolution", IEEE Communications Magazine, pp.38-45, Mar. 2006[13]

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[14] 3GPP, "LTE", http://www.3gpp.org/LTE

[15] Motorola, "Long Term Evolution (LTE): A Technical Overview", http://business.motorola.com/experiencelte/pdf/LTETechnicalOverview.pdf

[16] Federal Communications Commission, "Authorization of Spread Spectrum Systems Under Parts 15 and 90 of the FCC Rules and Regulations", June 1985.

[17] ITU, "ISM band" http://www.itu.int/ITU-R/terrestrial/faq/index.html#g013

[18] S. Perlman and A. Forenza, "Distributed-input distributed-output (DIDO) wireless technology: a new approach to multiuser wireless", Aug. 2011

http://www.rearden.com/DIDO/DIDO_White_Paper_110727.pdf

[19] Bloomberg Businessweek, "Steve Perlman's Wireless Fix", July 27, 2011

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[22] The White House, "Presidential Memorandum: Unleashing the Wireless Broadband Revolution", June 28, 2010

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[23] FCC, "Open commission meeting", Sept. 23 ^rd , 2010

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[24] IEEE 802.22, "IEEE 802.22 Working Group on Wireless Regional Area Networks", http://www.ieee802.org/22/

[25] "A bill", 112th congress, 1 ^st session, July 12, 2011

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, J. Karlsson, M. Meyer, S. Parkvall, J. Torsner, and M. Wahlqvist "Technical Solutions for the 3G Long-Term Evolution", IEEE Communications Magazine, pp.38-45, Mar. 2006[26]

J. Karlsson, M. Meyer, S. Parkvall, J. Torsner, and M. Wahlqvist, "Technical Solutions for the 3G Long-Term Evolution", IEEE Communications Magazine, pp. 38-45, Mar. 2006

[27] FCC, "Evaluating compliance with FCC guidelines for human exposure to radiofrequency electromagnetic fields," OET Bulletin 65, Edition 97-01, Aug. 1997

V. System and method for compensating Doppler effects in distributed input distributed output wireless systems

In this part of the detailed description, a multi-user (MU) multi-antenna system (MAS) for multi-user wireless transmission is described that adaptively reconfigures its parameters to compensate for user mobility or Doppler effects due to changes in propagation environment . In one embodiment, the MAS is a distributed input distributed output (DIDO) system as described in commonly assigned pending patent applications [0002-0016] and as shown in FIG. The DIDO system of one embodiment includes the following components.

User Equipment (UE): The UE 5301 of one embodiment receives fixed and mobile clients data streams on the downlink (DL) channel from the DIDO backhaul and transmits data on the uplink (UL) channel to the DIDO backhaul Lt; RTI ID = 0.0 &gt; transceiver &lt; / RTI &gt;

Base Transceiver Station (BTS): The BTSs 5310-5314 of one embodiment interface with the DIDO backhaul using wireless channels. The BTSs 5310-5314 are access points that are comprised of a DAC / ADC and a radio frequency (RF) chain for converting the baseband signal to RF. In some instances, the BTS is a simple RF transceiver with a power amplifier / antenna, and the RF signal is transmitted to the BTS via RF-over-fiber technology as described in our patent application. Lt; / RTI &gt;

Controller (CTR): The CTR 5320 of one embodiment transmits training signals for time / frequency synchronization of BTSs and / or UEs, receives / transmits control information from / to UEs, CSI) or one particular type of BTS designed for certain specialized features, such as receiving channel quality information from UEs.

Central Processor (CP): The CP 5340 in one embodiment is a DIDO server that interfaces with the Internet or other types of external networks 5350 using a DIDO backhaul. The CP computes the DIDO baseband processing and transmits the waveforms to the distributed BTSs for DL transmission.

Base Station Network (BSN): The BSN 5330 in one embodiment is a network that links CPs to distributed BTSs to carry information about DL or UL channels. The BSN is a wired or wireless network or a combination of both. For example, the BSN is a DSL, cable, fiber optic network, or a line of sight or a non-line of wireless link. Also, BSNs are proprietary networks or local area networks or the Internet.

As described in commonly pending applications, the DIDO system creates independent channels for a plurality of users, and thus each user receives channels without interference. In DIDO systems, this is achieved by using spatial diversity using distributed antennas or BTSs. In one embodiment, the DIDO system uses spatial, polarization, and / or pattern diversity to increase the degrees of freedom within each channel. Increased degrees of freedom of the wireless link are used to transmit independent data streams to an increased number of UEs (i.e., multiplexing gain) and / or improve coverage (i.e., diversity gain).

The BTSs 5310-5314 are located at any convenient location where there is access to the Internet or BSN. In one embodiment of the invention, UEs 5301-5305 are randomly placed and / or surrounded by BTSs or distributed antennas, as shown in Figure 54, around them.

In one embodiment, the BTSs 5310-5314 transmit training signals and / or independent data streams to the UEs 5301 as shown in FIG. The training signal is used by the UEs for different purposes, for example time / frequency synchronization, channel estimation and / or estimation of channel state information (CSI). In one embodiment of the present invention, the MU-MAS DL uses non-linear dictionary coding such as dirty-paper coding (DPC) [1-2] or TH (Tomlinson-Harashima) [3-4] pre-coding. In another embodiment of the present invention, the MU-MAS DL may include block diagonalization (BD) as described in co-pending patent applications [0002] or zero-forcing beamforming (ZF- The same nonlinear dictionary coding is used. If the number of BTSs is greater than the number of UEs, the extra BTSs are used to improve the link quality for all UEs via diversity schemes such as antenna selection or eigenmode selection described in [0002-0016]. If the number of BTSs is less than the number of UEs, the extra UEs share wireless links with other UEs via conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDMA).

The UL channel is used to transmit CSI (or channel quality information) used by the DIDO pre-coder and / or the UE 5330 from the UEs 5301 to the data. In one embodiment, the UL channels from the UEs are multiplexed to the CTR or to the nearest BTS as shown in Figure 56 via conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDMA). In another embodiment of the present invention, spatial processing techniques are used to separate UL channels from UEs 5301 into distributed BTSs 5310-5314, as shown in FIG. For example, UL streams are transmitted from the client to the DIDO antennas over multiple input multiple output (MIMO) multiplexing schemes. MIMO multiplexing schemes include transmitting independent data streams from clients and removing co-channel interference using linear or non-linear receivers in DIDO antennas. In another embodiment, UL / DL channel reciprocity is maintained and the uplink streams are demodulated using downlink weights over the uplink, assuming that the channel does not change significantly between DL and UL transmissions due to the Doppler effect . In another embodiment, a maximum ratio combining (MRC) receiver is used over the UL channel to improve the signal quality at the DIDO antennas from all clients.

Data, control information, and CSI transmitted over the DL / UL channels are shared between the CP 5340 and the BTSs 5310-5314 via the BSN 5330. Known training signals for the DL channel may be stored in memory at the BTSs 5310-5314 to reduce the overhead for the BSN 5330. Depending on the type of network (i.e., wireless versus wired, DSL versus cable or fiber), information may be exchanged between the CP 5340 and the BTSs 5310-5314, particularly when a baseband signal is transmitted to the BTSs There may not be a sufficient data rate available through the BSN 5330. &lt; RTI ID = 0.0 &gt; For example, it is assumed that BTSs transmit 10 Mbps independent data streams to all UEs over a 5 MHz bandwidth (in accordance with the digital modulation and FEC coding scheme used over the wireless link). When 16-bit quantization is used for the real component and 16-bit quantization is used for the imaginary component, the baseband signal requires 160 Mbps of data throughput from the CP to the BTS over the BSN. In one embodiment, the CPs and BTSs have encoders and decoders for compressing and decompressing information transmitted over the BSN. On the forward link, the pre-coded baseband data transmitted from the CP to the BTSs is compressed to reduce the amount of bits and overhead transmitted over the BSN. Similarly, in the reverse link, data is compressed before transmission from the BTSs to the CP via the BSN, as well as the CSI (transmitted from the UEs to the BTSs over the uplink channel). Using different compression algorithms including, but not limited to lossless and / or lossy techniques [6], to reduce the amount of bits and overhead transmitted over the BSN.

One feature of the DIDO systems used in one embodiment is that the CP 5340 knows the CSI or channel quality information between all BTSs 5310-5314 and UEs 5301 to enable pre-coding . [0006] As described in [0006], the performance of the DIDO depends on the rate at which the CSI is transmitted to the CP compared to the rate of change of the wireless links. It is well known that changes in channel complex gain are due to changes in UE mobility and / or propagation environment that cause Doppler effects. The rate of change of the channel is measured with respect to the channel cohesion time (T _c ) which is inversely proportional to the maximum Doppler shift. To reliably perform DIDO transmissions, the latency due to CSI feedback should be a fraction of the channel cohesion time (e.g., 1/10 or less). In one embodiment, the latency through the CSI feedback loop is measured as the time between when the CSI training is transmitted and when the pre-coded data is demodulated on the UE side, as shown in Fig.

In frequency division duplex (FDD) DIDO systems, the BTSs 5310-5314 send CSI training to the UEs 5301, which in turn estimate the CSI and feed back to the BTSs. The BTSs then transmit the CSI to the CP 5340 via the BSN, which computes the DIDO pre-coded data streams and transmits them back to the BTSs via the BSN 5330. Finally, the BTSs transmit the pre-coded streams to the UEs, which demodulate the data. Referring to Figure 58, the overall latency for the DIDO feedback loop is given by:

2 * T _DL + T _UL + T _BSN + T _CP

Where T _DL and T _UL comprise times for constructing, transmitting and processing the downlink and uplink frames, respectively, T _BSN is the round trip delay through the BSN, T _CP is the time at which the CP handles the CSI, And the time it takes to schedule different UEs for the current transmission. In this example, T _DL is multiplied by 2 to account for the training signal time (from the BTS to the UE) and the feedback signal time (from the UE to the BTS). In time division duplex (TDD), when channel mutuality can be used, the UEs send CSI training to the BTSs, which calculate the CSI and send it to the CP, so that the first step (i.e., To UE) is omitted. Thus, in this embodiment, the overall latency for the DIDO feedback loop is

T _DL + T _UL + T _BSN + T _CP

to be. The latency T _BSN depends on the type of BSN, i.e., whether it is a dedicated cable, a DSL, a fiber optic connection, or the general Internet. Typical values may vary between fractions of 1 ms to 50 ms. The computation time at the CP may be reduced if the DIDO processing is implemented in a CP on dedicated processors such as an ASIC, FPGA, DSP, CPU, GPU and / or GPGPU. Moreover, if the number of BTSs 5310-5314 exceeds the number of UEs 5301, all UEs can be served simultaneously, thus eliminating the latency due to multi-user scheduling. Thus, the latency T _CP can be neglected relative to the T _BSN . Finally, the transmit and receive processing for the DL and UL is implemented on an ASIC, FPGA or DSP with a computation time that can typically be ignored, and when the signal bandwidth is relatively large (e.g., greater than 1 MHz) The duration can be very short (ie less than 1 ms). Thus, T _DL and T _UL can also be ignored compared to T _BSN .

In one embodiment of the invention, CP 5340 tracks the Doppler rate of all UEs 5301 and dynamically allocates BTSs 5310-5314 with lowest T _BSN to UEs with higher Doppler do. This adaptation is based on the following different criteria:

Types of BSNs : For example, dedicated fiber optic links typically experience lower latency than cable modems or DSLs. In addition, the lower latency BSNs are used for high mobility UEs (e.g., vehicles on the freeway, trains), while the higher latency BSNs are used for fixed-wireless or low mobility UEs Equipment, walker, vehicles within the residential area).

Types of QoS : For example, the BSN may support different types of DIDO or non-DIDO traffic. It is possible to define quality of service (QoS) with different priorities for different types of traffic. For example, the BSN assigns high priority to DIDO traffic and low priority to non-DIDO traffic. Alternatively, the higher priority QoS is allocated to the traffic for the higher mobility UEs, and the lower priority QoS is assigned to the lower mobility UEs.

Long - term statistics : For example, traffic through BSNs can vary greatly depending on time of day (eg, nighttime home use and daytime use in the office). Higher traffic loads can lead to greater latency. In addition, at different times, BSNs with higher traffic are used for UEs with lower mobility when higher traffic causes higher latency, while BSNs with lower traffic use lower latency with lower latency Lt; RTI ID = 0.0 &gt; UEs &lt; / RTI &gt;

Short - term statistics : For example, any BSN can be affected by transient network congestion that can lead to higher latency. In addition, the CP can adaptively allocate BTSs from the congested BSNs for low-mobility UEs and congested BSNs from the remaining BSNs for high-mobility UEs if the remaining BSNs are in a lower latency case where congestion causes higher latency. You can choose.

)의 함수이다.In another embodiment of the present invention, the BTSs 5310-5314 are selected based on the Doppler experienced on each respective BTS-UE link. For example, in a line-of-sight (LOS) link B of FIG. 59, the maximum Doppler shift may be calculated from the angle between the BTS-UE link and the vehicle speed v

) &Lt; / RTI &gt;

는 캐리어 주파수에 대응하는 파장이다. 따라서, LOS 채널들에서, 도플러 시프트는 도 59에서 링크 A에 대해 최대이고, 링크 C에 대해 거의 0이다. 논-LOS(NLOS)에서, 최대 도플러 시프트는 UE들 주위의 다중 경로들의 방향에 의존하지만, 일반적으로 DIDO 시스템들에서의 BTS들의 분산성으로 인해, 일부 BTS들(예로서, BTS(5312))은 주어진 UE에 대해 더 높은 도플러를 겪을 것인 반면, 다른 BTS들(예로서, BTS(5314))은 그 주어진 UE에 대해 더 낮은 도플러를 겪을 것이다.here,

Is a wavelength corresponding to the carrier frequency. Thus, in LOS channels, the Doppler shift is maximum for link A in FIG. 59 and is nearly zero for link C. [ In non-LOS (NLOS), the maximum Doppler shift depends on the direction of the multipaths around the UEs, but due to the dispersibility of the BTSs in DIDO systems in general, some BTSs (e.g., BTS 5312) Other BTSs (e.g., BTS 5314) will experience a lower Doppler for that given UE, while other BTSs (e.g., BTS 5314) will undergo a higher Doppler for a given UE.

In one embodiment, the CP tracks the Doppler rate for all BTS-UE links and selects only links with the lowest Doppler effect for all UEs. Similar to the techniques described in [0002], CP 5340 defines a " user cluster " for all UEs 5301. The user cluster may be configured to have a good link quality (as defined based on a predetermined signal-to-noise ratio (SNR) threshold) and a low link quality (e.g., defined based on a predefined Doppler threshold) Lt; / RTI &gt; is a set of BTSs with Doppler. In Figure 60, all of the BTSs 5 through 10 have a good SNR for UE1, but only the BTSs 6 through 9 experience low (e.g., below the specified threshold) Doppler effect.

The CP of this embodiment records all values of the SNR and Doppler for all BTS-UE links in a matrix, and for each UE selects a sub-matrix that satisfies SNR and Doppler thresholds. In the example shown in FIG. 61, the lower matrix is a green dotted line enclosing C _2,6 , C _2,7 , C _3,9 , C _4,7 , C _4,8 , C _4,9 and C _5,6 Lt; / RTI &gt; DIDO precoding weights are calculated based on such a sub-matrix for such UE. Note that the BTSs 5 and 10 may be reached by the UEs 2, 3, 4, 5 and 7 as indicated by the table of FIG. Then, to prevent interference to UE1 when transmitting to such other UEs, BTSs 5 and 10 may be switched off or switched to a different orthogonal channel based on conventional multiplexing techniques such as TDMA, FDMA, CDMA or OFDMA. .

In another embodiment, the adverse effect of Doppler on the performance of DIDO pre-coding systems is reduced through linear prediction, which is one technique for estimating future complex channel coefficients based on past channel estimates. Different predictive algorithms for single-input single-output (SISO) and OFDM wireless systems have been proposed in [7-11] as examples, not limitations. Knowing future channel complex coefficients makes it possible to reduce errors due to outdated CSI. For example, Figure 62 shows the channel gain (or CSI) at different times: i) t _CTR is the time at which the _CTR of Figure 58 receives CSI from UEs in FDD systems (or equivalently, Lt; RTI ID = 0.0 &gt; UL / UL &lt; / RTI &gt; ii) t _CP is the time the CSI sends to the CP via the BSN; iii) t _BTS is the time that CSI is used for pre-coding over the wireless link. In Figure 62, due to the delayed T _BSN (also shown in Figure 58), the CSI estimated at time t _CTR will be outdated by the time used for wireless transmission over the DL channel at time t _BTS (i.e., Note that the complex channel gain has changed). One way to prevent this effect from Doppler is to perform the prediction method in the CP. The CSI estimates available at the CP at time t _CTR are delayed by T _BSN / 2 due to the CTR to CP latency and correspond to the channel gain at time t ₀ in FIG. In addition, the CP predicts future channel coefficients at time t ₀ + T _BSN = t _CP , using all or a portion of the CSI estimated before time t ₀ and stored in memory. If the prediction algorithm has a minimum error propagation, then the CSI predicted at time t _CP reliably reproduces the channel gain in the future. The time difference between the predicted CSI and the current CSI is referred to as the prediction horizon and is typically scaled using channel cohesion time in SISO systems.

In DIDO systems, the prediction algorithm is more complex because it estimates future channel coefficients in both temporal and spatial domains. Linear prediction algorithms using space-time properties of MIMO wireless channels have been described in [12-13]. In [13], the performance of prediction algorithms in MIMO systems (measured with respect to the mean square error, that is to say with respect to MSE) is lower than the channel aggregation time (i.e., Doppler effect reduction) Channel aggregation distance. Thus, the predicted horizon (expressed in seconds) of the space-time methods is directly proportional to the channel cohesion time and inversely proportional to the channel cohesion distance. In DIDO systems, the cohesion distance is low due to the high spatial selectivity produced by the distributed antennas.

Prediction techniques for predicting future vector channels (i.e., CSIs from BTSs to UEs) using time and space diversity of DIDO systems are described herein. These embodiments use spatial diversity available in wireless channels to obtain CSI prediction errors and extended prediction horizons that can be ignored through any existing SISO and MIMO prediction algorithms. One important feature of these techniques is that distributed antennas use such distributed antennas when receiving unrelated complex channel coefficients from dispersed UEs.

In one embodiment of the present invention, spatial and temporal predictors are combined with an estimator in the frequency domain to enable CSI prediction in all of the available subcarriers in the system, e.g., OFDM systems. In another embodiment of the present invention, DIDO precoding weights are predicted based on previous estimates of DIDO weights (not CSI).

References

[1] M. Costa, "Writing on dirty paper," IEEE Transactions on Information Theory, Vol. 29, No. 3, Page (s): 439 - 441, May 1983.

[2] U. Erez, S. Shamai, and R. Zamir, "Capacity and lattice-strategies for cancelling known interference," Proceedings of International Symposium on Information Theory, Honolulu, Hawaii, Nov. 2000.

[3] M. Tomlinson, "New automatic equalizer employing modulo arithmetic," Electronics Letters, Page (s): 138-139, March 1971.

[4] H. Miyakawa and H. Harashima, "A method of code conversion for intersymbol interference," Transactions of the Institute of Electronic

[5] R. A. Monziano and T. W. Miller, Introduction to Adaptive Arrays, New York: Wiley, 1980.

[6] Guy E. Blelloch, "Introduction to Data Compression", Carnegie Mellon University Tech. Report Sept. 2010

[7] A. Duel-Hallen, S. Hu, and H. Hallen, "Long-Range Prediction of Fading Signals," IEEE Signal Processing Mag., Vol. 17, no. 3, pp. 62-75, May 2000.

[8] A. Forenza and R. W. Heath, Jr., "Link Adaptation and Channel Prediction in Wireless OFDM Systems," in Proc. IEEE Midwest Symp. on Circuits and Sys., Aug 2002, pp. 211-214.

[9] M. Sternad and D. Aronsson, "Channel estimation and prediction for adaptive OFDM downlinks [vehicular applications]," in Proc. IEEE Vehicular Technology Conference, vol. 2, Oct 2003, pp. 1283-1287.

[10] D. Schafhuber and G. Matz, "MMSE and Adaptive Prediction of Time-Varying Channels for OFDM Systems," IEEE Trans. Wireless Commun., Vol. 4, no. 2, pp. 593-602, Mar. 2005.

[11] I. C. Wong and B. L. Evans, "Joint Channel Estimation and Prediction for OFDM Systems," in Proc. IEEE Global Telecommunications Conference, St. Louis, MO, Dec 2005.

[12] M. Guillaud and D. Slock, " A Specular Approach to MIMO Frequencyselective Channel Tracking and Prediction, " in Proc. IEEE Signal Processing Advances in Wireless Communications, July 2004, pp. 59-63.

[13] Wong, I.C. Evans, B. L., "Exploiting Spatio-Temporal Correlations in MIMO Wireless Channel Prediction," IEEE Globecom Conf., Pp. 1-5, Dec. 2006

Embodiments of the present invention may include various steps as described above. The steps may be implemented with machine-executable instructions that cause a general purpose or special purpose processor to perform certain steps. For example, the various components in the base stations / APs and client devices described above may be implemented as software running on a general purpose or special purpose processor. Various well-known personal computer components, such as computer memory, hard drives, input devices, and the like, are not shown in the drawings, so as not to obscure the relevant aspects of the present invention.

Alternatively, in one embodiment, the various functional modules and associated steps described herein may be implemented by specific hardware components including hardwired logic for performing the steps, such as an application specific integrated circuit (" ASIC "), RTI ID = 0.0 &gt; and / or &lt; / RTI &gt; customized hardware components.

In some embodiments, certain modules, such as the above-described coding, modulation and signal processing logic 903, are programmable digital signal processors (" DSPs ") (or groups of DSPs), such as those of Texas Instruments And may be implemented on a DSP using a TMS320x architecture (e.g., a TMS320C6000, TMS320C5000, etc.). The DSP in this embodiment may be embedded in an add-on card for a personal computer, such as a PCI card for example. Of course, a variety of different DSP architectures may be used, still following the basic principles of the present invention.

The elements of the present invention may also be provided as a machine readable medium for storing machine executable instructions. The machine-readable medium may include flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media, or any other type of machine readable medium suitable for storing electronic instructions But is not limited thereto. For example, the present invention may be implemented on a computer (e.g., a client) via a communication link (e.g., a modem or a network connection), via a data signal embodied in a carrier wave or other propagation medium, And can be downloaded as a computer program that can be transmitted.

Throughout the foregoing description, for purposes of explanation, various specific details have been set forth in order to provide a thorough understanding of the present systems and methods. However, it will be apparent to those skilled in the art that the system and method may be practiced without some of such specific details. Accordingly, the spirit and scope of the present invention should be determined with reference to the following claims.

Moreover, throughout the foregoing description, numerous publications have been cited to provide a more thorough understanding of the present invention. All of these cited references are incorporated herein by reference.

Claims (45)

A multiuser (MU) multiple antenna system (MAS)
Comprising one or more central units communicatively coupled to a plurality of distributed transceiver stations or antennas over a network,
The network includes wired or wireless links or a combination of both, which are used as a backhaul communication channel,
The one or more central units communicate with the distributed transceiver station or antennas over the network to compensate for Doppler effects due to changes in user mobility or propagation environment, Adaptively reconfiguring communication between the base station and the base station,
The MU-MAS may include a user equipment (UE), a base transceiver station (BTS), a controller (CTR), a centralized processor (CP) , &Lt; / RTI &gt; BSN)
Wherein the CP adaptively selects the BTSs to be used for low mobility or high mobility UEs based on latency for the BSN. The system of claim 1, wherein the system utilizes distributed antennas that utilize spatial, polarization, and / or pattern diversity to improve data rate and / or coverage for one or multiple users in wireless systems. 2. The system of claim 1, wherein the UEs are located around or between the distributed antennas, or are surrounded by the distributed antennas. 3. The system of claim 1, wherein the MU-MAS uses composite weights at the receiver of the uplink channel to demodulate independent streams (e.g., data or CSI) from the UEs. 5. The system of claim 4, wherein the composite weights of the receiver of the uplink channel are derived from downlink precoding weights or are calculated via a maximum ratio combining receiver. 2. The system of claim 1, wherein the CP and the BTSs comprise encoders / decoders for compressing / decompressing information exchanged between them via the BSN. 7. The method of claim 6, wherein the pre-coded baseband data streams are compressed prior to being transmitted from the BTS to the MU-MAS distributed antennas / from the MU-MAS distributed antennas to the BTS to reduce overhead for the BSN. System. 2. The method of claim 1, wherein the adaptation is based on high data rate versus low data rate BSN or QoS or average traffic statistics over the BSN (e.g., weekly or nightly use of different networks) or instant traffic statistics , &Lt; / RTI &gt; transient network congestion). A method implemented in a multi-user (MU) multi-antenna system (MAS) to compensate for Doppler effects, the MU-MAS comprising at least a plurality of base stations communicatively coupled to a plurality of distributed base transceiver stations Comprising a central unit,
The method comprising measuring a Doppler rate of a first mobile user for the plurality of BTSs,
Wherein the central unit is in communication with the BTSs through the network to adaptively reconfigure communications between the BTSs and users, and wherein the adaptively reconfiguring comprises communicating with a first one of the plurality of BTSs or BTSs Dynamically allocating the first mobile user to a first set, dynamically allocating based on the measured Doppler rate for the first BTS in relation to other BTSs. 10. The method of claim 9, wherein if the first mobile user has a relatively higher measured Doppler rate than the second mobile user, dynamically allocating the first mobile user to a first set of first BTSs or BTSs And assigning the second mobile user to a second set of second BTSs or BTSs, wherein the first set of BTSs or BTSs are relatively more common than the second set of second BTSs or BTSs Having a lower associated latency. 11. The method of claim 10, wherein the latency comprises: (a) a time taken to transmit a first training signal from the first mobile user to a BTS; (b) a base station network (BSN) And (c) the CP processes channel state information (CSI) for a wireless channel between the BTSs and the first mobile user, and based on the CSI, The time taken to generate pre-coded data streams for the user and to schedule transmissions to different mobile users including the first mobile user for current transmission. 12. The method of claim 11, wherein the latency further comprises a time taken to transmit a second training signal from the BTS to the first mobile user. 10. The method of claim 9, wherein dynamically allocating is based on a combination of a link quality of a communication channel between each BTS and the first mobile user and the measured Doppler velocity associated with each BTS with respect to the first mobile user Lt; RTI ID = 0.0 &gt; and / or &lt; / RTI &gt; 14. The method of claim 13, wherein for a given Doppler velocity, the BTS with a relatively higher link quality is selected. 14. The method of claim 13, wherein for a given link quality, the BTS with a relatively lower Doppler velocity is selected. 10. The method of claim 9,
Further comprising estimating future composite channel coefficients based on past composite channel coefficients to compensate for adverse effects of Doppler on communication between the BTSs and the first mobile user. 17. The method of claim 16, wherein linear prediction is used for estimation. 10. The method of claim 9, wherein the first mobile user is further configured to determine, based on both the Doppler rate and channel state information (CSI) that defines the quality of communication channels between the mobile user and each of the BTSs in the plurality of BTSs, The first BTS or the first set of BTSs. 19. The method of claim 18,
Forming a matrix of Doppler velocities and link qualities for each of the BTSs in the plurality of BTSs for the first mobile user; And
Further comprising selecting a BTS having a Doppler rate below a specified threshold and link quality above a specified threshold. A multi-user (MU) multi-antenna system (MAS) for compensating for a Doppler effect,
A plurality of base transceiver stations (BTS);
A network coupling the plurality of base BTSs to at least one central processor (CP);
A first mobile user establishing a communication link with each of the BTSs;
Wherein the CP is in communication with the BTSs over the network to adaptively reconfigure communications between the BTSs and users, and wherein the adaptively reconfiguring comprises communicating with a Doppler rate of the first mobile user for each of the BTSs Dynamically allocating the first mobile user to a first one of the plurality of BTSs and dynamically allocating based on the measured Doppler rate for the first BTS with respect to the other BTSs Including the system. 21. The method of claim 20, wherein if the first mobile user has a relatively higher measured Doppler rate than a second mobile user, dynamically allocating allocates the first mobile user to a first BTS, And assigning a mobile user to a second BTS, wherein the first BTS has a relatively lower associated latency than the second BTS. 22. The method of claim 21, wherein the latency comprises: (a) a time taken to transmit a first training signal from the first mobile user to a BTS; (b) a base station network (BSN) And (c) the CP processes channel state information (CSI) for a wireless channel between the BTSs and the first mobile user, and based on the CSI a dictionary for the first mobile user The time taken to generate coded data streams and to schedule transmissions to different mobile users including the first mobile user for current transmission. 23. The system of claim 22, wherein the latency further comprises a time taken to transmit a second training signal from the BTS to the first mobile user. 21. The method of claim 20, wherein dynamically allocating is based on a combination of a link quality of a communication channel between each BTS and the first mobile user and the measured Doppler velocity associated with each BTS with respect to the first mobile user Wherein the system further comprises: 25. The system of claim 24, wherein for a given Doppler velocity, the BTS with a relatively higher link quality is selected. 25. The system of claim 24, wherein for a given link quality, the BTS with a relatively lower Doppler velocity is selected. 21. The system of claim 20, wherein the CP estimates future composite channel coefficients based on past composite channel coefficients to compensate for the adverse effect of Doppler on communication between the BTSs and the first mobile user. 28. The system of claim 27, wherein linear prediction is used for estimation. 21. The method of claim 20, wherein the first mobile user is further configured to determine, based on both the Doppler rate and channel state information (CSI) that defines the quality of communication channels between the mobile user and each of the BTSs in the plurality of BTSs, And is dynamically allocated to the first BTS. 30. The method of claim 29,
Form a matrix of Doppler velocities and link qualities for each of the BTSs in the plurality of BTSs for the first mobile user;
Perform additional operations to select a BTS having a Doppler rate below a specified threshold and link quality above a specified threshold. A multi-user (MU) multi-antenna system (MAS)
Comprising one or more central units communicatively coupled to a plurality of distributed transceiver stations or antennas over a network,
The network includes wired or wireless links or a combination of both, used as a backhaul communication channel,
The communication between the distributed transceiver stations or antennas and users to compensate for Doppler effects due to a change in user mobility or propagation environment by the one or more central units communicating with the distributed transceiver station or antennas via the network Adaptive reconstruction,
The MU-MAS comprises one or more sets of user equipment (UE), base transceiver stations (BTS), controllers (CTRs), central processors (CPs) and base station networks (BSN)
Wherein the CP adaptively selects the BTSs to be used for low mobility or high mobility UEs based on Doppler rates of all BTS-UE links. 32. The system of claim 31, utilizing distributed antennas that utilize spatial, polarization and / or pattern diversity to improve data rate and / or coverage for one or more users in wireless systems. 32. The system of claim 31, wherein the UEs are located around or between the distributed antennas, or are surrounded by the distributed antennas. 32. The system of claim 31, wherein the MU-MAS uses composite weights at the receiver of the uplink channel to demodulate independent streams (e.g., data or CSI) from the UEs. 35. The system of claim 34, wherein the composite weights of the receiver of the uplink channel are derived from downlink precoding weights or are calculated via a maximum ratio combining receiver. 32. The system of claim 31, wherein the CP and the BTSs comprise encoders / decoders for compressing / decompressing information exchanged between them via the BSN. 38. The method of claim 36, wherein the pre-coded baseband data streams are compressed prior to being transmitted from the BTS to the MU-MAS distributed antennas / from the MU-MAS distributed antennas to the BTS to reduce overhead for the BSN. System. A multi-user (MU) multi-antenna system (MAS)
Comprising one or more central units communicatively coupled to a plurality of distributed transceiver stations or antennas over a network,
The network includes wired or wireless links or a combination of both, used as a backhaul communication channel,
The communication between the distributed transceiver stations or antennas and users to compensate for Doppler effects due to a change in user mobility or propagation environment by the one or more central units communicating with the distributed transceiver station or antennas via the network Adaptive reconstruction,
The MU-MAS comprises one or more sets of user equipment (UE), base transceiver stations (BTS), controllers (CTRs), central processors (CPs) and base station networks (BSN)
Linear predictions are used to estimate future CSI or MU-MAS pre-coding weights to eliminate the adverse effects of Doppler on the performance of the MU-
system. 39. The system of claim 38, wherein the linear prediction is used in time, frequency and / or spatial domains. 39. The system of claim 38, utilizing distributed antennas that utilize space, polarization, and / or pattern diversity to improve data rate and / or coverage for one or more users in wireless systems. 39. The system of claim 38, wherein the UEs are located around or between the distributed antennas, or are surrounded by the distributed antennas. 39. The system of claim 38, wherein the MU-MAS uses composite weights at the receiver of the uplink channel to demodulate independent streams (e.g., data or CSI) from the UEs. 43. The system of claim 42, wherein the composite weights of the receiver of the uplink channel are derived from downlink precoding weights or are calculated via a maximum ratio combining receiver. 39. The system of claim 38, wherein the CP and the BTSs comprise encoders / decoders for compressing / decompressing information exchanged between them via the BSN. 45. The method of claim 44, wherein the pre-coded baseband data streams are compressed prior to being transmitted from the BTS to the MU-MAS distributed antennas / from the MU-MAS distributed antennas to the BTS to reduce overhead for the BSN. System.

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