TWI591976B - System and methods for coping with doppler effects in distributed-input distributed-output wireless systems - Google Patents

Description

System and method for Doppler effect in a wireless system for processing distributed input distributed output

This application is a continuation of the U.S. Patent Application Serial No. 12, filed on November 1, 2010, entitled "Systems And Methods To Coordinate Transmissions In Distributed Wireless Systems Via User Clustering" /917, 257; US Application No. 12/802,988, filed on June 16, 2010, entitled "Interference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO) Communication Systems"; US Application No. 12/802,976, entitled "System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements", which is filed on June 16, 2012, which is now granted to the United States on May 1, 2012, granting 8,170,081 US Application No. 12/802,974, filed on June 16, 2010, entitled "System And Method For Managing Inter-Cluster Handoff Of Clients Which Traverse Multiple DIDO Clusters"; System And Method For Managing Handoff Of A Client Between Different Distributed-Input-Dist ributed-Output (DIDO) Networks Based On Detected Velocity Of The Client, US Application No. 12/802,989; June 16, 2010, entitled "System And Method For Power Control And Antenna Grouping In A US Application No. 12/802,958 to Distributed-Distributed-Output (DIDO) Network; US Application No. 12, entitled "System And Method For Link adaptation In DIDO Multicarrier Systems", filed on June 16, 2010 /802,975; US Application No. 12/802,938, filed on June 16, 2010, entitled "System And Method For DIDO Precoding Interpolation In Multicarrier Systems"; "System and Method" filed on December 3, 2009 U.S. Application Serial No. 12/630,627, entitled "System and Method For Distributed Input-Distributed Output Wireless Communications", filed on June 20, 2008, which is incorporated herein by reference. U.S. Patent Application Serial No. 11/894,394, entitled "System and Method for Distributed Input Distributed Output Communications", filed on August 20, 2009, which is hereby incorporated by reference. The patent granted to the United States granted on October 6, 2009 is 7,599,420; the application dated August 20, 2007 is "Syst". U.S. Application No. 11/894,362, to the United States, issued to the United States on December 15, 2009, and the US Patent No. 7,633,994 issued on December 15, 2009; U.S. Application No. 11/894,540 to System and Method For Distributed Input-Distributed Output Wireless Communications, which is now issued to the U.S. Patent No. 7,633,381 issued on Dec. 22, 2009; filed on October 21, 2005 US Application No. 11/256,478, entitled "System and Method For Spatial-Multiplexed Tropospheric Scatter Communications", which is now granted to the United States on May 4, 2010, 7,711,030; application on April 2, 2004 U.S. Application Serial No. 10/817,731, entitled "System and Method For Enhancing Near Vertical Incidence Skywave ("NVIS") Communication Using Space-Time Coding, which is now patented in the United States granted on February 28, 2011. No. 7,885,354.

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

A single WiFi base station attached to a broadband cable Internet connection in an area without other WiFi access points (eg, a WiFi access point attached to a DSL in a rural home) (eg, utilizing 2.4 GHz 802.11b) , g or n protocol) is an example of a relatively simple multi-user wireless system consisting of a single base station shared by one or more users within its transmission range. If a 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 (eg, packet loss may occur due to a 2.4 GHz jammer (eg, microwave oven) , but there will be no packet loss due to the spectrum sharing with other WiFi devices. If the user has a medium distance or there are several obstacles in the path between the user and the WiFi access point, the user may experience Medium speed link. If a user approaches the edge of the range of WiFi access points, the user will likely experience a low speed link, and the user may experience periodic shedding if the channel change causes the signal SNR to fall below the available bit criteria. And finally, if the user is outside the range of the WiFi base station, the user will have no link at all.

When multiple users access the WiFi base station at the same time, the available data throughput is shared therebetween. Different users will usually propose different throughput requirements for the WiFi base station at a given time, but sometimes when some aggregate throughput requirements exceed the available throughput from the WiFi base station to the user, some or all users will receive the ratio. It is seeking data throughput with less data throughput. In the extreme case where a WiFi access point is shared between a very large number of users. The throughput to each user can be slowed down to creep speed, and worse, the data throughput to each user can arrive at a short burst separated by a long period of no data throughput at all. Serve other users during the cycle time. This "intermittent" data transfer can compromise specific applications like media streaming.

Adding an extra WiFi base station in the case of a large number of users will only be in a certain distance It helps. In the 2.4 GHz ISM band in the United States, there are 3 non-interfering channels available for WiFi, and if 3 WiFi base stations in the same coverage area are configured to each use a different non-interfering channel, then multiple The aggregate throughput of the coverage area between users will increase by a factor of three. But beyond that, adding more WiFi base stations in the same coverage area will not increase aggregate throughput because it will begin to share the same available spectrum between them, thereby effectively utilizing time-sharing by "rotating" the spectrum. Worker access (TDMA). This situation is common in areas of coverage with high population densities (such as in multi-dwelling units). For example, a user in a large apartment building with a WiFi adapter can be adequately attributed to many other interfering WiFi networks (eg, in other apartments) that serve other users in the same coverage area. Very poor throughput is experienced even if the user's access point is in the same room as the client device accessing the base station. While the link quality may be good in this situation, the user will receive interference from neighboring WiFi adapters operating in the same frequency band, thereby reducing the effective throughput to the user.

Current multi-user wireless systems, including both unlicensed spectrum (such as WiFi) and licensed spectrum, suffer from several limitations. These restrictions include coverage area, downlink (DL) data rate, and uplink (UL) data rate. The key goal of next-generation wireless systems, such as WiMAX and LTE, is to improve coverage areas and DL and UL data rates via Multiple Input Multiple Output (MIMO) technology. MIMO uses multiple antennas on the transmission and reception side of the wireless link to improve link quality (resulting in wider coverage) or data rate (by establishing multiple non-interfering spatial channels to each user). However, if sufficient data rates are available for each user (note that the terms "user" and "client" are used interchangeably herein), they may need to be utilized in accordance with multi-user MIMO (MU-MIMO) technology. Channel space diversity to establish non-interfering channels to multiple users (rather than a single user). See, for example, the following reference: July 2003, IEEE Trans.Info.Th., Vol. 49, pp. 1691 to 1706, G.Caire and S. Shamai, "On the achievable throughput of a Multiantenna Gaussian broadcast channel".

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

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

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

May Transaction Information Journal, Vol. 29, pp. 439-441, M. Costa, "Writing on dirty paper", May 1983.

August 2002, Proceedings of the Sensor Array and Multichannel Signal Processing Symposium, pp. 130-134, M. Bengtsson, "A pragmatic approach to multi-user spatial multiplexing".

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

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

For example, a MIMO 4×4 system (ie, four transmissions) that is error-corrected (FEC) encoded (generating a spectral efficiency of 3 bps/Hz) with 10 MHz bandwidth, 16-QAM modulation, and 3/4 rate. In the antenna and four receiving antennas, the ideal peak data rate achievable at the physical layer for each user is 4 x 30 Mbps = 120 Mbps, which is better than the transmission of high definition video content (which can only require ~10 Mbps) The rate is much higher. Has four In a MU-MIMO system with a transmit antenna, four users, and a single antenna per user, in an ideal situation (ie, independent and identically distributed (iid) channels), the downlink data rate can be used by four users. The channel space diversity is shared and can be used to establish four parallel 30 Mbps data links to the user.

Different MU-MIMO schemes have been proposed as part of the LTE standard, such as, for example, March 2007, 3GPP, "Multiple Input Multiple Output in UTRA", 3GPP TR 25.876 V7.0.0; May 2009, 3GPP, "Base Physical Channels and modulation", TS 36.211, V8.7.0; and May 2009, 3GPP, "Multiplexing and channel coding", TS 36.212, V8.7.0. However, these schemes can only provide up to 2 times (2x) improvement in DL data rate by four transmit antennas. The actual implementation of MU-MIMO technology in standard and proprietary cellular systems by companies like ArrayComm (see, for example, ArrayComm, "Field-proven results", http://www.arraycomm.com/serve.php?page =proof) As many as ~3 times increase in DL data rate (by four transmit antennas) has been generated by Domain Multiple Access (SDMA). A key limitation of the MU-MIMO scheme in cellular networks is the lack of spatial diversity at the transmission side. Spatial diversity varies with antenna spacing and multipath angular spread in the wireless link. In a cellular system using MU-MIMO technology, the transmission antenna at the base station is usually attributed to the limited area and attribution of the antenna support structure (referred to herein as the "tower", whether it is physically high or not high). They are clustered together and placed only one or two wavelengths apart from the limits of where the tower can be located. In addition, multi-path angular spread is low because the cell tower is typically placed high above the obstacle (10 meters or more) to create a wider coverage.

Other practical issues with cellular system deployment include the excessive cost of cellular antenna locations and limited availability of locations (eg, due to municipal restrictions on antenna placement, real estate costs, physical obstacles, etc.) and the network to the transmitter The cost and/or availability of connectivity (referred to herein as "reloading"). In addition, honeycomb systems are often attributed to the difficulty of reaching deep in the building due to the loss of walls, ceilings, floors, furniture and other obstacles. Account.

Indeed, the entire concept of a cellular architecture for wide-area wireless networks presupposes the rather rigid placement of cellular towers, the frequency alternation between adjacent cells, and frequent sectorization to avoid transmissions of the same frequency. Interference between the base station (base station or user). As a result, a given sector of a given cell eventually becomes a shared block of DL and UL spectrum between all users in the cell sector, and then the DL is shared primarily among such users only in the time domain. And UL spectrum. For example, a time-based multiple access (TDMA) and code division multiple access (CDMA) cellular system shares spectrum among users in the time domain. By covering these cellular systems with sectorization, it is possible to achieve a 2-3 times spatial domain benefit. Moreover, an additional 2-3x space-time domain benefit may be achieved by overlaying such cellular systems with MU-MIMO systems, such as those previously described. However, considering that the cells and sectors of the cellular system are usually in a fixed location (often specified by the place where the tower can be placed), if the user density (or data rate demand) is not arranged with the tower/sector at a given time. Even well matched, even these limited benefits are difficult to exploit. Honeycomb smart phone users often experience the result: Today users may talk or download a web page on the phone without any problems, and then suddenly find the voice quality degradation or web page after driving (or even walking) to a new location Slow down to creep speed, or even completely lose the connection. However, on different days, the user can encounter the exact opposite situation in each location. Assuming the same environmental conditions, the user may experience a situation where the user density (or data rate requirement) is highly variable, but the total available spectrum shared between users at a given location (and the total data rate) , using the technology of the prior art) the fact that it is largely fixed.

Furthermore, prior art cellular systems rely on the use of different frequencies, typically 3 different frequencies, in different neighboring cells. For a given amount of spectrum, this reduces the available data rate to one-third.

So, in summary, prior art honeycomb systems can be lost due to honeycombing Perhaps 3 times the spectrum utilization, and the improved spectrum utilization by sectorization may be 3 times and may be further improved by MU-MIMO technology, perhaps 3 times, resulting in a net 3*3/3=3 times possible spectrum utilization. The bandwidth is then typically split between users in the time domain based on which sector of the cell the user belongs to at a given time. There is even further inefficiency due to the fact that the data rate requirements of a given user are generally independent of the user's location but the available data rate varies depending on the quality of the link between the user and the base station. For example, users who are further away from the cellular base station will typically have smaller data rates available to users closer to the base station. Since the data rate is typically shared among all users in a given cellular sector, the result is that all users are subject to high distance users (e.g., at the edge of the cell) with poor link quality. The data rate requirements are affected because these users will still need the same amount of data rate, however they will consume more shared spectrum to get the data rate.

Other proposed spectrum sharing systems, such as spectrum sharing systems used by WiFi (eg, 802.11b, g, and n) and those proposed by the White Spaces Coalition, share spectrum very inefficiently because Simultaneous transmission by a base station within range of the user causes interference, and thus the system utilizes 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 the efficiency of each base station itself in terms of spectrum utilization, the base stations are collectively limited by the spectrum between each other. Time domain sharing. Other prior art spectrum sharing systems similarly rely on similar methods to mitigate interference between base stations, whether they are cellular base stations with antennas on the tower or small scale base stations, such as WiFi access points (APs). Such methods include: limiting the transmission power from the base station to limit the range of interference; beamforming (via synthetic or physical means) to narrow the interference region; time domain multiplexing of the spectrum; and/or in the user device, MU-MIMO technology with multiple cluster antennas on a base station or both. Moreover, many of these technologies are often used simultaneously in the context of advanced cellular networks that are now in place or under planning.

However, the fact that even an advanced cellular system can only achieve about a three-fold increase in spectrum utilization compared to a single user utilizing the spectrum can be understood: all such techniques are added between shared users in a given coverage area. The aggregate data rate is not effective. In particular, as a given coverage area scales on the user side, it becomes increasingly difficult to scale the available data rate within a given amount of spectrum to keep up with the user's growth. For example, in the case of a cellular system, to increase the aggregate data rate in a given area, the cell is typically subdivided into smaller cells (often referred to as microcells or picocells). Considering the restrictions on where the tower can be placed, and the need for the tower to be placed in a properly structured manner to provide coverage with minimal "dead zones", avoiding the use of interference between adjacent cells of the same frequency, These small cells can become extremely expensive. Essentially, the coverage area must be drawn, the available locations for the placement tower or base station must be identified, and then, taking into account these constraints, the designer of the cellular system must do its best to try. And, of course, if the user's data rate demand grows over time, the designer of the cellular system must redraw the coverage area again, try to find the location of the tower or base station, and work again within the constraints of the environment. Moreover, there is often no good solution at all, resulting in a dead zone in the coverage area or insufficient aggregate data rate capacity. In other words, in order to avoid the use of interference between the towers or base stations of the same frequency, the rigid physical placement requirements of the cellular system lead to significant difficulties and constraints in the design of the cellular system, and often fail to meet the user data rate and coverage. Claim.

The so-called prior art "cooperative" and "cognitive" radio systems seek to minimize the interference between each other by using smart algorithms within the radio to enable the radio to potentially "listen" to other spectrum uses. In order to increase the spectrum utilization in a given area until the channel is undisturbed. These systems are proposed to be used especially in unlicensed spectrum to increase the spectrum utilization of this spectrum.

The Mobile Internet (MANET) (see http://en.wikipedia.org/wiki/Mobile_ad_hoc_network) is a collaborative self-configuring network for peer-to-peer communication. An example of, and can be used to establish communication between radios without a cellular infrastructure, and with sufficient low power communication, can potentially mitigate interference between simultaneous transmissions outside of each other's range. A large number of routing protocols have been proposed and implemented for MANET systems (for a list of many routing protocols for various categories, see http://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols), but the common theme between them is Techniques for routing (eg, repeating) transmissions to achieve transmitter efficiency interference within the available spectrum for the purpose of achieving a particular efficiency or reliability paradigm.

All prior art multi-user wireless systems seek to improve spectrum utilization within a given coverage area by utilizing techniques that allow for simultaneous spectrum utilization between the base station and multiple users. Note that in all of these situations, techniques for simultaneous spectrum utilization between a base station and multiple users achieve simultaneous spectrum usage by multiple users by mitigating interference between waveforms of multiple users. . For example, in the case where each of the three base stations transmits to one of the three users using a different frequency, since the three transmission systems are at three different frequencies, the interference is alleviated. In the case of sectorization from the base station to three different users (180 degrees apart from the base station), interference is mitigated because beamforming prevents the three transmissions from overlapping at any user.

When such techniques are enhanced by MU-MIMO and, for example, each base station has 4 antennas, then there will be a downlink by establishing four non-interfering spatial channels to the user in a given coverage area. The potential for link throughput is increased by a factor of four. However, it still requires some techniques to mitigate the interference to multiple simultaneous transmissions to multiple users in different coverage areas.

Moreover, as previously discussed, such prior art techniques (e.g., cellularization, sectorization) are not only often compromised by the increased cost and/or deployment flexibility of multi-user wireless systems, but they often encounter Entity or actual limit on aggregate throughput in a given coverage area. For example, in a cellular system, there may not be enough available locations to install more The base station establishes a smaller cell. Also, in MU-MIMO systems, given the cluster antenna spacing at each base station location, as more antennas are added to the base station, limited spatial diversity results in a progressively decreasing throughput return.

Further, in the case of a user's wireless system where the location and density of the user are unpredictable, it results in unpredictable throughput (with frequent sharp changes), which is inconvenient for the user and causes some applications (eg, Delivery of services that require predictable throughput is unrealistic or low quality. Thus, prior art multi-user wireless systems still have a number of improvements in their ability to provide predictable and/or high quality services to users.

Although prior art multi-user wireless systems have become very sophisticated and complex over time, there is a common theme: spreading transmissions between different base stations (or special transceivers) and structuring and/or controlling transmissions so that RF waveform transmissions from different base stations and/or different special purpose transceivers are prevented from interfering with each other at the receiver of a given user.

Or, in other words, what is known to be known is that if a user happens to receive transmissions from more than one base station or a special transceiver at the same time, interference from multiple simultaneous transmissions will result in an SNR to the user's signal and / or a reduction in bandwidth, which (if severe enough) will result in the loss of all or some of the potential data (or analog information) that would otherwise be received by the user.

Therefore, in a multi-user wireless system, one or more spectrum sharing methods or another method must be utilized to avoid or mitigate such interference to users from multiple base stations or special transceivers transmitting simultaneously at the same frequency. . There are a number of prior art methods of avoiding such interference, including controlling the physical location of the base station (e.g., cellularization), limiting the power output of the base station and/or the special transceiver (e.g., limiting the transmission range), beamforming/sector Time, multiplex in time. In short, all of these spectrum sharing systems seek to address the limitations of multi-user wireless systems, that is, when multiple base stations and/or special transceivers transmitting simultaneously on the same frequency are received by the same user, the interference is obtained. Reduce or destroy information to affected users Throughput. If a large percentage (or all) of the users in the multi-user wireless system are subject to interference from multiple base stations and/or special transceivers (eg, in the event of a component failure of a multi-user wireless system) Then it can lead to a situation in which the aggregate throughput of a multi-user wireless system is drastically reduced or even lost.

Prior art multi-user wireless systems add complexity and introduce limitations to wireless networks, and frequently result in a given user experience (eg, available bandwidth, latency, predictability, reliability) in the region The situation in which other users influence the utilization of the spectrum. Considering the increasing demand for aggregated bandwidth in the wireless spectrum shared by multiple users, and relying on the reliability, predictability and low latency of multiple user wireless networks for a given user The application continues to grow, and it is clear that prior art multi-user wireless technologies suffer from many limitations. In fact, due to the limited availability of spectrum applicable to a particular type of wireless communication (eg, at wavelengths that can effectively penetrate a building's walls), it may be that prior art wireless technologies will not be sufficient to be reliable, predictable, and The increasing demand for bandwidth during low latency.

Prior Art Description of the Invention A beamforming system and method for null-steering in a multi-user scenario. Beamforming was originally conceived to maximize the received signal-to-noise ratio (SNR) by dynamically adjusting the phase and/or amplitude (i.e., beamforming weight) of the signal fed to the antenna of the array, thereby The direction concentrates energy. In a multi-user scenario, beamforming can be used to suppress interference sources and maximize 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 establish a null in the direction of the interferer. When beamforming is used at the transmitter in a multi-user downlink scenario, the weights are calculated to pre-empt inter-user interference and maximize the SINR to each user. Alternative techniques for multi-user systems, such as BD precoding, calculate precoding weights to maximize throughput in the downlink broadcast channel. The above-identified technology is described in the same application (which is hereby incorporated by reference herein in its entirety) in its entirety in the the the the the the the the the the the the the

231‧‧‧DIDO precoding unit

232‧‧‧Power Control Unit

233‧‧‧Channel Quality Information (CQI)

234‧‧‧Multiplexer

235‧‧‧DAC (digital/analog converter) unit

410‧‧‧Interference zone

411‧‧‧ main cluster

412‧‧‧Interference Cluster/Interference DIDO Cluster

413‧‧‧Interference Cluster/Interference DIDO Cluster

501‧‧‧User MC

502‧‧‧Customer IC

503‧‧‧Green Cross/Target Client TC

510‧‧‧Interference zone

511‧‧‧ main cluster

512‧‧‧Interference cluster

801‧‧‧Customer Device/Client/Target Client

802‧‧‧Main DIDO Cluster/Main DIDO Cluster (C1)

803‧‧‧Interference clusters/adjacent clusters (C2)

812‧‧‧DIDO antenna

813‧‧‧DIDO antenna

1701‧‧‧DIDO precoding unit

1702‧‧‧Power Control Unit

1703‧‧‧Channel Quality Information (CQI)

1710‧‧‧Multiplexer

1711‧‧‧DAC (digital/analog converter) unit

3640a‧‧‧ Adjacent Super Cluster

3640b‧‧‧Super Cluster

3640c‧‧‧ adjacent super cluster

3641‧‧‧DIDO Cluster

3642‧‧‧ User Cluster

4901‧‧‧Centralized Processor (CP)

4902‧‧‧Centralized Processor (CP)

4903‧‧‧Centralized Processor (CP)

4904‧‧‧Centralized Processor (CP)

4911‧‧‧Distributed nodes

4912‧‧‧Distributed nodes

4913‧‧‧Distributed nodes

5001‧‧‧ Licensed node

5002‧‧‧Unlicensed node

5201‧‧‧Network

5202‧‧‧DN or decentralized nodes

5203‧‧‧DN or decentralized nodes

5205‧‧‧Centralized Processor (CP)

5206‧‧‧Centralized Processor (CP)

5301‧‧‧User Equipment (UE)

5302‧‧‧User Equipment (UE)

5303‧‧‧User Equipment (UE)

5304‧‧‧User Equipment (UE)

5305‧‧‧User Equipment (UE)

5310‧‧‧Base Transceiver Station (BTS)

5311‧‧‧Base Transceiver Station (BTS)

5312‧‧‧Base Transceiver Station (BTS)

5313‧‧‧Base Transceiver Station (BTS)

5314‧‧‧Base Transceiver Station (BTS)

5320‧‧‧Controller (CTR)

5330‧‧‧Base Station Network (BSN)

5340‧‧‧Centralized Processor (CP)

5350‧‧‧External network

F ₁ ‧‧‧carrier frequency

Carrier frequency F ₂ ‧‧‧

F ₃ ‧‧‧carrier frequency

T ₁ ‧‧‧ slot

t ₁ ‧‧‧ data stream

T ₂ ‧‧‧ slot

t ₂ ‧‧‧ data stream

T ₃ ‧‧‧ slot

t _M ‧‧‧ data stream

A better understanding of the present invention can be obtained from the following detailed description, in which: FIG. 1 illustrates a primary DIDO cluster surrounded by adjacent DIDO clusters in one embodiment of the present invention.

2 illustrates a frequency division multiple access (FDMA) technique for use in an embodiment of the present invention.

Figure 3 illustrates a time division multiple access (TDMA) technique for use in an embodiment of the present invention.

Figure 4 illustrates different types of interference regions processed in an embodiment of the present invention.

Figure 5 illustrates a framework for use in an embodiment of the present invention.

Figure 6 illustrates a graph showing SER as a function of SNR, assuming SIR = 10 dB for the target user in the interference region.

Figure 7 illustrates a graph showing SER derived from two IDCI precoding techniques.

Figure 8 illustrates an exemplary scenario in which a target client autonomous DIDO cluster moves to an interference cluster.

Figure 9 illustrates signal to interference plus noise ratio (SINR) as a function of distance (D).

Figure 10 illustrates the symbol error rate (SER) performance for the three cases of 4-QAM modulation in a flat fading narrowband channel.

Figure 11 illustrates a method for IDCI precoding in accordance with an embodiment of the present invention.

Figure 12 illustrates the SINR variation as a function of the distance of the user terminal from the center of the primary DIDO cluster in one embodiment.

Figure 13 illustrates one embodiment in which a SER is derived for 4-QAM modulation.

Figure 14 illustrates an embodiment of the present invention in which a finite state machine implements a handover algorithm.

Figure 15 illustrates (depicts) one embodiment of a handover strategy in the presence of shadowing.

Figure 16 illustrates the hysteresis loop mechanism when switching between any two states in Figure 13.

Figure 17 illustrates one embodiment of a DIDO system with power control.

Figure 18 illustrates the SER vs. SNR in the case where four DIDO transmit antennas and four clients are assumed in different situations.

19 illustrates MPE power density as a function of distance from an RF radiation source for different transmission power values, in accordance with an embodiment of the present invention.

Figures 20a through 20b illustrate different distributions of low power and high power DIDO distributed antennas.

Figures 21a through 21b illustrate two power distributions corresponding to the configurations in Figures 20a and 20b, respectively.

Figures 22a through 22b illustrate the rate profiles for the two scenarios shown in Figures 20a and 20b, respectively.

Figure 23 illustrates one embodiment of a DIDO system with power control.

Figure 24 illustrates one embodiment of a method of repeating on all antenna groups in accordance with the cyclic scheduling principle for transmitting data.

Figure 25 illustrates a comparison of uncoded SER performance with power steering of antenna groups with conventional eigenmode selections in U.S. Patent No. 7,636,381.

26a- 26c illustrate three scenarios in which BD precoding dynamically adjusts precoding weights to account for different power levels on the wireless link between the DIDO antenna and the UE.

Figure 27 illustrates the amplitude of the low frequency selective channel (assuming β = 1) in the delay domain or instantaneous PDP (upper curve) and frequency domain (lower curve) of the DIDO 2x2 system.

Figure 28 illustrates an embodiment of a channel matrix frequency response for DIDO 2x2 with a single antenna per client.

Figure 29 illustrates an embodiment of a channel matrix frequency response for DIDO 2x2, where for a channel characterized by high frequency (e.g., where β = 0.1), each client has a single antenna.

Figure 30 illustrates an exemplary SER of different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM).

Figure 31 illustrates an embodiment of a method for implementing a Link Adaptation (LA) technique.

Figure 32 illustrates the SER performance of one embodiment of a Link Adaptation (LA) technique.

Figure 33 illustrates the entries of the matrix in equation (28) of the DIDO 2x2 system as a function of the OFDM carrier tone index in the case of N _FFT = 64 and L ₀ = 8.

Figure 34 illustrates the SER versus SNR for a transmit antenna and a variable number P for L ₀ = 8, M = N _t = 2.

Figure 35 illustrates the SER performance for one of the different embodiments the number and order of L DIDO interpolation method of embodiment 16 = _0.

Figure 36 illustrates an embodiment of a system that uses super clustering, DIDO clustering, and user clustering.

Figure 37 illustrates a system with user clusters in accordance with an embodiment of the present invention.

Figures 38a- b illustrate the link quality threshold for use in an embodiment of the present invention.

Figures 39 through 41 illustrate examples of link quality matrices for establishing user clusters.

Figure 42 illustrates an embodiment of the user side moving across different DIDO clusters.

43 to 46 illustrate the relationship between the resolution of the spherical array and its area A in an embodiment of the present invention.

Figure 47 illustrates the degree of freedom of a MIMO system in actual indoor and outdoor propagation scenarios.

Figure 48 illustrates the degrees of freedom in a DIDO system as a function of array diameter.

Figure 49 illustrates one embodiment of a plurality of centralized processors (CPs) and distributed nodes (DNs) including communications via wired or wireless connections.

50 illustrates the unlicensed CP DN and DN exchanging control information to reconfigure an embodiment has a frequency band used to close the license.

Figure 51 illustrates one embodiment in which the entire spectrum is allocated to a new service and control information is used by the CP to turn off all unlicensed DNs to avoid interfering with the licensed DN.

Figure 52 illustrates one embodiment of a cloud wireless system that includes multiple CPs, distributed nodes, and a network interconnecting the CPs to the DN.

53 to 59 illustrate adaptively reconfigure parameters for many users due to the Doppler effect compensating changes in the propagation environment or the user's action is (MU) multi-antenna system (MAS) embodiment.

Figure 60 illustrates a plurality of BTSs, some of which have good SNR and some of which have low Doppler relative to the UE.

Figure 61 illustrates one embodiment of a matrix containing the values of SNR and Doppler recorded by the CP for a plurality of BTS-UE links.

Figure 62 illustrates channel gain (or CSI) at different times in accordance with an embodiment of the present invention.

One solution to overcome many of the limitations of the prior art described above is one embodiment of a Decentralized Input Decentralized Output (DIDO) technique. The DIDO technology is described in the following patents and patent applications, the entireties of which are hereby incorporated by reference. This application is part of the continuation (CIP) of these patent applications. These patents and applications are sometimes referred to herein as "related patents and applications".

U.S. Application No. 13/232,996, filed on September 14, 2011, entitled "Systems And Methods To Exploit Areas of Coherence in Wirless Systems"

U.S. Application No. 13/233,006, filed on September 14, 2011, entitled "Systems and Methods for Planned Evoluation and Obsolescence of Multiuser Spectrum"

Application for "Systems And Methods To Coordinate Transmissions In Distributed Wireless Systems Via User Clustering" on November 1, 2010 US Application No. 12/917,257

US Application No. 12/802,988, filed on June 16, 2010, entitled "Interference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO) Communication Systems"

US Application No. 12/802,976, filed on June 16, 2010, entitled "System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements"

US Application No. 12/802,974, filed on June 16, 2010, entitled "System And Method For Managing Inter-Cluster Handoff Of Clients Which Traverse Multiple DIDO Clusters"

US Application No. 12/802,989, filed on June 16, 2010, entitled "System And Method For Managing Handoff Of A Client Between Different Distributed-Input-Distributed-Output (DIDO) Networks Based On Detected Velocity Of The Client"

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

US Application No. 12/802,975, filed on June 16, 2010, entitled "System And Method For Link adaptation In DIDO Multicarrier Systems"

US Application No. 12/802,938, filed on June 16, 2010, entitled "System And Method For DIDO Precoding Interpolation In Multicarrier Systems"

U.S. Application No. 12/630,627, filed on December 2, 2009, entitled "System and Method For Distributed Antenna Wireless Communications"

US Patent No. 7,599,420, entitled "System and Method for Distributed Input Distributed Output Wireless Communication", dated October 20, 2007, filed on August 20, 2007.

US Patent No. 7,633,994, entitled "System and Method for Distributed Input Distributed Output Wireless Communication", dated December 15, 2009, filed on August 15, 2007.

US Patent No. 7,636,381, entitled "System and Method for Distributed Input Distributed Output Wireless Communication", dated August 20, 2007, filed on August 20, 2007.

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

US Application No. 11/256,478, filed on October 21, 2005, entitled "System and Method For Spatial-Multiplexed Tropospheric Scatter Communications"

U.S. Patent No. 7,418,053, entitled "System and Method for Distributed Input Distributed Output Wireless", dated August 28, 2008, filed on July 30, 2004.

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

In order to reduce the size and complexity of the present patent application, the disclosure of some of the related patents and applications is not explicitly set forth below. For a complete and detailed description of the disclosure, please refer to the relevant patents and applications.

Note that the following section I (from the disclosure of the relevant application No. 12/802,988) uses its own reference to prior art references and prior assignments to the assignee of the present application. The endnote collection of the case. These endnotes refer to the series at the end of Chapter I (just before the header of Chapter II). The numerical representations used in Section II may overlap with their numerical representations used in Section I, even if such numbers indicate the identification of different references (listed at the end of Section II). Thus, a reference identified by the digital representation can be identified in a section using a particular digital representation.

I. Revelation from related application No. 12/802,988 1. Method of removing inter-cluster interference

Described below are wireless radio frequency (RF) communication systems and methods that use a plurality of distributed transmission antennas to establish a location in space with zero RF energy. When M transmit antennas are used, up to (M-1) zero RF energy points can be established in predefined locations. In one embodiment of the invention, the zero RF energy point is a wireless device and the transmit antenna is aware of channel state information (CSI) between the transmitter and the receiver. In an embodiment, the CSI is calculated at the receiver and fed back to the transmitter. In another embodiment, it is assumed that CSI is calculated at the transmitter via training from the receiver using channel reciprocity. The transmitter can utilize the CSI to determine the interference signal to be transmitted simultaneously. In an embodiment, block diagonalization (BD) precoding is used at the transmit antenna to produce a zero RF energy point.

The systems and methods described herein differ from the conventional receive/transmit beamforming techniques described above. In effect, receive beamforming calculates weights to suppress interference on the receiving side (via zero control), while some embodiments of the invention described herein apply weights on the transmission side to establish one of the "zero RF energy" in space. Or interference patterns in multiple locations. Unlike conventional transmit beamforming or BD precoding, which are designed to maximize signal quality (or SINR) or downlink throughput, respectively, the systems and methods described herein are minimized under certain conditions. And/or signal quality from a particular transmitter, thereby establishing a zero RF energy point at the customer premises device (sometimes referred to herein as a "user"). In addition, in the context of a Decentralized Input Decentralized Output (DIDO) system (described in our related patents and applications), transmission antennas dispersed in space can be used to establish multiple A higher degree of freedom (ie, higher channel space diversity) for zero RF energy points and/or maximum SINR to different users. For example, up to (M-1) RF energy points can be established by M transmit antennas. In contrast, actual beamforming or BD multi-user systems are typically designed to have dense antennas on the transmission side, limiting the number of simultaneous users that can be servoed over the wireless link for any number M of transmission antennas.

Consider a system with M transmit antennas and K users, where K < M. We assume that the transmitter knows the CSI between the M transmit antennas and the K users. C ^K×M ). For simplicity, it is assumed that each user is equipped with a single antenna, but the same method can be extended to have multiple receive antennas per user. Calculate precoding weights for zero RF energy at the location of K users (w C ^{M × 1} ) to satisfy the following conditions Hw = 0 ^{K × 1}

Where 0 ^K×1 is the vector with all zero inputs and H is the channel vector from the M transmit antennas to K users (h _k The channel matrix obtained by combining C ^1×M ) is as follows

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

The transmit antenna uses the weight vector defined above to transmit RF energy while establishing K zero RF energy points at the location of the K users such that the signal received at the kth user is given by r _k = h _k ws _k +n _k =0+n _k

Where n _k C ^1×1 is the additive white Gaussian noise (AWGN) at the kth user. In an embodiment, the singular value decomposition (SVD) of the channel matrix H is calculated and the precoding weight w is defined as the right singular vector corresponding to the zero subspace of H (identified by the zero singular value).

In another embodiment, the wireless system is a DIDO system and zero RF energy points are established to pre-empt interference with the UEs between different DIDO coverage areas. In US Application Serial No. 12/630,627, a DIDO system is described which includes:

̇IDO client

̇IDO distributed antenna

̇IDO Base Transceiver Station (BTS)

̇IDO base station network (BSN)

Each BTS is connected via a BSN to a plurality of decentralized antennas that provide service to a given coverage area known as a DIDO cluster. In this patent application, we describe a system and method for removing interference between adjacent DIDO clusters. As described in FIG. 1, I assumed that the main DIDO cluster UE hosted by the interference from neighboring clusters (or target client) effects (i.e., the servo system of a multi-user DIDO user device).

In an embodiment, adjacent clusters operate at different frequencies in accordance with a frequency division multiple access (FDMA) technique, similar to conventional cellular systems. For example, in the case where a frequency reuse factor of 3, as illustrated in FIG. 2 every three DIDO cluster reuse the same carrier frequency. In Figure 2, different carrier frequencies are identified as F _1, F ₂ and F _3. While this embodiment can be used in some implementations, this solution creates a loss of spectral efficiency because the available spectrum is divided into multiple sub-bands and only a subset of the DIDO clusters operate in the same sub-band. In addition, complex cell planning is required to correlate different DIDO clusters with different frequencies, thereby preventing interference. Similar to prior art cellular systems, this cellular plan requires specific placement of the antenna and limits transmission power in order to avoid interference between clusters of the same frequency.

In another embodiment, adjacent clusters operate in the same frequency band but at different time slots according to time division multiple access (TDMA) techniques. For example, as described in FIG. 3, the groove is allowed only when T _1, T ₂ and T ₃ in the DIDO transmission, as described for a particular cluster. The time slots can be equally assigned to different clusters such that different clusters are scheduled according to the cycle principle. Different priorities are assigned to different clusters if different clusters are characterized by different data rate requirements (i.e., clusters in a crowded urban environment are compared to clusters in a rural area with a smaller number of clients per coverage area) So that more time slots are assigned to the cluster with larger data rate requirements. While TDMA as described above can be used in one embodiment of the present invention, the TDMA method can require time synchronization across different clusters and can result in lower spectral efficiency because interference clusters cannot use the same frequency at the same time.

In one embodiment, all adjacent clusters are simultaneously transmitted in the same frequency band and use spatial processing across the cluster to avoid interference. In this embodiment, the multi-cluster DIDO system: (i) uses conventional DIDO precoding within the main cluster to transmit both non-interfering data streams to multiple clients in the same frequency band (such as in related patents and applications) Described, including 7,599,420; 7,633,994; 7,636,381; and application No. 12/143,503); (ii) using DIDO precoding with interference cancellation in adjacent clusters to establish zero radio frequency (RF) at the location of the target client The energy point is to avoid interference with the UE located in the interference region 410 in FIG . If the target client is in an interference region 410, it will receive the sum of the RF containing the data stream from the primary cluster 411 and the zero RF energy from the interference clusters 412 through 413, which will simply be from the primary cluster. The RF of the data stream. Therefore, adjacent clusters can utilize the same frequency at the same time and the target UE in the interference region is not subject to interference.

In practical systems, the effectiveness of DIDO precoding can be affected by different factors, such as: channel estimation error or Doppler effect (obsolete channel state information at DIDO distributed antennas); mutual in multi-carrier DIDO systems Modulation distortion (IMD); time or frequency offset. Due to these effects, achieving zero RF energy points can be impractical. However, as long as the RF energy from the interference cluster at the target UE is negligible compared to the RF energy from the main cluster, the link performance at the target UE is unaffected by interference. For example, assume that the UE requires a 20 dB signal to noise ratio (SNR) to demodulate the variable 4-QAM cluster using Forward Error Correction (FEC) encoding to achieve a target bit error rate (BER) of ^10-6 . If the RF energy received by the self-interference cluster at the target UE is 20 dB lower than the RF energy received by the autonomous cluster, the interference is negligible and the UE can successfully demodulate the variable data within the predefined BER target. Thus, the term "zero RF energy" as used herein does not necessarily mean that the RF energy from the interfering RF signal is zero. Specifically, it means that the RF energy is sufficiently low relative to the RF energy of the desired RF signal such that the desired RF signal can be received at the receiver. Moreover, although specific thresholds for interfering RF energy with respect to desired RF energy are described, the underlying principles of the invention are not limited to any particular threshold.

As shown the presence of different types of interference in region 4 in FIG. 410. For example, the "Type A" area (as indicated by the letter "A" in Figure 4) is only affected by interference from an adjacent cluster, and the "Type B" area (as indicated by the letter "B") Describe interference from two or more adjacent clusters.

Figure 5 depicts a framework for use in an embodiment of the invention. The dot represents the DIDO distributed antenna, the cross refers to the DIDO client and the arrow indicates the direction of propagation of the RF energy. The pre-coded data signal is transmitted to the user terminal MC 501 of the cluster in the DIDO antenna in the main cluster. Similarly, the DIDO antenna in the interference cluster is servoed to the client IC 502 in the cluster via conventional DIDO precoding. The green cross 503 represents the target user terminal TC 503 in the interference area. The DIDO antenna in the main cluster 511 transmits the precoded data signal to the target client (black arrow) via conventional DIDO precoding. The DIDO antenna in the interference cluster 512 uses precoding to establish zero RF energy in the direction of the target client 503 (green arrow).

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

Where k =1,..., K , where K is the number of clients in the interference regions 8010A, 8010B, U is the number of clients in the main DIDO cluster, C is the number of interfering DIDO clusters 412 to 413 and I _c is The number of users in the interference cluster c . In addition, r _k C ^{N × M} is a vector containing the received data stream at the user terminal k , assuming that there are M transmission DIDO antennas and N receiving antennas at the client device; s _k C ^{N ×1} is the vector of the transmission data stream to the client k of the main DIDO cluster; s _u C ^{N ×1} is the vector of the transmission data stream to the user terminal u in the main DIDO cluster; s _{c , i} C ^{N × 1} is to the c-th cluster of interfering users DIDO vector i of data stream transmission terminal; n _k C ^{N ×1} is the vector of the additive white Gaussian noise (AWGN) at the N receiving antennas of the user terminal k ; H _k C ^{N × M is} the DIDO channel matrix from the M transmit DIDO antennas to the N receive antennas at the user k of the main DIDO cluster; H _{c , k} C ^{N × M} is the DIDO channel matrix from the M transmit DIDO antennas to the N receive antennas at the user k of the c -interfering DIDO cluster; W _k C ^{N × M} is a matrix of DIDO precoding weights to the client k of the main DIDO cluster; W _u C ^{N × M} is a matrix of DIDO precoding weights to the user u of the main DIDO cluster; W _{c , i} C ^{N × M} is to the c-th cluster of interfering users terminus of the heavy DIDO DIDO precoding weight matrix of a i.

To simplify the notation and without loss of generality, we assume that all clients are equipped with N receive antennas and there are M DIDO distributed antennas in each DIDO cluster, where M ( N . U ) and M ( N . I _c ), c =1,..., C . If M is greater than the total number of receive antennas in the cluster, the additional transmit antenna is used to pre-empt interference with the target user in the interference zone or through related patents and applications (including 7,599,420; 7,633,994; 7,636,381 and 12/143,503) The diversity scheme described in No.) improves the link robustness to the UE in the same cluster.

The DIDO precoding weights are calculated to pre-empt inter-user interference within the same DIDO cluster. For example, block diagonalization (BD) precoding as described in related patents and applications (including 7,599,420; 7,633,994; 7,636,381 and applications 12/143,503 and [7]) can be used to remove inter-user interference. So that the following conditions are met in the main cluster

The precoding weight matrix in the adjacent DIDO cluster is designed such that the following conditions are met

To calculate the precoding matrix W _{c, i,} the UE estimates from M transmission antennas to a cluster of interfering users end I _c and k to the interference region of a downlink channel, and interference by the cluster of DIDO BTS Calculate the precoding matrix. If the method for calculating interference BD cluster of precoding matrices, then build the following matrix to calculate the effective channel adjacent to the i th user's right side of the cluster weight

among them Channel matrix for self-interference cluster c The obtained matrix in which the column corresponding to the i- th client is removed.

Substituting conditions (2) and (3) into (1), we obtain the received data stream for the target client k , where the intra-cluster and inter-cluster interference is removed r _k =H _k W _k s _k + n _k . (5)

The precoding weight W _{c , i} in (1) calculated in the adjacent cluster is designed to transmit the precoded data stream to all clients in the cluster, while pre-eliminating the target user in the interference region interference. The target client receives precoded data only from its primary cluster. In various embodiments, both the autonomous cluster and the adjacent cluster stream the same data stream to the target client to obtain a diversity gain. In this case, the signal model in (5) is expressed as

Wherein W _{c, k} of the c-th cluster from the transmitter to DIDO DIDO precoding matrix interference region of the target UE k. Note that the method in (6) requires time synchronization across adjacent clusters, which can be complicated in large systems, but nevertheless, if the diversity gain benefit proves that the implementation cost is justified, then this is very feasible.

We begin by evaluating the performance of the proposed method based on the symbol error rate (SER) as a function of the signal-to-noise ratio (SNR). Without loss of generality, we assume that each client has a single antenna and defines the following signal model and (1) re-formulated as

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

Figure 6 shows the SER as a function of SNR, assuming SIR = 10 dB for the target UE in the interference region. Without loss of generality, we measured the SER of 4-QAM and 16-QAM without forward error correction (FEC) coding. For unencoded systems, we fixed the target SER to 1%. This target corresponds to a different value of SNR depending on the modulation order (ie, SNR = 20 dB for 4-QAM and SNR = 28 dB for 16-QAM). When using FEC encoding, due to the coding gain, a lower SER target can be satisfied for the same SNR value. I consider the case where each cluster has two DIDO antennas and two clusters (each equipped with a single antenna) and two clusters (one main cluster and one interference cluster). One of the users in the main cluster is located in the interference area. We assume flat fading narrowband channels, but the following results can be extended to frequency selective multi-carrier (OFDM) systems where each subcarrier experiences flat fading. We consider two scenarios: (i) a case with DIDO inter-cluster interference (IDCI), in which the precoding weights w _{c , i} are calculated without considering the target UE in the interference region; and (ii) another situation Where IDCI is removed by calculating the weights w _{c , i} to eliminate the IDCI for the target client. We observed that SER is high and higher than the predefined target in the presence of IDCI. By IDCI precoding at adjacent clusters, interference to the target UE is removed and the SER target is reached for SNR > 20 dB.

The result in Figure 6 assumes IDCI precoding as in (5). If the IDCI precoding at the adjacent cluster is also used to precode the data stream to the target UE in the interference region as in (6), an additional diversity gain is obtained. Figure 7 compares the SER derived from the two techniques: (i) "Method 1" using IDCI precoding in (5); (ii) "Method 2" using IDCI precoding in (6), where adjacent The cluster also transmits the precoded data stream to the target client. Compared to conventional IDCI precoding, Method 2 produces a ~3 dB gain due to the additional array gain provided by the DIDO antennas used to transmit the precoded data stream to the target UE in the adjacent cluster. More generally, the array gain of method 2 relative to method 1 is proportional to 10*log10(C+1), where C is the number of adjacent clusters and the factor "1" refers to the main cluster.

Next, we evaluate the performance of the above method as the target client is positioned relative to the location of the interference region. We consider a simple case, where the target UE 8401 autonomously interference DIDO cluster 802 to cluster 803 moves, as depicted in FIG. We assume that all DIDO antennas 812 within primary cluster 802 use BD precoding to eliminate intra-cluster interference to satisfy condition (2). We assume a single interfering DIDO cluster, a single receiver antenna at the client device 801 and all DIDO antennas in the autonomous cluster or interference cluster (ie, the DIDO antenna placed in a circular surround user) to the user's equal path loss. We use a simplified path loss model with a path loss index of 4 (as in a typical urban environment) [11].

The analysis below is based on the extension (7) to consider the following simplified signal model for path loss.

The signal-to-interference ratio (SIR) is derived as SIR=((1-D)/D) ⁴ . In modeled IDCI, we consider three scenarios: i) ideal conditions without IDCI; ii) pre-dissolving IDCI via BD precoding in the interference cluster to satisfy condition (3); iii) having pre-cancelled by adjacent clusters IDCI.

Figure 9 shows the signal-to-interference plus noise ratio (SINR) as a function of D (i.e., when the target client autonomous cluster 802 moves toward the DIDO antenna 813 in the interference cluster 8403). The SINR is derived from the signal model in (8) and is derived as the ratio of signal power to interference plus noise power. We assume that for D = D ₀ , D ₀ = 0.1 and SNR = 50 dB. In the absence of IDCI, the radio link performance is only affected by noise and the SINR is reduced due to path loss. In the presence of IDCI (i.e., without IDCI precoding), interference from DIDO antennas in adjacent clusters helps to reduce SINR.

Figure 10 shows the symbol error rate (SER) performance for the above three scenarios for 4-QAM modulation in a flat fading narrowband channel. These results correspond to the SINR SER 9 of FIG. We assume that the 1% SER threshold for uncoded systems (ie, no FEC) corresponds to the SINR threshold SINR _T = 20 dB in Figure 9 . The SINR threshold depends on the modulation order used for data transmission. The higher modulation order is typically characterized by a higher SINR _T to achieve the same target error rate. With FEC, due to the coding gain, a lower target SER can be achieved for the same SINR value. In the absence of pre-coded IDCI, the target SER is only achieved within the range of D < 0.25. By the IDCI precoding at the adjacent cluster, the range extension of the target SER is satisfied to reach D < 0.6. Outside this range, the SINR increases due to path loss and the SER target is not met.

An embodiment of a method for IDCI precoding is shown in Figure 11 and consists of the following steps:

̇ SIR estimate 1101 : The UE estimates the signal power from the primary DIDO cluster (ie, based on the received precoding data) and the interference plus noise signal power from the adjacent DIDO cluster. In a single carrier DIDO system, the frame structure can be designed with a short silence period. For example, the silence period may be defined between training for channel estimation and precoding data transmission during channel state information (CSI) feedback. In one embodiment, the interference plus noise signal power from adjacent clusters is measured by the DIDO antenna in the autonomous cluster during the silence period. In an actual DIDO multi-carrier (OFDM) system, zero carrier frequency is typically invoked to prevent direct current (DC) offset and attenuation at the band edges due to filtering on the transmit and receive sides. In another embodiment using a multi-carrier system, the interference plus noise signal power is estimated from the zero carrier tone. A correction factor can be used to compensate for transmission/reception filter attenuation at the edge of the band. Once the signal plus interference and noise power (P _S ) from the main cluster and the interference plus noise power (P _IN ) from the adjacent cluster are estimated, the UE calculates the SINR as

Alternatively, the SINR estimate is derived from a Received Signal Strength Indication (RSSI) for measuring radio signal power in a typical wireless communication system.

We have observed that the measure in (9) cannot distinguish between noise and interference power levels. For example, being masked in a non-interfering environment (ie, attenuating all DIDOs from the main cluster) The affected end of the distributed antenna's signal power can estimate the low SINR even if it is not affected by inter-cluster interference.

A more reliable measure for the proposed method is SIR, which is calculated as

Where P _N is the noise power. In an actual multi-carrier OFDM system, the noise power P _N in (10) is estimated from the zero-carrier frequency modulation, assuming that all DIDO antennas from the primary cluster and the adjacent cluster use the same set of zero-carrier tones. The interference plus noise power (P _IN ) is estimated based on the silence period as mentioned above. Finally, the signal plus interference and noise power (P _S ) are derived based on the data carrier frequency. Based on these estimates, the UE calculates the SIR in (10).

频道Channel estimates 1102 to 1103 at adjacent clusters : If it is determined at 1102 in Figure 11, the estimated SIR in (10) is below the predefined threshold (SIR _T ), then the user begins to listen to the phase Training signals for adjacent clusters. Note that SIR _T depends on the modulation used for data transmission and the FEC coding scheme (MCS). Different SIR targets are defined depending on the MCS of the client. When DIDO distributed antennas from different clusters are time synchronized (ie, locked to the same Pulse Per Second (PPS) time reference), at 1103 the UE utilizes a training sequence to deliver its channel estimate to the adjacent cluster. The DIDO antenna in the middle. The training sequence for channel estimation in adjacent clusters is designed to be orthogonal to the training from the main cluster. Alternatively, orthogonal sequences (with good cross-correlation properties) are used for time synchronization in different DIDO clusters when DIDO antennas in different clusters are not time synchronized. Once the client locks to the time/frequency reference of the adjacent cluster, channel estimation is performed at 1103.

̇ IDCI Precoding 1104 : Once the channel estimate is available at the DIDO BTS in the adjacent cluster, the IDCI precoding is calculated to satisfy the condition in (3). The DIDO antennas in the adjacent cluster transmit only the precoded data stream to the UEs in its cluster, while prematurely eliminating interference to the UE in the interference region 410 of FIG . It has been observed that if the UE is located in the Type B interference region 410 of Figure 4 , the interference to the UE is generated by multiple clusters and the IDCI precoding is performed simultaneously by all adjacent clusters.

Method for handing over

In the following, we describe different handover methods for clients that move across DIDO clusters, which are populated by decentralized antennas located in separate areas or providing different kinds of services (ie, low or high mobility services) .

a. Handover between adjacent DIDO clusters

In an embodiment, an IDCI precoder to remove the inter-cluster interference described above is used as a baseline for the handover method in the DIDO system. The conventional handover in a cellular system is assumed to be that the UE seamlessly switches across cells that are served by different base stations. In a DIDO system, handoff allows the client to move from one cluster to another without losing connectivity.

To illustrate one embodiment of the DIDO system's handover strategy, we again consider the example of FIG. 8 having only two clusters 802 and 803. When the client 801 autonomous cluster (C1) 802 moves to the adjacent cluster (C2) 803, one embodiment of the handover method dynamically calculates the signal quality in the different clusters and selects a cluster that produces the lowest error rate performance for the UE.

Figure 12 shows the SINR variation as a function of the distance of the user terminal from the center of cluster C1. For 4-QAM modulation without FEC coding, we consider the target SINR = 20 dB. When both C1 and C2 use DIDO precoding without interference cancellation, the line identified by the circle indicates the SINR of the target UE served by the DIDO antenna in C1. The SINR is reduced as a function of D due to path loss and interference from adjacent clusters. When IDCI precoding is implemented at adjacent clusters, the SINR loss is only due to path loss (as shown by the line with triangles) because the interference is completely removed. Symmetric behavior is experienced when serving the client from an adjacent cluster. One embodiment of the handover strategy is defined such that when the client moves from C1 to C2, the algorithm switches between different DIDO schemes to maintain the SINR above the predefined target.

From the curve in Fig. 12 , we derive the SER for 4-QAM modulation in Fig. 13 . We have observed that by switching between different precoding strategies, the SER is maintained within a predefined target.

One of the implementation strategies of the handover strategy is as follows.

̇ C1-DIDO and C2-DIDO precoding : When the UE is located in C1 and away from the interference area, the clusters C1 and C2 are independently operated by the conventional DIDO precoding.

̇ C1-DIDO and C2-IDCI precoding : When the UE moves towards the interference area, its SIR or SINR is degraded. When the target SINR _T1 is reached, the target UE begins to estimate the channel from all DIDO antennas in C2 and provides a BTS to C2 to C2. The BTS in C2 calculates IDCI precoding and transmits to all clients in C2 while preventing interference to the target UE. 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 precoding : When the user moves towards C2, its SIR or SINR continues to decrease until it reaches a target again. At this point, the client decides to switch to the adjacent cluster. In this case, C1 begins to use CSI from the target UE to establish zero interference towards its direction by IDCI precoding, while adjacent clusters use CSI for conventional DIDO precoding. In an embodiment, when the SIR estimate approaches the target, clusters C1 and C2 alternately attempt both the DIDO precoding scheme and the IDCI precoding scheme to allow the UE to estimate the SIR in both conditions. The client then selects the best solution to maximize the specific error rate performance metric. When this method is applied, the intersection for the handover strategy appears at the intersection of the curve with triangles and diamonds in Figure 12 . An embodiment uses the modified IDCI precoding method described in (6), wherein adjacent clusters also transmit precoded data streams to the target UE to provide array gain. By this method, the handover strategy is simplified because the UE does not need to estimate the SINR of the two strategies at the intersection.

̇ C1-DIDO and C2-DIDO precoding : When the UE moves out of the interference area towards C2, the main cluster C1 stops pre-dissolving the interference towards the target user via IDCI precoding and switches for all the clients that remain in C1. Go back to the traditional DIDO precoding. This final intersection in our handoff strategy can be used to avoid unnecessary CSI feedback from the target client to C1, thereby reducing the extra burden on the feedback channel. In an embodiment, a second target SINR _{T2 is} defined. When the SINR (or SIR) increases above this target, the policy is switched to C1-DIDO and C2-DIDO. In an embodiment, cluster C1 continues to alternate between DIDO precoding and IDCI precoding to allow the UE to estimate the SINR. The UE then selects the method for C1 that is closer to the target SINR _T1 from above.

The method described above calculates the SINR or SIR estimates for different scenarios on the fly and uses them to select an optimization scheme. In one embodiment, the handover algorithm is designed based on the finite state machine illustrated in FIG . When the SINR or SIR falls below or above the predefined threshold illustrated in Figure 12 , the UE remembers its current state and switches to the next state. As discussed above, in state 1201, clusters C1 and C2 are independently operated by conventional DIDO precoding and the client is servoed by cluster C1; in state 1202, the client is servoed by cluster C1, C2 The BTS calculates the IDCI precoding and the cluster C1 operates using the conventional DIDO precoding; in state 1203, the client is served by cluster C2, the BTS in C1 calculates IDCI precoding and the cluster C2 uses conventional DIDO precoding. To operate; and in state 1204, the client is served by cluster C2, and clusters C1 and C2 are independently operated by conventional DIDO precoding.

In the presence of shadowing effects, signal quality, SIR may be as shown in Figure 15 fluctuate around the threshold, thereby causing repeated switching between the successive states of FIG. 14. The state of repeated changes is an undesirable effect because it results in a significant additional burden on the control channel between the UE and the BTS to allow for switching between transmission schemes. Figure 15 depicts an example of a handover strategy in the presence of shadowing. In one embodiment, the masking factor is modeled according to a lognormal distribution with a variance of 3 [3]. In the following, we define some methods to prevent repeated switching effects during DIDO handover.

One embodiment of the present invention uses a hysteresis loop to resolve state switching effects. For example, when switching between "C1-DIDO, C2-IDCI" 9302 and "C1-IDCI, C2-DIDO" 9303 state (or vice versa) in FIG. 14 , the threshold SINR _T1 can be adjusted to be in the range. the A _1. This method avoids repeated switching between states when the signal quality oscillates around SINR _T1 . For example, Figure 16 shows the hysteresis loop mechanism when switching between any two states in Figure 14 . In order to switch from state B to state A, the SIR must be greater than (SIR _T1 + A ₁ /2), but in order to switch back from B to B, the SIR must fall below (SIR _{T1 -} A ₁ /2).

In various embodiments, the threshold SINR _T2 is adjusted to avoid repeated switching between the first state and the second state (or the third state and the fourth state) of the finite state machine of FIG . For example, the range of values A ₂ can be defined such that video channel conditions and shadowing effects select a threshold SINR _T2 within the range.

In an embodiment, the SINR threshold is dynamically adjusted within the range [SINR _T2 , SINR _T2 + A ₂ ] depending on the variance of the expected shadow on the wireless link. When the UE moves from its current cluster to an adjacent cluster, the variance of the lognormal distribution can be estimated from the variance of the received signal strength (or RSSI).

The above method assumes that the client triggers the handover policy. In an embodiment, it is assumed that communication across multiple BTSs is enabled, and the handover decision to the DIDO BTS is postponed.

For the sake of simplicity, the above method is derived assuming no FEC coding and 4-QAM. More generally, the SINR or SIR threshold is derived for different modulation coding schemes (MCS) and the handover strategy is designed in conjunction with link adaptation (see, for example, US Pat. No. 7,636,381) to optimize into the interference region. The downlink data rate for each client.

b. Handover between Low Doppler and Gaudub DIDO network

The DIDO system uses a closed loop transmission scheme to precode the data stream on the downlink channel. Closed loop schemes are inherently constrained by latency on the feedback channel. In an actual DIDO system, when CSI and baseband precoding data are delivered from the BTS to the decentralized antenna, the computation time can be reduced by the transceiver with high processing power and most of the latency is expected to be introduced by the DIDO BSN. BSN can include a variety of network technologies, including (but not limited to) Subscriber Line (DSL), cable modem, fiber ring, T1 line, fiber-optic coaxial hybrid (HFC) network, and/or fixed wireless (eg, WiFi). Dedicated fiber typically has very large bandwidth and low latency (which may be less than a few milliseconds in a local area), but it is not as widely deployed as DSL and cable modems. Today, DSL and cable modem connections in the United States typically have a last-mile latency of between 10ms and 25ms, but are deployed very widely.

The maximum latency on the BSN determines the maximum Doppler frequency that can be tolerated on the DIDO wireless link without the performance degradation of the DIDO precoding. For example, in [1], we show that at a carrier frequency of 400 MHz, a network with a latency of about 10 milliseconds (ie, DSL) can tolerate a user terminal speed of up to 8 mph (running speed) with 1 The millisecond latency network (ie, fiber optic ring) can support speeds up to 70 mph (ie, highway traffic).

We define two or more DIDO subnets depending on the maximum Doppler frequency that can be tolerated on the BSN. For example, a BSN with a high latency DSL connection between a DIDO BTS and a decentralized antenna can deliver only low mobility or fixed wireless services (ie, low Doppler networks), while low latency fiber optic rings The low latency BSN can tolerate high mobility (ie, high Doppler networks). I have observed that most broadband users do not move when they use broadband, and further that most people are less likely to be located near areas where many high-speed objects move (for example, near highways) because such locations are often less than ideal. Residential or office location. However, there are broadband users who will use broadband at high speeds (eg, when in a car traveling on a highway) or will be in the vicinity of a high speed object (eg, in a store located near a highway). To handle the Doppler scenario of these two different users, in one embodiment, the low Doppler DIDO network route is spread over a wide area with relatively low power (ie, for indoor or rooftop installations, 1W to 100W) typically consists of a larger number of DIDO antennas, while a high Doppler network route has a generally lower number of DIDO antennas with high power transmission (ie, 100W for roof or tower installation). Low Doppler DIDO Network servos are typically performed by a larger number of low Doppler users and can be implemented at generally lower connectivity costs using inexpensive high latency broadband connections, such as DSL and cable modems. High Doppler DIDO network servos are typically a smaller number of high Doppler users and can be executed at generally higher connectivity costs using more expensive low latency broadband connections, such as fiber optics.

To avoid interference across different types of DIDO networks (eg, Low Doppler and Gaudigh), different multiple access techniques can be used, such as Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA). ) or code division multiple access (CDMA).

In the following, we propose a method for assigning a client to a different type of DIDO network and allowing a handover between them. Network selection is based on the type of mobility of each client. According to the following equation, the velocity ( v ) of the user end is proportional to the maximum Doppler shift [6].

Where f _d is the maximum Doppler shift, λ is the wavelength corresponding to the carrier frequency and θ is the angle between the vector indicating the direction of the transmitter-user end and the velocity vector.

In an embodiment, the Bucher frequency shift for each user terminal is calculated via a blind estimation technique. For example, similar to the Doppler radar system, the Doppler shift can be estimated by transmitting RF energy to the user and analyzing the reflected signal.

In another embodiment, one or more DIDO antennas transmit training signals to the client. Based on their training signals, the UE estimates the Doppler shift using techniques such as counting the zero crossing rate of the channel gain or performing spectral analysis. We have observed that for the fixed velocity v and the trajectory of the user end, the angular velocity v sin θ in (11) can be determined by the relative distance of the user terminal from each DIDO antenna. For example, a DIDO antenna near a mobile client produces a larger angular velocity and a Doppler shift than a distant antenna. In one embodiment, the Doppler velocity is estimated from a plurality of DIDO antennas at different distances from the user end, and the average, weighted average or standard deviation is used as an indicator of the user's mobility. Based on the estimated Doppler indicator, the DIDO BTS decides whether to assign the client to the low or high Doppler network.

The Doppler indicator is periodically monitored for all clients and sent back to the BTS. When one or more clients change their Doppler speed (ie, the user is walking or sitting on the bus against the client), their clients are dynamically reassigned to allow for different levels of mobility. DIDO network.

Although the low-speed client's Doppler can be affected by high-speed objects (for example, near highways), the Doppler is usually much smaller than the user's Doppler in motion. Thus, in one embodiment, the speed of the client is estimated (e.g., by using a method such as using GPS to monitor the location of the client), and if the speed is low, the client is assigned to a low Doppler network. And if the speed is high, the client is assigned to a high Doppler network.

Method for power control and antenna grouping

It depicts a block diagram of a power control system having DIDO in FIG 17. First, one or more data streams ( s _k ) of each client (1, . . . , U ) are multiplied by the weights generated by the DIDO precoding unit. The precoded data stream is multiplied by a power scaling factor calculated by the power control unit based on input channel quality information (CQI). The CQI is derived from the UE to the DIDO BTS or assumes uplink-downlink channel reciprocity and is derived from the uplink channel. Different clients of U pre-encoded stream, and then combined into a multi-data stream of M (t _m), a data stream, for each of the M transmission antennas. Finally, the stream is sent to t _m digital / analog converter (DAC) unit, a radio frequency (RF) unit, a power amplifier (PA) unit and finally to an antenna.

The power control unit measures the CQI for all clients. In an embodiment, the CQI is an average SNR or RSSI. Depending on path loss or shadowing, CQI varies for different clients. Our power control method adjusts the transmission power scaling factor P _k for different clients and multiplies it by the data stream generated for precoding for different clients. Note that one or more data streams can be generated for each client, depending on the number of receive antennas at the subscriber end.

In order to evaluate the effectiveness of the proposed method, we define the following signal model including path loss and power control parameters based on (5).

Where k =1,..., U , U is the number of clients, SNR= P _o / N _o , where P _o is the average transmission power, N _o is the noise power and α _k is the path loss/shadow coefficient. To model path loss/shadowing, we use the following simplified model

Where a = 4 is the path loss index and we assume that the path loss increases with the client index (ie, the user is located at an increasing distance from the DIDO antenna).

Figure 18 shows the SER vs. SNR for the case of four DIDO transmit antennas and four clients in different scenarios. The ideal situation assumes that all clients have the same path loss (i.e., a = 0), thereby generating P _k =1 for all clients. A curve with a square refers to a situation where the user end has different path loss coefficients and no power control. The curve with points is derived from the same situation (with path loss) in which the power control coefficients are chosen such that P _k = 1 / α _k . With the power control method, more power is allocated to the data stream intended to the user who experiences higher path loss/shadow, resulting in a 9 dB SNR gain (for this particular case) compared to the condition without power control.

The Federal Communications Commission (FCC) (and other international regulatory agencies) define constraints on the maximum power that can be transmitted from wireless devices to limit human exposure to electromagnetic (EM) radiation. There are two types of restrictions [2]: i) "occupational/controlled" restrictions in which the source of the RF is fully known via fences, warnings or markings; ii) "general population/uncontrolled" restrictions, where there is no control over exposure .

Different emission levels are defined for different types of wireless devices. In general, DIDO distributed antennas for indoor/outdoor applications meet the requirements of the FCC's "Action" device type, which is defined as [2]: "Designed to not be used in a fixed position, usually in a radiated structure Used by users or nearby people at a distance of 20 cm or more Transmission device".

The EM emission of the "action" device is measured in terms of Maximum Allowable Exposure (MPE) (expressed in mW/cm ² ). Figure 19 shows the MPE power density as a function of distance from the RF radiation source for different values of transmission power at a carrier frequency of 700 MHz. The maximum allowable transmission power is 1 W to meet the FCC "uncontrolled" limit of FCC, which is typically operated 20 cm away from the human body.

Less restrictive power emission constraints are defined for transmitters mounted on roofs or buildings that are remote from the "general population." For these "roof transmitters", the FCC defines a looser emission limit of 1000 W based on effective radiated power (ERP) measurements.

Based on the above FCC constraints, in one embodiment, we define two types of DIDO distributed antennas for the actual system:

̇ Low Power (LP) Transmitter: Anywhere at any height (ie indoors or outdoors) with 1W maximum transmit power and 5Mbps consumer-grade broadband (eg DSL, Cable Data, Fiber to the Home (FTTH) ) Reload connectivity.

̇High Power (HP) Transmitter: An antenna mounted on a roof or building at a height of approximately 10 meters with 100W transmission power and commercial grade broadband (eg, fiber optic ring) reload (with throughput available on DIDO wireless links) Compared to the amount, there is actually an "infinite" data rate).

Note that an LP transmitter with DSL or cable modem connectivity is a good candidate for a low Doppler DIDO network (as described in the previous section) because its clients are mostly fixed or have low mobility. HP transmitters with commercial fiber connectivity allow for higher client mobility and can be used in high Doppler DIDO networks.

In order to get an actual visual impression of the performance of DIDO systems with different types of LP/HP transmitters, we consider the actual situation of DIDO antenna installation in downtown Palo Alto (CA). Figure 20a shows the random distribution of N _LP = 100 low power DIDO distributed antennas in Palo Alto. In Figure 20b , 50 LP antennas are replaced with N _HP = 50 high power transmitters.

Based on the distribution of the DIDO antennas in Figures 20a to 20b , we have obtained the coverage map in Palo Alto for systems using DIDO technology. Figures 21a and 21b show two power distributions corresponding to the configurations in Figures 20a and 20b , respectively. The received power distribution (expressed in dBm) is derived assuming a path loss/shadowing model for the urban environment defined by the 3GPP standard [3] at a carrier frequency of 700 MHz. I have observed that using a 50% HP transmitter produces a better coverage of the selected area.

Figures 22a through 22b depict the rate distribution for the two scenarios described above. The throughput (expressed in Mbps) is derived based on the power threshold of the different modulation coding schemes defined in the 3GPP Long Term Evolution (LTE) standard in [4, 5]. At a carrier frequency of 700 MHz, the total available bandwidth is fixed to 10 MHz. Consider two different frequency allocation plans: i) allocate only 5 MHz spectrum to the LP station; ii) allocate 9 MHz to the HP transmitter and allocate 1 MHz to the LP transmitter. Note that the lower bandwidth is typically assigned to the LP station due to its DSL bearer connectivity with limited throughput. Figures 22a-22b show that the rate profile can be significantly increased when using a 50% HP transmitter, thereby increasing the average per-user data rate from 2.4 Mbps in Figure 22a to 38 Mbps in Figure 22b .

Next, we define the algorithm to control the transmission power of the LP stage, so that at any given time to allow higher power, thereby increasing throughput on the downlink channel of FIG. 22b DIDO system. We have observed that FCC limits for power density are defined as time-averaged [2]

among them For the MPE average time, t _n is the time period of exposure to radiation having a power density S _n . For "controlled" exposure, the average time is 6 minutes, and for "uncontrolled" exposure, it increases by 30 minutes. Next, any power source is allowed to transmit at a power level greater than the MPE limit, as long as the average power density in (14) meets the FCC's 30 minute average limit for "uncontrolled" exposure.

Based on this analysis, we define an adaptive power control method to increase the instantaneous daily line transmission power while maintaining the average power per DIDO antenna below the MPE limit. I consider a DIDO system with more transmission antennas than the active user side. Considering that DIDO antennas can be thought of as inexpensive wireless devices (similar to WiFi access points) and can be placed anywhere in the presence of DSL, cable modems, fiber optics or other Internet connectivity, this is a reasonable assumption.

The architecture of a DIDO system with adaptive daily line power control is depicted in Figure 23 . The amplitude of the digital signal generated by multiplexer 234 is dynamically adjusted by power scaling factors S ₁ , . . . , S _M before being sent to DAC unit 235. The power scaling factor is calculated by power control unit 232 based on CQI 233.

In one embodiment, the number N _g DIDO defined antenna groups. Each group contains at least as many DIDO antennas as the number of active clients ( K ). At any given time, only one group has a greater than MPE limit ( ) The power level (S _o) transmitted to the UE N _a> K a role DIDO antennas. A method repeats across all antenna groups according to the cyclic scheduling principle depicted in FIG . In another embodiment, different scheduling techniques (i.e., proportional fair scheduling [8]) are used for cluster selection to optimize error rate or throughput performance.

Assuming loop power allocation, since (14) we export the average transmission power of each DIDO antenna to

Where t _o is the time period in which the antenna group is _active and T _MPE = 30 min is the average time defined by the FCC criteria [2]. The ratio in (15) is the working factor (DF) of the groups, which is defined such that the average transmission power from each DIDO antenna meets the MPE limit ( ). According to the following definition, the working factor depends on the number of users in the role, the number of groups, and the antenna in each group.

The SNR gain (in dB) obtained in a DIDO system with power control and antenna grouping is expressed as a function of the working factor as follows

We have observed that the gain in (17) is achieved at the expense of the G _dB extra transmission power on all DIDO antennas.

In general, the total transmission power from all over the definition of groups of all N _g N _a is

Where P _ij is the average daily line transmission power, which is given by

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

In order to obtain some intuitive perception of the performance of the proposed method, consider 400 DIDO distributed antennas in a given coverage area and 400 clients that subscribe to the wireless internet service provided via the DIDO system. It is impossible that every Internet connection is always fully utilized. It is assumed that 10% of the clients will effectively use the wireless internet connection at any given time. Next, 400 DIDO antennas can be divided into N _g = 10 groups each with N _a = 40 antennas, each group serving K = 40 active clients at any given time with a working factor DF = 0.1. The resulting SNR gain from the transmission scheme is G _dB = ₁₀ log ₁₀ (1/DF) = 10 dB, provided by 10 dB of additional transmit power from all DIDO antennas. However, I have observed that the average daily line transmission power is constant and within the MPE limits.

Figure 25 compares the above-described power control with antenna grouping with the (uncoded) SER performance of the conventional eigenmode selection in U.S. Patent No. 7,636,381. All schemes use BD precoding with four clients, each equipped with a single antenna. SNR refers to the ratio of power per transmit antenna to noise power (ie, the line transmission SNR per day). The curve represented by DIDO 4×4 assumes four transmission antennas and BD precoding. A curve with a square indicates SER performance with two additional transmit antennas and BD with eigenmode selection, resulting in a 10 dB SNR gain (at the 1% SER target) relative to conventional BD precoding. Power control with antenna grouping and DF = 1/10 also produces a 10 dB gain at the same SER target. I observed that due to the diversity gain, the eigenmode selection changes the slope of the SER curve, and our power control method shifts the SER curve to the left (maintaining the same slope) due to the increased average transmission power. For comparison, a SER with a larger duty factor DF = 1/50 is shown to provide an additional 7 dB gain compared to DF = 1/10.

Note that our power control can have a lower complexity than the conventional eigenmode selection method. In fact, the antenna ID of each group can be pre-computed and shared between the DIDO antenna and the UE via a lookup table such that only K channel estimates are required at any given time. For eigenmode selection, ( K + 2 ) channel estimates are calculated and additional computational processing is required to select to minimize the eigenmode of the SER for all clients at any given time.

Next, we describe another method involving DIDO antenna grouping to reduce the additional burden of CSI feedback in some special cases. Figure 26a shows a situation in which the user terminals (points) are randomly scattered in one of the areas covered by a plurality of DIDO distributed antennas (crosses). The average power on each transmission receive radio link can be calculated as A = {|H| ² }. (20)

Where H is the channel estimation matrix available at the DIDO BTS.

The matrix A in Figs. 26a to 26c is numerically obtained by averaging the channel matrices over 1000 instances. Two alternative scenarios are depicted in Figures 26b and 26c , respectively, where the user terminals are grouped together around a subset of DIDO antennas and the client receives negligible power from a DIDO antenna located remotely. For example, Figure 26b shows two antenna groups that generate a block diagonal matrix A. An extreme case is when each client is only very close to one transmitter and the transmitters are far apart from each other such that the power from all other DIDO antennas is negligible. In this case, DIDO plurality of link degradation SISO links 26c and A as shown in the diagonal matrix.

In all three of the above cases, BD precoding dynamically adjusts the precoding weight to account for different power levels on the wireless link between the DIDO antenna and the UE. However, it is convenient to identify multiple groups in a DIDO cluster and operate the DIDO precoding only within each group. The grouping method proposed by us has the following advantages:

̇ Calculated Gain : DIDO precoding is calculated only in each group in the cluster. For example, if BD precoding is used, the singular value decomposition (SVD) has a complexity O(n ³ ), where n is the minimum dimension of the channel matrix H. If H can be reduced to a block diagonal matrix, the SVD of each block is calculated with reduced complexity. In fact, if the channel matrix is divided into two block matrices having dimensions n ₁ and n ₂ such that n = n ₁ + n ₂ , the complexity of SVD is only O(n ₁ ³ ) + O(n ₂ ³ ) <O(n ³ ). In extreme cases, if H is a diagonal matrix, the DIDO link is reduced to multiple SISO links and no SVD calculation is required.

̇ Reduced CSI feedback extra burden : When the DIDO antenna and the UE are grouped into groups, in one embodiment, the CSI from the UE to the antenna is calculated only in the same group. In the TDD system, assuming antenna reciprocity, the antenna grouping reduces the number of channel estimates used to calculate the channel matrix H. In FDD systems where the CSI is fed back over the wireless link, antenna grouping further produces a reduction in the additional burden of CSI feedback on the wireless link between the DIDO antenna and the UE.

Multiple access technology for DIDO uplink channels

In one embodiment of the invention, different multiple access techniques are defined for DIDO uplink channels. These techniques can be used to feed back CSI or transmit data streams from the UE to the DIDO antenna on the uplink. In the following, we refer to the feedback CSI and data stream as uplink streams.

Multiple Input Multiple Output (MIMO) : The uplink stream is transmitted from the UE to the DIDO antenna via an open loop MIMO multiplex scheme. This method assumes that all clients are synchronized over time/frequency. In an embodiment, synchronization between the UEs is achieved via training from the downlink and all DIDO antennas are assumed to be locked to the same time/frequency reference clock. Note that variations in delay spread at different UEs can result in jitter between the clocks of different UEs that can affect the performance of the MIMO uplink scheme. After the UE transmits the uplink stream via the MIMO multiplex scheme, the receiving DIDO antenna can use the nonlinear (ie, maximum likelihood, ML) or linear (ie, zero-minimum mean squared) receiver to eliminate the same The channel interferes and individually demodulates the uplink stream.

Time Division Multiple Access (TDMA) : Different clients are assigned to different time slots. Each 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 a multi-carrier (OFDM) system, a subset of carrier tones is assigned to different clients that simultaneously transmit uplink streams, thereby reducing latency.

̇ Code Division Multiple Access (CDMA) : Each client is assigned to a different pseudo-random sequence and achieves orthogonality across the user end in the code domain.

In one embodiment of the invention, the UE is a wireless device that transmits at a much lower power than the DIDO antenna. In this case, the DIDO BTS defines the user terminal group based on the uplink SNR information such that interference across the subgroups is minimized. In each subgroup, the multiple access techniques described above are used to establish orthogonal channels in the time, frequency, spatial, or code domains, thereby avoiding uplink interference across different UEs.

In another embodiment, the uplink multiple access technique described above is used in conjunction with the antenna grouping method proposed in the previous section to define different groups of users within a DIDO cluster.

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

A link adaptation method for a DIDO system utilizing time, frequency, and spatial selectivity of a wireless channel is defined in U.S. Patent No. 7,636,381. The following is used to make use of A link-adapted embodiment of the invention in a time/frequency selective multi-carrier (OFDM) DIDO system for a wireless channel.

We simulate the Rayleigh fading channel according to the exponentially decaying power delay profile (PDP) or the Saleh-Valenzuela model in [9]. For the sake of simplicity, we assume that a single cluster channel with a multipath PDP is defined as P _n = e ^-βn (21)

Where n =0,..., L -1 is the index of the channel tap, L is the number of channel taps and β =1/ σ _DS is the indicator of the channel coherence bandwidth, and the channel delay spread ( σ _DS ) is inversely proportional to the PDP index. The low value of β produces a frequency flat channel, while the high value of β produces a frequency selective channel. The PDP in (21) is normalized so that the total average power of all L channel taps is one.

Figure 27 depicts the amplitude of the low frequency selective channel (assuming β = 1) in the delay domain or instantaneous PDP (upper curve) and frequency domain (lower curve) of the DIDO 2x2 system. The first subscript indicates the UE and the second subscript indicates the transmission antenna. A high frequency selective channel (where β = 0.1) is shown in Figure 28 .

Next, we study the performance of DIDO precoding in frequency selective channels. Assuming that the signal model in (1) satisfies the condition in (2), we calculate the DIDO precoding weight via BD. We re-formulated the DIDO received signal model in (5) to r _k =H _ek s _k +n _k by the condition in (2). (twenty three)

Where H _ek =H _k W _k is the effective channel matrix of user k . For a single antenna DIDO 2x2 at each client, the effective channel matrix is reduced to a channel having the frequency response shown in Figure 29 and used for high frequency selectivity (e.g., where β = 0.1) in Figure 28 a value. The solid line in Fig. 29 refers to the client terminal 1, and the line with the dot refers to the client terminal 2. Based on the channel quality in Figure 29 , we define a time/frequency domain link adaptation (LA) method that dynamically adjusts the MCS depending on the channel conditions of the change.

We began by evaluating the effectiveness of different MCSs in the AWGN and Rayleigh fading SISO channels. For the sake of simplicity, we assume no FEC coding, but the following LA method can be extended to systems including FEC.

Figure 30 shows the SER of different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM). Without loss of generality, we assume a 1% target SER for uncoded systems. The SNR threshold used to satisfy the target SER in the AWGN channel is 8 dB, 15.5 dB, and 22 dB for the three modulation schemes, respectively. In the Rayleigh fading channel, it is well known that the SER performance of the above modulation scheme is worse than AWGN [13] and the SNR thresholds are: 18.6 dB, 27.3 dB and 34.1 dB, respectively. We have observed that DIDO precoding transforms a multi-user downlink channel into a set of parallel SISO links. Therefore, on the per-user basis, the same SNR threshold as used in the SISO system for Figure 30 applies to the DIDO system. In addition, if the instantaneous LA is performed, the threshold in the AWGN channel is used.

A key idea of the proposed LA method for DIDO systems is to use a low MCS order to provide link robustness when the channel experiences deep fading in the time or frequency domain (depicted in Figure 28 ). Conversely, when the channel is characterized by a large gain, the LA method switches to a higher MCS order to increase spectral efficiency. One contribution of the present application is to use the effective channel matrix in (23) and in Figure 29 as a measure to allow for adaptation, as compared to U.S. Patent No. 7,636,381.

The overall architecture of the LA method is depicted in Figure 31 and is defined as follows:

̇ CSI Estimation : At 3171, the DIDO BTS calculates the CSI from all users. The user can be equipped with a single or multiple receiving antennas.

̇ DIDO precoding : At 3172, the BTS calculates the DIDO precoding weights for all users. In an embodiment, the BD is used to calculate such weights. The precoding weights are calculated on a carrier-by-carrier basis.

̇ Link quality calculation : At 3173, the BTS calculates the frequency domain link quality. In an OFDM system, metrics are calculated based on CSI and DIDO precoding weights for each carrier tone. In one embodiment of the invention, the link quality is the average SNR over all OFDM carrier frequencies. We define this method as LA1 (based on average SNR performance). In another embodiment, the link quality is a frequency response of the active channel in (23). We define this method as LA2 (based on carrier-to-carrier tone performance to take advantage of frequency diversity). If each client has a single antenna, the frequency domain active channel is depicted in FIG . If the UE has multiple receive antennas, the link quality is defined as the Frobenius norm for the effective channel matrix for each carrier tone. Alternatively, a plurality of link quality metrics are defined for each client as a singular value of the effective channel matrix in (23).

̇ Bit Loading Algorithm : At 3174, based on the link quality, the BTS determines the MCS for different UEs and different OFDM carrier frequencies. For the LA1 method, the same MCS is used for all UEs and all OFDM carrier tones based on the SNR threshold of the Rayleigh fading channel in FIG . For LA2, different MCSs are assigned to different OFDM carrier tones to utilize channel frequency diversity.

̇ Precoding data transmission : At 3175, the BTS uses the MCS derived from the bitloading algorithm to stream the precoded data stream from the DIDO distributed antenna to the UE. A header is attached to the precoded material to communicate the MCS for different carrier tones to the client. For example, if eight MCSs are available and the OFDM symbols are defined with N=64 carrier frequency modulations, then log ₂ (8)*N=192 bits are needed to communicate the current MCS to each client. Assuming 4-QAM (2-bit/symbol spectral efficiency) is used to map the bits into the symbols, only 192/2/N=1.5 OFDM symbols are needed to map the MCS information. In another embodiment, multiple subcarriers (or OFDM carrier tones) are grouped into subbands, and the same MCS is assigned to all carrier tones in the same subband to reduce the additional burden due to control information. In addition, the MCS is adjusted based on the time variation of the channel gain (proportional to the coherence time). In a fixed wireless channel (characterized by a low Doppler effect), the MCS is recalculated at one of the channel coherence times, thereby reducing the additional burden required to control the information.

Figure 32 shows the SER efficacy of the LA method described above. For comparison, the SER performance in the Rayleigh fading channel is plotted for each of the three QAM schemes used. The LA2 method adapts the MCS to accommodate fluctuations in the effective channel in the frequency domain, thereby providing 1.8 bps/Hz gain and SNR for low SNR (ie, SNR = 20 dB) spectral efficiency compared to LA1 (for SNR > 35 dB) 15dB gain in ).

System and method for DIDO precoding interpolation in a multi-carrier system

The computational complexity of a DIDO system is primarily limited to the central processor or BTS. The most computationally expensive operation is to calculate the precoding weights of all clients according to the CSI of all clients. When using BD precoding, the BTS must perform as many singular value decomposition (SVD) operations as there are numbers of clients in the system. One way to reduce complexity is via parallel processing, where the SVD is calculated on an individual processor for each client.

In a multi-carrier DIDO system, each subcarrier experiences a flat fading channel and SVD is performed for each UE on each subcarrier. Clearly, the complexity of the system increases linearly with the number of subcarriers. For example, in an OFDM system with a 1 MHz signal bandwidth, the cyclic first code (L ₀ ) must have at least eight channel taps (ie, a duration of 8 microseconds) to avoid outdoor with large delay spread. Intersymbol interference in the environment of urban giant cells [3]. The size of the Fast Fourier Transform (FFT) used to generate the OFDM symbols (N _FFT ) is typically set to a multiple of L ₀ to reduce the loss of data rate. If N _FFT = 64, the effective spectral efficiency of the system is limited by the factor N _FFT /(N _FFT +L ₀ )=89%. The larger value of the N _FFT yields higher spectral efficiency at the expense of higher computational complexity at the DIDO precoder.

One way to reduce the computational complexity at the DIDO precoder is to perform an SVD operation on a subset of the carrier frequency (which we call the pilot carrier tone) and derive the precoding for the remaining carrier frequency via interpolation. Weights. Weight interpolation is an error source that causes interference between users. In an embodiment, the optimized weight interpolation technique is used to reduce inter-user interference, resulting in improved error rate performance and lower computational complexity in a multi-carrier system. In a DIDO system with M transmit antennas, U client terminals, and N receive antennas per user terminal, the condition of the precoding weight (W _k ) of the kth client end that guarantees zero interference to other UEs u is Exported as (2)

Where H _u is the channel matrix corresponding to other DIDO clients in the system.

In an embodiment of the invention, the objective function of the weight interpolation method is defined as

Where θ _k is a set of parameters to be optimized for user k , (θ _k ) is the weight interpolation matrix and ∥. ∥ _F represents the Frobenius norm of the matrix. The optimization problem is formulated as

Where Θ _k is a feasible set of optimization problems and θ _{k , opt} is the optimal solution.

The objective function in (25) is defined for an OFDM carrier tone. In another embodiment of the invention, the objective function is defined as a linear combination of the Frobenius norms in (25) of the matrix of all OFDM carrier tones to be interpolated. In another embodiment, the OFDM spectrum is divided into subsets of carrier frequency modulation and the optimal solution is given by

Where n is the OFDM carrier frequency index and A is the subset of carrier frequency modulation.

The weight interpolation matrix W _k (θ _k ) in (25) is expressed as a function of the set of parameters θ _k . Once the optimization set is determined according to (26) or (27), the optimization weight matrix is calculated. In one embodiment of the invention, the weighted interpolation matrix for a given OFDM carrier tone n is defined as a linear combination of the weighted matrix of the pilot carrier tone. An example of a weight interpolation function for a beamforming system with a single client is defined in [11]. In the DIDO multi-user system, we write the weight interpolation matrix as

Where 0 l ( L ₀ -1), L ₀ is the number of pilot carrier tones and c _n = ( n -1) / N ₀ , where N ₀ = N _FFT / L ₀ . Then normalize the weight matrix in (28) so that To ensure a single power transfer from each antenna. If N = 1 (single receive antenna per client), then the matrix in (28) becomes a vector normalized with respect to its norm. In one embodiment of the invention, the pilot carrier tone is uniformly selected over the range of OFDM carrier frequency modulation. In another embodiment, the pilot carrier tone is adaptively selected based on CSI to minimize interpolation errors.

We have observed that a key difference between the system and method in [11] and the system and method proposed in this patent application is the objective function. In particular, the system in [11] assumes multiple transmit antennas and a single client, and the associated method is designed to maximize the product of the precoding weight multiplied by the channel to maximize the received SNR at the user end. However, this approach does not work in a multi-user situation because it creates inter-user interference due to interpolation errors. Instead, our approach is designed to minimize inter-user interference, thereby improving error rate performance for all clients.

Figure 33 shows the entries for the matrix in (28) as a function of the OFDM carrier tone index for a DIDO 2x2 system where N _FFT = 64 and L ₀ = 8. The channel PDP is generated according to the model in (21) (where β = 1), and the channel is composed of only eight channel taps. We have observed that L ₀ must be chosen to be greater than the number of channel taps. The solid line in Fig. 33 represents an ideal function, and the broken line is an interpolation function. According to the definition in (28), for pilot carrier frequency modulation, the interpolation weights match the ideal function. The weight calculated on the remaining carrier frequency is due to the estimation error and only approximates the ideal condition.

One way to implement a weighted interpolation method is through an exhaustive search of the feasible set Θ _k in (26). In order to reduce the complexity of the search, we quantize the feasible set into P values uniformly within the range [0, 2π]. Figure 34 shows a variable number of SER versus SNR for L ₀ = 8, M = N _t = 2 transmit antennas and P. As the number of quantization levels increases, the SER performance improves. I have observed that the P = 10 condition is close to the performance of P = 100 due to the much lower computational complexity attributed to the reduced number of searches.

FIG 35 shows performance for a number of different SER DIDO bands and interpolation methods of the L ₀ = 16. We assume that the number of clients is the same as the number of transmission antennas and that each client is equipped with a single antenna. When the number of clients increases, the SER performance is degraded due to an increase in inter-user interference caused by weight interpolation errors.

In another embodiment of the invention, weight interpolation functions other than the weighting interpolation functions of (28) are used. For example, a linear predictive self-regressive model [12] can be used to interpolate weights across different OFDM carrier frequencies based on estimates of channel frequency correlation.

references

[1] US Application No. 12/630,627, filed on December 2, 2009, entitled "System and Method For Distributed Antenna Wireless Communications", "System and method for distributed antenna wireless communications" by A. Forenza and SGPerman .

[2] August 1997, OET Bulletin 65 (Ed. 97-01), FCC "Evaluating compliance with FCC guidelines for human exposure to radio frequency electromagnetic fields".

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

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

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

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

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

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

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

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

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

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

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

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

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[16] February 1995, IEEE Trans. On Sign. Proc. , Vol. 43, No. 2, pp. 506-515, F. Qian, "Partially adaptive beamforming for correlated interference rejection".

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

[19] January 1977, U.S. Patent No. 4,003,016, by W.R. Remley "Digital beamforming system".

[18] September, 1988, U.S. Patent No. 4,771,289, "Beamforming/null-steering adaptive array" by R.J. Masak.

[20] In February 1997, U.S. Patent No. 5,600,326, K.-B. Yu et al., "Adaptive digital beamforming architecture and algorithm for nulling main lobe and multiple side lobe radar jammers while preserving monopulse ratio angle estimation accuracy".

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[22] December 2002, Vol. 2, pp. 536-540, M. Schubert et al., "Joint 'dirty paper' pre-coding and downlink beamforming".

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

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

[25] February 2004, IEEE Trans. Sig. Proc., Vol. 52, pp. 461 to 4/1, QHSpencer, ALSwindlehurst, and M. Haardt, "Zero forcing methods for downlink spatial multiplexing in multiuser MIMO channels".

II. Disclosure from related application No. 12/917,257

Radio frequency (RF) communication systems and methods using a plurality of distributed transmission antennas that cooperatively operate to establish a wireless link to a given user while suppressing interference to other users are described below. Coordination across different transmit antennas is allowed via user clustering. The user cluster is such that its signal can be reliably detected by a given user (ie, received A subset of the transmit antennas whose signal strength is above the noise or interference level. Each user in the system defines its own user-cluter. The waveforms transmitted by the transmit antennas of the same user cluster are coherently combined to establish RF energy at the location of the target user and establish zero RF interference points at the location of any other user reachable by their antennas.

Consider a system with M transmit antennas in a user cluster and K users reachable by their M antennas, where K M. We assume that the transmitter knows the CSI between the M transmit antennas and the K users. C ^{K × M} ). For simplicity, it is assumed that each user is equipped with a single antenna, but the same method can be extended to multiple receive antennas per user. Consider channel vectors from M transmit antennas to K users (h _k C ^{1 × M} ) The following channel matrix H obtained by combination

Calculate the precoding weights (w _k ) that establish the RF energy to user k and zero RF energy to all other K -1 users C ^{M ×1} ) to satisfy the following conditions

among them The effective channel matrix of user k obtained by removing the kth column of matrix H , and 0 ^{K × 1} is a vector having all zero entries.

In one embodiment, the wireless system is a DIDO system and uses user clustering to establish a wireless communication link to the target user while pre-empting interference with any other users that can be reached by antennas located within the user cluster. In US Application Serial No. 12/630,627, a DIDO system is described which includes: ̇ DIDO client : a user terminal equipped with one or more antennas; ̇ DIDO distributed antenna : a transceiver station that cooperatively operates Transmitting precoded data streams to multiple users, thereby suppressing inter-user interference; ̇ DIDO base transceiver station (BTS) : a centralized processor that generates precoded waveforms to DIDO distributed antennas; ̇ DIDO base Network (BSN) : Wired backhaul, which connects BTS and DIDO distributed antennas or other BTS.

DIDO distributed antennas are grouped into different subsets depending on their spatial distribution relative to the location of the BTS or DIDO client. I defines three types of clusters, as depicted in Figure 36: Super cluster ˙ 3640: is connected to one or more sets of BTS DIDO distributed antennas, so that all round between the BTS and the latent respective user time Within the constraints of the DIDO precoding loop; ̇ DIDO cluster 3641 : a collection of DIDO distributed antennas connected to the same BTS. When a supercluster contains only one BTS, its definition is consistent with the DIDO cluster; ̇user cluster 3642 : a collection of DIDO decentralized antennas that cooperatively transmit precoded data to a given user.

For example, the BTS is a local hub connected to other BTS and DIDO distributed antennas via the BSN. BSN can include a variety of network technologies including, but not limited to, Digital Subscriber Line (DSL), ADSL, VDSL [6], cable modems, fiber optic rings, T1 lines, fiber-optic coaxial hybrid (HFC) networks, and/or fixed Wireless (for example, WiFi). All BTSs within the same supercluster share information about DIDO precoding via the BSN such that the round trip latency is within the DIDO precoding loop.

In Fig. 37 , respectively, the dots represent DIDO distributed antennas, the cross is the user and the dashed lines indicate the user clusters of users U1 and U8. The method described below is designed to establish a communication link to the target user U1 while establishing a zero RF energy point for any other user (U2 to U8) internal or external to the user cluster.

I propose a similar method in [5], in which a zero RF energy point is established to remove the DIDO plexus. Interference in overlapping areas between sets. Additional antennas are needed to transmit signals to the UEs within the DIDO cluster while suppressing inter-cluster interference. An embodiment of the method proposed in the present application does not attempt to remove DIDO inter-cluster interference; rather it assumes that the cluster is tied to the UE (ie, user-cluster) and guarantees that it is not in any of the neighborhoods Other clients generate interference (or interference can be ignored).

One idea associated with the proposed method is that users far enough away from the user-cluster are attributable to large path losses without being affected by radiation from the transmitting antenna. Users near or within the user-cluster receive interference-free signals due to precoding. Further, additional transmit antennas to the user - cluster (as shown in FIG. 37), so as to satisfy the condition K M.

An embodiment of a method of using user clustering consists of the following steps:

a. Link quality measurement : report the link quality between each DIDO distributed antenna and each user to the BTS. The link quality consists of a signal-to-noise ratio (SNR) or a signal-to-interference plus noise ratio (SINR).

In an embodiment, the DIDO distributed antenna transmits a training signal and the user estimates the received signal quality based on the training. The training signals are designed to be orthogonal in the time, frequency or code domain so that the user can distinguish between different transmitters. Alternatively, the DIDO antenna transmits a narrowband signal (i.e., a single carrier tone) at a particular frequency (i.e., a beacon channel), and the user estimates the link quality based on the beacon signal. Was defined as a threshold for successful data demodulation minimum signal amplitude (or power) over the noise level, as shown in FIG. 38a. Any link quality value below this threshold is assumed to be zero. The link quality is quantized over a finite number of bits and fed back to the transmitter.

In various embodiments, the beacon based training signal or from a user and transmitting the link quality is estimated based (e.g., in FIG. 38b) in DIDO transmission antenna, assuming an uplink (UL) and the downlink path loss (DL Reciprocity between path losses. Note that when the UL and DL frequency bands are relatively close, the path loss reciprocity is the actual time division duplex (TDD) system (with UL and DL channels at the same frequency) and the frequency division duplex (FDD) system. Assumption.

As depicted in FIG. 37, BSN via the BTS across different shared information on the link quality measure, so that the quality of the link between all its BTS across different DIDO clusters each antenna / coupling users.

b. User-cluster definition : The link quality of all radio links in a DIDO cluster is an entry for the link quality matrix shared across all BTSs via the BSN. An example of a link quality matrix for the situation in Figure 37 is depicted in Figure 39 .

The link quality matrix is used to define the user cluster. For example, Figure 39 shows a selection of user clusters for user U8. A subset of the transmitters of user U8 having a non-zero link quality (i.e., the active transmitter) is first identified. These transmitters are populated for the user-cluster of user U8. A sub-matrix containing non-zero entries from the transmitter in the user-cluster to other users is then selected. Note that because the link quality is only used to select the user cluster, it can be quantized by only two bits (ie, to identify the state above or below the threshold in Figure 38 ), thereby Reduce the extra burden of feedback.

Another example for user U1 is depicted in FIG . In this case, the number of active transmitters is lower than the number of users in the sub-matrix, thereby violating the condition K. M. Therefore, one or more rows are added to the submatrix to satisfy the condition. If the number of transmitters exceeds the number of users, additional antennas can be used for the diversity scheme (i.e., antenna or eigenmode selection).

Yet another example for user U4 is shown in FIG . We have observed that this submatrix can be obtained as a combination of two sub-matrices.

c. CSI Report to BTS : Once the user cluster is selected, the CSI from all transmitters in the user-cluster to each user reached by their transmitters is available to all BTSs. CSI information is shared across all BTSs via the BSN. In a TDD system, UL/DL channel reciprocity can be utilized to derive CSI from training on the UL channel. In the FDD system, a feedback channel from all users to the BTS is required. In order to reduce the amount of feedback, only the CSI corresponding to the non-zero entry of the link quality matrix is fed back.

d . DIDO Precoding: Finally, DIDO precoding is applied to each CSI submatrix corresponding to a different set of users (e.g., as described in the related U.S. Patent Application).

In an embodiment, calculating an effective channel matrix Singular value decomposition (SVD) and the precoding weight w _k for user k is defined as corresponding to The right singular vector of the zero subspace. Or, if M > K and SVD decomposes the effective channel matrix into , the DIDO precoding weight for user k is given by _{k k} =U _O (U _O ^H .h _k ^T )

U _O system behavior A matrix of singular vectors of zero subspaces.

According to the basic linear algebra, we observed the matrix The right singular vector in the zero subspace is equal to the eigenvector of C corresponding to the zero eigenvalue

Where the effective channel matrix is decomposed into SVDs into . Next, calculate An alternative to SVD is to calculate the eigenvalue decomposition of C. There are several methods of calculating eigenvalue decomposition, such as power methods. Since we are only interested in the eigenvectors corresponding to the zero subspace of C , we use the inverse power method described by iteration.

The first iterative vector (u _i ) is a random vector.

Considering that the eigenvalue (λ) of the zero subspace is known (ie, zero), the inverse power method requires only one iteration to converge, thereby reducing computational complexity. Next, we write the precoding weight vector as w=C ^-1 u ₁

Where u ₁ is a vector having a real input term equal to 1 (ie, the precoding weight vector is the sum of the lines of C ^-1 ).

DIDO precoding calculations require a matrix inversion. There are several numerical solutions to reduce the complexity of matrix inversion, such as the algorithm of Strassen [1] or the algorithm of Coppersmith-Winograd [2, 3]. Since C is a Hermitian matrix by definition, an alternative solution is to decompose C into the real part and the imaginary part, 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 a user as shown in FIG. 42 DIDO clusters across different mobile terminal, the user - the cluster to follow the movement. In other words, when the UE changes its position, the subset of transmit antennas is continually updated and the effective channel matrix (and corresponding precoding weights) is recalculated.

The method proposed herein works within the supercluster in Figure 36 because the link between the BTSs via the BSN must be low latency. In order to suppress interference in overlapping regions of different super clusters, the method of [5] can be used, which uses additional antennas to establish zero RF energy points in the interference region between the DIDO clusters.

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

references

[1] November 2005, SIAM News, Vol. 38, No. 9, S. Robinson's "Toward an Optimal Algorithm for Matrix Multiplication".

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

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

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

[5] Patent Application No. 12/802,988, filed on June 16, 2010, A. Forenza and S.G. Perlman, "INTERFERENCE MANAGEMENT, HANDOFF, POWER CONTROL AND LINK ADAPTATION IN DISTRIBUTED-INPUT DISTRIBUTED-OUTPUT(DIDO)COMMUNICATION SYSTEMS".

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

III. System and method for utilizing coherent regions in a wireless system

The capacity of a multiple antenna system (MAS) in an actual propagation environment varies with the spatial diversity available on the wireless link. Spatial diversity is determined by the distribution of scatter artifacts in the wireless channel and the geometry of the transmit and receive antenna arrays.

One common model of the MAS channel is the so-called cluster channel model, which defines a group of scatterers as a cluster positioned around the transmitter and receiver. In general, the more clusters and the greater the angular spread, the higher the spatial diversity and capacity achievable on the wireless link. The cluster channel model has been verified by actual measurements [1-2], and variants of these models have been made up of different indoors (ie, IEEE 802.11n technology group [3] for WLAN) and outdoor (for 3G cellular) The 3GPP technical specification group [4] of the system is adopted by the wireless standard.

Other factors that determine spatial diversity in a wireless channel are characteristics of the antenna array, including: antenna element spacing [5-7], number of antennas [8-9], array aperture [10-11], array geometry [5, 12,13], polarization and antenna pattern [14-28].

A unified model describing the antenna array design and the effects of the characteristics of the propagation channel on the spatial diversity (or degree of freedom) of the wireless link is presented in [29]. The received signal model in [29] is given by y(q)=ʃC(q,p)×(p) d p+z(q)

Where x(p) C ³ is the polarization vector describing the transmitted signal, p, q R ³ is the position of the polarization vector describing the transmission and reception arrays, respectively, and C(.,.) C ^3×3 is a matrix describing the system response between the position of the transmission vector and the position of the received vector, which is given by

Where A _t (.,.)A _r (.,.) C ^{3 × 3} for the transmission array response and receive array response and H ( , ) C ^3×3 is the channel response matrix, where the input is the transmission direction And receiving direction The complex gain between. In a DIDO system, a user device can have a single or multiple antennas. For the sake of simplicity, we assume a single antenna receiver with an ideal isotropic field and rewrite the system response matrix to

Which only considers the transmission antenna field type A ( , p).

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

Where p P, P is the space defining the antenna array and

among them( , p) Ω×P. For unpolarized antennas, studying the array response is equivalent to studying the integral kernel above. In the following, we show the closure of the statements for the integral kernels of different types of arrays.

Unpolarized linear array

For an unpolarized linear array of length L (normalized by wavelength) and an antenna element oriented along the z-axis and centered at the origin, the integral kernel is given by [29] a(cos θ , p _z )=exp( -j2π p _z cos θ )

Extending the above equation into a series of shifting dyadicians, we obtain a sinusoidal function with a resolution of 1/L, and the dimension of the subspace where the array is limited and the approximate wave vector is finite (ie, the degree of freedom) is D _F =L| Ω _θ |

Where Ω _θ ={cos θ : θ Θ}. I have observed for the vertical array | Ω _θ |=|Θ|, and for the end-fire array | Ω _θ | |Θ| ² /2.

Unpolarized spherical array

The integral kernel of a spherical array of radius R (normalized by wavelength) is given by [29]

Decomposing the above function by the sum of the first type of spherical Bessel functions, we obtain the resolution of the spherical array as 1/(πR ² ), and the degree of freedom is given by D _F =A|Ω|=πR ² |Ω |

Where A is the area of the spherical array and |Ω| [0, π) × [0, 2π).

Coherent area in wireless channel

The relationship between the resolution of the spherical array and its area A is depicted in FIG . The middle ball is a spherical array of area A. The projection of the channel cluster on the unit sphere defines different scattering regions whose size is proportional to the angular spread of the cluster. The area of size 1/ A within each cluster (referred to as the "coherent region" by me) represents the projection of the fundamental function of the radiation field of the array and defines the resolution of the array in the wave vector domain.

Comparing Figure 43 with Figure 44 , we observed that the size of the homology region decreases with the reciprocal of the size of the array. In fact, a larger array can concentrate energy in a smaller area, resulting in a greater number of degrees of freedom D _F . Note that the total number of degrees of freedom also depends on the angular extent of the cluster, as shown in the definition above.

Figure 45 depicts another example in which the array size covers an even larger area resulting in additional degrees of freedom compared to Figure 44 . In a DIDO system, the array aperture can be approximated by the total area covered by all DIDO transmitters (assuming the antennas are spaced apart by the fraction of wavelengths). Thus, Figure 45 shows that the DIDO system can achieve an increased number of degrees of freedom by dispersing the antennas in space, thereby reducing the size of the coherent regions. Note that these plots are produced assuming an ideal spherical array. In practical situations, DIDO antennas are randomly scattered over a large area and the resulting shape of the coherent area may not be as regular as in the figure.

Figure 46 shows that as the array size increases, more clusters are included in the wireless channel as the radio waves are scattered by an increased number of objects between the DIDO transmitters. Thus, an increased number of basis functions (across the radiation field) can be excited to create additional degrees of freedom as defined above.

The multi-user (MU) multi-antenna system (MAS) described in this patent application utilizes the coherence region of the radio channel to establish multiple simultaneous independent non-interfering data streams to different users. For a given channel condition and user distribution, the basic function of the radiation field is selected to establish independent and simultaneous wireless links to different users such that each user experiences a non-interfering link. When the MU-MAS knows the channel between each transmitter and each user, the precoding transmission is adjusted based on the information to establish individual coherence regions to different users.

In one embodiment of the invention, MU-MAS uses nonlinear precoding, such as dirty paper coding (DPC) [30-31] or Tomlinson-Harashima (TH) [32- 33] Precoding. In another embodiment of the invention, the MU-MAS uses non-linear precoding, such as block diagonalization (BD) or zero-forcing beamforming (ZF- in our prior patent application [0003-0009]. BF) [34].

In order to allow precoding, MU-MAS needs to know Channel Status Information (CSI). The CSI can be used for MU-MAS via the feedback channel, or CSI can be estimated on the uplink channel (assuming uplink/downlink channel reciprocity is possible in a time division duplex (TDD) system). One way to reduce the amount of feedback required for CSI is to use a limited feedback technique [35-37]. In an embodiment, the MU-MAS uses limited feedback techniques to reduce the additional CSI burden on the control channel. Codebook design is the key to limited feedback technology. An embodiment defines a codebook from a fundamental function of the radiation field across the transmission array.

The coherent area changes its position and shape as the user moves in space or the propagation environment changes over time due to a moving object, such as a person or a car. This is due to the well-known Doppler effect in wireless communications. The MU-MAS described in this patent application adjusts the precoding to constantly adapt the coherent region for each user as the environment changes due to the Doppler effect. This adaptation of the coherent region is not a dry process for establishing different users. Disturb the channel.

Another embodiment of the present invention adaptively selects a subset of antennas of the MU-MAS system to create coherent regions of different sizes. For example, if the user dispersed sparsely in space (i.e., the time, or rural areas having low usage of radio resources), then only a small subset of selected antennas and the same size of the area with respect to the tone as in FIG. 43 The size of the array is large. Alternatively, in densely populated areas (i.e., urban areas or times with peak usage of wireless services), more antennas are selected to create small coherent areas for users in close proximity to each other.

In one embodiment of the invention, the MU-MAS is a DIDO system as described in the prior patent application [0003-0009]. DIDO systems use linear or nonlinear precoding and/or finite feedback techniques to establish coherence regions to different users.

Numerical result

We begin by calculating the number of degrees of freedom in a conventional multiple input multiple output (MIMO) system based on array size. We consider unpolarized linear arrays and two types of channel models: indoor models in the IEEE 802.11n standard for WiFi systems and outdoor models in the 3GPP-LTE standard for cellular systems. The indoor channel model in [3] defines the number of clusters in the range [2, 6] and the angular spread in the range [15°, 40°]. The outdoor channel model for urban micro cells defines about 6 clusters and an angular spread of about 20° at the base station.

Figure 47 shows the degrees of freedom of a MIMO system in actual indoor and outdoor propagation scenarios. For example, considering a linear array with ten antennas spaced one wavelength apart, the maximum degree of freedom (or number of spatial channels) available on the wireless link is limited to about 3 for outdoor situations and 7 for indoor situations. Of course, indoor channels offer more freedom due to larger angular spread.

Next, we calculate the degrees of freedom in the DIDO system. I consider the situation in which the antenna is dispersed in 3D space, such as DIDO access points can be dispersed on different floors of adjacent buildings. The city center situation on the top. Thus, we have modeled DIDO transmission antennas (both connected to each other via fiber or DSL backbone) as a spherical array. Again, we assume that the clusters are evenly dispersed over the solid angles.

Figure 48 shows the degree of freedom as a function of array diameter in a DIDO system. I have observed that for a diameter equal to ten wavelengths, about 1000 degrees of freedom can be used in a DIDO system. In theory, it is possible to create up to 1000 non-interfering channels to the user. The increased spatial diversity of the spatial diversity system due to the scattered antennas in space is key to the multiplex gain provided by conventional MIMO systems.

For comparison, we show the degree of freedom that can be achieved in a suburban environment by the DIDO system. We assume that the cluster is dispersed in the elevation angle [α, π-α], and the solid angle of the cluster is defined as |Ω|=4π cosα. For example, in a suburban situation with a two-story building, the elevation angle of the scatterer can be α = 60°. In this case, the number of degrees of freedom as a function of wavelength is shown in FIG .

references

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IV. Program evolution and outdated systems and methods for multi-user spectrum

The growing demand for high-speed wireless services and an increasing number of cellular phone users has been generated in the wireless industry over the past three decades from the initial analog voice service (AMPS [1-2]) to support digital voice (GSM [3] -4], IS-95 CDMA [5]), data services (EDGE [6], EV-DO [7]) and Internet browsing (WiFi [8-9], WiMAX [10-11], 3G [12-13], 4G [14-15]) The fundamental technological revolution of the standard. The growth of this wireless technology over the years has been achieved thanks to two main tasks:

i) The Federal Communications Commission (FCC) [16] has been allocating new spectrum to support emerging standards. For example, in the first generation of 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 increased number of cellular clients. Recently, commercialization of technologies like Wi-Fi, Bluetooth and ZigBee has become possible by using the unlicensed ISM band [17] allocated by the FCC as early as 1985.

Ii) The wireless industry has been generating new technologies that make more efficient use of limited available spectrum to support higher data rate links and an increasing number of users. a major revolution in the wireless field In the 1990s, from the analogy of the AMPS system to the digital D-AMPS and GSM migration, digital D-AMPS and GSM allowed for a much higher number of calls for a given frequency band due to improved spectral efficiency. In the early 21st century, spatial processing techniques such as Multiple Input Multiple Output (MIMO) produced another fundamental change, resulting in a 4x (4x) improvement in data rate relative to previous wireless networks and by different standards. (ie, IEEE 802.11n for Wi-Fi, IEEE 802.16 for WiMAX, 3GPP for 4G-LTE).

Despite efforts to provide solutions for high-speed wireless connectivity, the wireless industry is facing new challenges: providing high-definition (HD) video streaming to meet the growing demand for similar gaming services and anywhere (including rural areas) , where the construction of wired backbones is costly and impractical) provides wireless coverage. Currently, state-of-the-art wireless standard systems (i.e., 4G-LTE) cannot provide data rate requirements and latency constraints to support HD streaming services, especially when the network is overloaded due to a large number of concurrent links. Once again, the main drawbacks are limited spectrum availability and the lack of technology that can truly enhance the data rate and provide fully covered spectrum efficiency.

A new technique called Decentralized Input Decentralized Output (DIDO) [18-21] and described in our prior patent application [0002-0009] has emerged in recent years. DIDO technology promises an order of magnitude increase in spectral efficiency, making HD wireless streaming services possible in overloaded networks.

At the same time, the US government has been addressing the lack of spectrum by embarking on a plan to release the 500MHz spectrum over the next 10 years. The program was released on June 28, 2010 with the goal of allowing emerging wireless technologies to operate in new bands and provide high-speed wireless coverage in urban and rural areas [22]. As part of this program, on September 23, 2010, the FCC opened about 200 MHz for unlicensed VHF and UHF spectrum, which is called "white space" [23]. One limitation of operating in their frequency bands is that no harmful interference to existing wireless microphone devices operating in the same frequency band may be generated. Thus, in 2011 On July 22, the IEEE 802.22 working group finalized the standard for new wireless systems using cognitive radio technology (or spectrum sensing) with key features that dynamically monitor the spectrum and operate in the available frequency bands, thereby avoiding Harmful interference to coexisting wireless devices [24]. Only recently has one part of the white space allocated to licensed use and open for spectrum auctions [25].

Over the years, the simultaneous presence of unlicensed devices in the same frequency band and the unlicensed use of spectrum contention for licensed use have become two of the major issues in the FCC spectrum allocation plan. For example, in white space, the simultaneous presence between a wireless microphone and a wireless communication device has been enabled by cognitive radio technology. However, cognitive radio can only provide a portion of the spectral efficiency of other technologies that use spatial processing like DIDO. Similarly, over the past decade, the performance of Wi-Fi systems has been attributed to the increased number of access points and the use of Bluetooth/ZigBee devices operating in the same unlicensed ISM band and generating uncontrolled interference. Significantly downgraded. One of the disadvantages of the unlicensed spectrum is the unregulated use of RF devices, which will continue to contaminate the spectrum over the next few years. RF pollution also prevents unlicensed spectrum from being used for future licensed operations, limiting important market opportunities for wireless broadband commercial services and spectrum auctions.

We propose a new system and method that allows for the dynamic allocation of wireless spectrum to allow for the coexistence and evolution of different services and standards. One embodiment of our method dynamically assigns permissions to an RF transceiver to operate in certain portions of the spectrum and allows for obsolescence of the same RF device to provide:

i) Spectrum reconfigurability to enable new wireless operations (ie, licensed to unlicensed) and/or to meet new RF power emission limits. This feature allows for spectrum auctions when necessary without pre-planning for the use of licensed spectrum relative to unlicensed spectrum. It also allows adjustment of the transmit power level to meet the new power transmit level enforced by the FCC.

Ii) coexistence of different technologies operating in the same frequency band (ie, white space and wireless microphone, WiFi and Bluetooth/ZigBee), so that the frequency band can be dynamically activated when new technologies are established Redistribution while avoiding interference with existing technology.

Iii) Seamless wireless infrastructure as the system migrates to more advanced technologies that provide higher spectral efficiency, better coverage and improved performance to support new services that require higher QoS (ie, HD video streaming) Evolution.

In the following, we describe a system and method for planning evolution and obsolescence for multi-user spectrum. One embodiment of a system according to one or more of the composition 4911-4913 centralized processor (CP) 4901 to 4904 and one or more distributed node (DN), and a dispersion of such a centralized processor node via As depicted in FIG. 49 Communicate by wired or wireless connection. For example, in the context of the 4G-LTE network [26], the centralized processor is an Access Core Gateway (ACGW) connected to several Node B transceivers. In the context of Wi-Fi, the centralized processor is an Internet Service Provider (ISP) and the decentralized node is a Wi-Fi access point that connects to the ISP via a modem or a direct connection to a cable or DSL. . In another embodiment of the invention, the system is a decentralized input decentralized node having a centralized processor (or BTS) and a decentralized node that is a DIDO access point (or a DIDO distributed antenna connected to the BTS via a BSN) Output (DIDO) system [0002-0009].

The DNs 4911 to 4913 communicate with the CPs 4901 to 4904. The information exchanged from the DN to the CP is used to dynamically adjust the configuration of the node to the evolution of the network architecture. In an embodiment, DNs 4911 through 4913 share their identification numbers with the CP. The CP stores the identification numbers of all DNs connected via the network in a lookup table or a shared database. Their lookup tables or repositories can be shared with other CPs and the information is synchronized so that all CPs always have access to up-to-date information about all DNs on the network.

For example, the FCC may decide to allocate a portion of the spectrum for unlicensed use and the proposed system may be designed to operate in the spectrum. Due to the lack of spectrum, the FCC may then need to allocate a portion of the spectrum to licensed use for commercial telecommunications vendors (ie, AT&T, Verizon or Sprint), defense or public safety. In conventional wireless systems, this coexistence will be impossible because existing wireless devices operating in the unlicensed band will cause harmful interference to the licensed RF transceiver. In the system proposed by us, the decentralized nodes exchange control information with CPs 4901 through 4903 to adapt their RF transmissions to accommodate the evolved band plan. In an embodiment, DNs 4911 through 4913 were originally designed to operate on different frequency bands within the available spectrum. When the FCC assigns one or more parts of the spectrum to a licensed operation, the CP exchanges control information with the unlicensed DN and reconfigures the DN to turn off the band for licensed use, so that the unlicensed DN does not interfere with the licensed DN . This situation is depicted in Figure 50 , where the unlicensed node (e.g., 5002) is indicated by a solid circle and the licensed node (e.g., 5001) is indicated by a hollow circle. In another embodiment, the entire spectrum can be assigned to a new licensed service and control information used by the CP to turn off all unlicensed DNs to avoid interfering with the licensed DN. This situation is illustrated in Figure 51 , where an outdated unlicensed node is covered by a cross.

By another example, it may be necessary to limit the power emissions of certain devices operating in a given frequency band to meet FCC exposure limits [27]. For example, a wireless system may initially be designed for a fixed wireless link where DNs 4911 through 4913 are connected to an outdoor rooftop transceiver antenna. Subsequently, the same system can be updated to support a DN with an indoor portable antenna to provide better indoor coverage. Due to the possibility of being closer to the human body, FCC exposure limits for portable devices are more limited than for roof transmitters. In this case, the old DN designed for outdoor applications can be reused for indoor applications as long as the transmission power setting is adjusted. In an embodiment of the invention, the DN is designed with a predefined set of transmission power levels, and when the system is upgraded, the CPs 4901 to 4903 send control information to the DNs 4911 to 4913 to select a new power level, thereby satisfying the FCC. Exposure limits. In another embodiment, the DN is manufactured to have only one power transmission setting, and the DNs that exceed the new power transmission level are turned off remotely by the CP.

In an embodiment, CPs 4901 through 4903 periodically monitor all DNs 4911 through 4913 in the network to define their authority to operate as RF transceivers according to a certain standard. Not the latest DNs can be marked as obsolete and removed from the network. For example, the DN operating in the current power limit and frequency band remains active in the network, and all other DNs are shut down. Note that the DN parameter controlled by the CP is not limited to power transmission and frequency band; it can be any parameter that defines the wireless link between the DN and the client device.

In another embodiment of the invention, DNs 4911 through 4913 may be reconfigured to allow for the coexistence of different standard systems within the same frequency spectrum. For example, power transmissions, frequency bands, or other configuration parameters of certain DNs operating in the context of the WLAN may be adjusted to accommodate the adoption of new DNs designed for WPAN applications while avoiding unwanted interference.

DN 4911 to 4913 can be updated to support their standards when developing new wireless standards to enhance data rates and coverage in wireless networks. In one embodiment, the DN is a software-defined radio (SDR) equipped with programmable computing capabilities, such as an FPGA, DSP, CPU, GPU, and/or GPGPU that performs algorithms for baseband signal processing. If the standard is upgraded, the new baseband algorithm can be uploaded from the CP remotely to the DN to reflect the new standard. For example, in one embodiment, the first standard is a CDMA based standard and then it is replaced by OFDM technology to support different types of systems. Similarly, the sampling rate, power, and other parameters can be remotely updated to the DN. This DN's SDR feature allows continuous upgrades to the network when new technologies are developed to improve overall system performance.

In another embodiment, the system described herein is a cloud wireless system consisting of a plurality of CPs, decentralized nodes, and a network interconnecting the CPs and DNs. 52 shows an example of a cloud wireless system in which all nodes (eg, 5203) identified by a solid circle communicate with a CP 5206 via a network 5201, nodes identified by a hollow circle communicate with a CP 5205, and CPs 5205 to 5206 communicate with each other. Communication between. In one embodiment of the invention, the cloud wireless system is a DIDO system and the DN is connected to the CP and exchanges information to periodically or immediately reconfigure system parameters and dynamically adapt to changing conditions of the wireless architecture. In the DIDO system, the CP is a DIDO BTS, the decentralized node is a DIDO decentralized antenna, the network is a BSN, and the plurality of BTSs are interconnected to one another via a DIDO centralized processor as described in our prior patent application [0002-0009].

All DNs 5202 through 5203 within the cloud wireless system can be grouped into different collections. These sets of DNs can simultaneously establish non-interfering wireless links to many client devices, while each set supports different multiple access technologies (eg, TDMA, FDMA, CDMA, OFDMA, and/or SDMA), different modulations ( For example, QAM, OFDM) and/or coding schemes (eg, convolutional coding, LDPC, turbo code). Similarly, each client can be servoed with different multiple access techniques and/or different modulation/coding schemes. Based on the role of the client in the system and its standards for its wireless link, the CPs 5205 through 5206 dynamically select a subset of DNs that can support their standards and are within range of the client device.

references

[1] Wikipedia's "Advanced Mobile Phone System"

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

[2] AT&T's "1946: First Mobile Telephone Call"

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[3] "GSM technology" of GSMA

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[4] ETSI's "Mobile technologies GSM"

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[5] Wikipedia's "IS-95"

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[6]Ericsson's "The Evolution of EDGE"

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

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

[9] Wi-Fi certification makes it Wi-Fi

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

[11] C. Eklund, RBMarks, KLStanwood and S. Wang "IEEE Standard 802.16: A Technical Overview of the WirelessMAN ^TM Air Interface for Broadband Wireless Access"

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

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

[13] March 2006, IEEE Communications Magazine, pages 38 to 45, H. Ekström, A. Furuskär, J. Karlsson, M. Meyer, S. Parkvall, J. Torsner and M. Wahlqvist, "Technical Solutions" For the 3G Long-Term Evolution"

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

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

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

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

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

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

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

Http://www.businessweek.com/magazine/the-edison-of-silicon-valley-07272011.html

[20] June 30, 2011, Wired's "Has OnLive's Steve Perlman Discovered Holy Grail of Wireless?"

Http://www.wired.com/epicenter/2011/06/perlman-holy-grail-wireless/

[21] July 28, 2011, Wall Street Journal's "Silicon Valley Inventor's Radical Rewrite of Wireless"

Http://blogs.wsj.com/digits/2011/07/28/silicon-valley-inventors-radical-rewrite-of-wireless/

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

Http://www.whitehouse.gov/the-press-office/presidential-memorandum-unleashing-wireless-broadband-revolution

[23] September 23, 2010, FCC's "Open commission meeting"

Http://reboot.fcc.gov/open-meetings/2010/september

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

[25] "A bill" at the 1st meeting of the 112th Congress on July 12, 2011

http://republicans.energycommerce.house.gov/Media/file/Hearings/Telecom/071511/DiscussionDraft.pdf

[26] March 2006, IEEE Communications Magazine, pp. 38-45, H. Ekström, A. Furuskär, J. Karlsson, M. Meyer, S. Parkvall, J. Torsner and M. Wahlqvist, "Technical Solutions For the 3G Long-Term Evolution"

[27] August 1997, OET Announcement 65, version 97 to 01, FCC's "Evaluating compliance with FCC guidelines for human exposure to radiofrequency electromagnetic fields"

V. System and method for compensating the Doppler effect in a distributed input distributed output wireless system

In this part of the [Embodiment], we describe a multi-user (MU) multi-antenna system (MAS) for multi-user wireless transmission, which adaptively reconfigures its parameters to compensate for user action The Doppler effect of changes in sexual or communication environments. In one embodiment, the MAS is a Decentralized Input Decentralized Output (DIDO) system as described in the patent application [0002-0016] and in FIG . The DIDO system of one embodiment includes the following components:

̇ User Equipment (UE) : UE 5301 of one embodiment includes a fixed or mobile UE receiving data streams from DIDO backhaul via a downlink (DL) channel and transmitting data via an uplink (UL) channel to DIDO back-loaded RF transceiver

Base Transceiver Station (BTS) : BTS 5310 to 5314 of one embodiment establishes a DIDO backhaul interface with a wireless channel. BTSs 5310 through 5314 are access points consisting of a DAC/ADC and a radio frequency (RF) chain that converts the baseband signal to RF. In some cases, the BTS is a simple RF transceiver equipped with a power amplifier/antenna, and the RF signal is carried to the BTS via RF-over-fiber technology as described in our patent application [0010].

̇ Controller (CTR) : CTR 5320 in one embodiment is a training signal designed for certain dedicated features, such as transmitting time/frequency synchronization for BTS and/or UE, receiving control information from the UE/ A specific type of BTS that transmits control information to the UE and receives channel state information (CSI) or channel quality information from the UE.

̇ Centralized Processor (CP) : The CP 5340 of one embodiment is a DIDO server that establishes an interface for the Internet or other types of external network 5350 and DIDO backhaul. The CP calculates the DIDO fundamental frequency processing and sends the waveform to the decentralized BTS for DL transmission.

Base Station Network (BSN) : An embodiment of the BSN 5330 is a network that connects a CP to a decentralized BTS carrying information for 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 line of sight or non-line of sight wireless link. In addition, the BSN is a proprietary network, or a regional network, or the Internet.

As described in the application in the application, the DIDO system establishes independent channels to multiple users such that each user receives a non-interfering channel. In DIDO systems, this is achieved by using spatial diversity using a decentralized antenna or BTS. In one embodiment, the DIDO system utilizes spatial, polarization, and/or field diversity to increase the degree of freedom within each channel. The increased degree of freedom of the wireless link is used to transmit an independent data stream to an increased number of UEs (i.e., multiplex gain) and/or improved coverage (i.e., diversity gain).

The BTSs 5310 through 5314 are placed at any convenient location on the accessible Internet or BSN. In one embodiment of the present invention, UE 5301 to 5305 are randomly disposed or distributed between BTS antenna, or distributed around the BTS antenna and / or around or distributed by the BTS antenna, as depicted in FIG. 54 .

In one embodiment, BTS 5310 to 5314 sends a training signal and / or independent data streams to UE 5301 via the DL channel, as depicted in FIG. 55. The training signal is used by the UE for different purposes, such as time/frequency synchronization, channel estimation, and/or channel state information (CSI) estimation. In one embodiment of the invention, the MU-MAS DL uses nonlinear precoding, such as dirty paper coding (DPC) [1-2] or Tomlinson-Harashima (TH) [3] -4] Precoding. In another embodiment of the invention, the MU-MAS DL uses non-linear precoding, such as block diagonalization (BD) or zero-forcing beamforming as described in the patent application [0003-0009] in the application. (ZF-BF) [5]. If the number of BTSs is greater than the UE, the additional BTSs are used to increase the link quality to each UE via a diversity scheme, such as antenna selection or eigenmode selection as described in [0002-0016]. If the number of BTSs is less than the UE, the additional UE shares the wireless link with other UEs via conventional multiplexing techniques (eg, TDMA, FDMA, CDMA, OFDMA).

The UL channel is used to transmit data from the UE 5301 to the CP 5340 and/or CSI (or channel quality information) used by the DIDO precoder. In one embodiment, via conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDMA ) UL channel from the UE to the multiplexing CTR (as depicted in FIG. 56) or to the nearest BTS. In another embodiment of the present invention, the spatial processing technique used to distinguish from the UE 5301 to the UL channel distributed BTS 5310 to 5314 of (as depicted in FIG. 57). For example, the UL stream is transmitted from the UE to the DIDO antenna via a Multiple Input Multiple Output (MIMO) multiplexing scheme. The MIMO multiplex scheme involves transmitting independent data streams from the UE and using linear or non-linear receivers at the DIDO antenna to remove co-channel interference. In another embodiment, it is assumed that UL/DL channel reciprocity is established and the channel does not significantly change between DL and UL transmission due to the Doppler effect, using downlink weights on the uplink for demodulation Change the uplink stream. In another embodiment, a maximum ratio combining (MRC) receiver is used on the UL channel to increase the signal quality from each user terminal at the DIDO antenna.

The data, control information, and CSI transmitted via the DL/UL channel are shared between the CP 5340 and the BTSs 5310 to 5314 via the BSN 5330. Known training signals for the DL channel can be stored in the memory at the BTSs 5310 through 5314 to reduce the additional burden on the BSN 5330. Depending on the type of network (ie, wireless to cable, DSL to cable or fiber), there may be insufficient data rates available on the BSN 5330 to exchange information between the CP 5340 and the BTS 5310 to 5314, especially When the baseband signal is delivered to the BTS. For example, assume that the BTS transmits a 10 Mbps independent data stream to each UE over a 5 MHz bandwidth (depending on the digital modulation and FEC write coding scheme used on the wireless link). If the quantized 16 bits are used for the real part and 16 bits are used for the imaginary part, the baseband signal requires a data throughput of 160 Mbps from the CP to the BTS on the BSN. In one embodiment, the CP and BTS are equipped with an encoder and decoder to compress and decompress the information transmitted on the BSN. In the forward link, the precoded baseband data transmitted from the CP to the BTS is compressed to reduce the bits transmitted on the BSN. And the amount of extra burden. Similarly, in the reverse link, the CSI and the profile (transmitted on the uplink channel from the UE to the BTS) are compressed before being transmitted from the BTS to the CP via the BSN. Different compression algorithms are used to reduce the amount of bits and extra burdens transmitted on the BSN, including but not limited to lossless and/or lossy techniques [6].

One feature of the DIDO system used in one embodiment is that the CP 5340 knows the CSI or channel quality information between all BTSs 5310 through 5314 and the UE 5301 to allow for precoding. As explained in [0006], the performance of DIDO depends on the rate at which CSI is delivered to the CP relative to the rate of change of the wireless link. It is well known that the change in the channel complex gain is due to changes in the UE's mobility and/or propagation environment that cause the Doppler effect. The rate of change of the channel is measured based on the channel coherence time (T _c ) which is inversely proportional to the maximum Doppler shift. In order for the DIDO transmission to be performed reliably, the latency due to CSI feedback must be a fraction of the channel coherence time (eg, 1/10 or less). In one embodiment, when the potential of the CSI feedback loop is measured as the time between the transmission time of training and at the CSI UE side precoded data demodulation time, 58 as depicted in FIG.

In a frequency division duplex (FDD) DIDO system, BTSs 5310 through 5314 send CSI training to UE 5301, which estimates the CSI and feeds it back to the BTS. Next, the BTS sends a CSI to the CP 5340 via the BSN, which calculates the DIDO precoded data streams and sends them back to the BTS via the BSN 5330. Finally, the BTS sends the precoded stream to the UE, and the UE demodulates the data. Referring to Figure 58 , the total latency of the DIDO feedback loop is given by 2*T _DL +T _UL +T _BSN +T _CP

Where T _DL and T _UL respectively include the time of establishing, transmitting and processing the downlink and uplink frames, T _BSN is the round-trip delay on the BSN, and T _CP is the CP processing CSI, generating a pre-use for the UE Encoded data streams and schedules are used for the time spent by different UEs currently transmitting. In this case, T _{DL is} multiplied by 2 to take into account the training signal time (from BTS to UE) and the feedback signal time (from UE to BTS). In time division duplex (TDD), if channel reciprocity is available, when the UE sends CSI training to calculate CSI and sends it to the BTS of the CP, the first step is skipped (ie, the CSI training signal is to be used) Transfer from BTS to UE). Therefore, in this embodiment, the total latency of the DIDO feedback loop is T _DL +T _UL +T _BSN +T _CP

The latent time T _BSN depends on the type of BSN being dedicated cable, DSL, fiber optic connection or general internet. Typical values can vary between a fraction of 1 millisecond and 50 milliseconds. If the DIDO processing is implemented at a CP on a dedicated processor such as an ASIC, FPGA, DSP, CPU, GPU, and/or GPGPU, the computation time at the CP can be reduced. Furthermore, if the number of BTSs 5310 through 5314 exceeds the number of UEs 5301, all UEs can be served simultaneously, thereby removing the latency attributed to the multi-user schedule. Therefore, the latency T _CP is negligible compared to the T _BSN . Finally, the transmission and reception processes for DL and UL are typically implemented on ASICs, FPGAs or DSPs with negligible computation time, and if the signal bandwidth is relatively large (eg, over 1 MHz), the frame duration can be Very small (ie, less than 1 millisecond). Therefore, T _DL and T _UL can be ignored compared to T _BSN .

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

Types of ̇BSN: For example, dedicated fiber links typically experience lower latency than cable modems or DSLs. Thus lower latency BSNs are used for highly mobile UEs (eg, cars, trains on highways), while higher latency BSNs are used for fixed wireless or low mobility UEs (eg, home devices, pedestrians, homes) Car in the district)

̇ Type of QoS: For example, BSN can support different types of DIDO or non-DIDO traffic. Service quality (QoS) with different priorities may be defined for different traffic types. For example, the BSN assigns a high priority to the DIDO traffic and assigns a low priority to the non-DIDO traffic. Alternatively, the high priority QoS is assigned to the traffic of the high mobility UE and the low priority QoS is assigned to the UE with low mobility.

̇ Long-term statistics: For example, the traffic on the BSN can be seen as the time of day Significant changes (for example, for the family to use at night and for the office for daytime use). Higher traffic loads can result in higher latency. Then, at different times of the day, BSNs with higher traffic (when they lead to higher latency) are used for low mobility UEs with lower traffic (when they cause lower latency) In case of BSN is used for high mobility UE

̇ Short-term statistics: For example, any BSN can be affected by temporary network congestion that can lead to higher latency. The CP can then adaptively select the BTS for the low mobility UE from the congested BSN (in the case of congestion causing higher latency) and select the remaining BSN (in the case of lower latency) For highly mobile UEs.

In another embodiment of the invention, BTSs 5310 through 5314 are selected based on the Doppler experienced on each individual BTS-UE link. For example, in line of sight (LOS) link B in Figure 59 , the maximum Doppler shift is based on the angle between the BTS-UE links of the well-known equations below ( ) and vehicle speed (v) function

Where λ is the wavelength corresponding to the carrier frequency. Thus, in Figure 59 , in the LOS channel, the Doppler shift is greatest for link A and close to zero for link C. In non-LOS (NLOS), the maximum Doppler shift depends on the direction of the multipath around the UE, but in general due to the decentralized nature of the BTS in the DIDO system, some BTSs will experience a better UE for a given UE. High Doppler (e.g., BTS 5312), while other BTSs will experience lower Doppler (e.g., BTS 5314) for the given UE.

In an embodiment, the CP tracks the Doppler speed on each BTS-UE link and selects only the link with the lowest Doppler offset for each UE. Similar to the technique described in [0002], CP 5340 defines a "user cluster" for each UE 5301. As depicted in FIG. 60, user cluster having a good link quality to the UE (based on a Signal to Noise ratio (SNR) defined threshold) and low Doppler (e.g., based on a predefined Doppler Pro A limited set of BTSs. In Figure 60 , both BTSs 5 through 10 have a good SNR to UE1, but only BTSs 6 through 9 experience a low Doppler effect (e.g., below a specified threshold).

The CP of this embodiment records the SNR of each BTS-UE link and all values of Doppler in a matrix, and for each UE, selects a sub-matrix that satisfies the SNR and the Doppler threshold. In the example depicted in Figure 61 , the sub-matrices are surrounded by C _2,6 , C _2,7 , C _3,9 , C _4,7 , C _4,8 , C _4,9 , and C _5,6 Green dotted line recognition. A DIDO precoding weight for the UE is calculated based on the submatrix. Note that, as shown in the table in FIG. 61, BTS 5 and 7 and 10 may be UE 2,3,4,5 reached. Next, in order to avoid interfering with UE1 when transmitting to other UEs, BTSs 5 and 10 must be turned off or assigned to different orthogonal channels based on conventional multiplexing techniques such as TDMA, FDMA, CDMA or OFDMA.

In another embodiment, the unfavorable Doppler effect on the performance of the DIDO precoding system is reduced via linear prediction, which is a technique for estimating future complex channel coefficients based on past channel estimates. By way of example and not limitation, different prediction algorithms for single-input single-output (SISO) and OFDM wireless systems are proposed in [7-11]. When the future channel complex coefficients are known, it is possible to reduce the error due to outdated CSI. For example, Figure 62 shows channel gain (or CSI) at different times: i) t _CTR is the _CTR in Figure 58 receiving CSI from the UE in the FDD system (or equivalently, the BTS utilizes the TDD system) DL/UL reciprocity estimates the time according to the UL channel CSI); ii) t _CP is the time at which the CSI is delivered to the CP via the BSN; iii) t _BTS is the time at which the CSI is used for precoding on the wireless link. In Figure 62 , we observe that due to the delay T _BSN ( also depicted in Figure 58 ), the CSI estimated at time t _CTR will be obsolete when used for wireless transmission on the DL channel at time t _BTS (also That is, the complex channel gain has changed). One way to avoid this effect due to Doppler is to perform a prediction method at the CP. The CSI estimate available at the time t _CTR at the CP is delayed by T _BSN /2 due to the CTR to CP latency, and corresponds to the channel gain at time t ₀ in FIG . Next, the CP predicts the future channel coefficients at time t ₀ + T _BSN = t _CP using all or part of the CSI estimated before time t ₀ and stored in the memory. If the prediction algorithm has minimal error propagation, the predicted CSI at time t _CP reliably regenerates future channel gains. The time difference between the predicted CSI and the current CSI is called the prediction horizon and is typically scaled with the channel coherence time in the SISO system.

In DIDO systems, the prediction algorithm is more complicated because it estimates the future channel coefficients in both the time domain and the spatial domain. A linear prediction algorithm utilizing the space-time characteristics of a MIMO wireless channel is described in [12-13]. In [13], the performance of the prediction algorithm in the MIMO system (based on the mean square error or MSE measurement) is shown in the higher channel coherence time (ie, the reduction of the Doppler effect) and the lower channel coherence. The distance is improved (due to lower spatial correlation). Therefore, the prediction period (expressed in seconds) of the space-time method is proportional to the channel coherence time and inversely proportional to the channel coherence distance. In the DIDO system, the homology distance is low due to the high spatial selectivity produced by the distributed antenna.

Prediction techniques for predicting future vector channels (i.e., CSI from BTS to UE) using temporal and spatial diversity of the DIDO system are described herein. These embodiments utilize spatial diversity available in the radio channel to obtain negligible CSI prediction errors and extended prediction intervals compared to any existing SISO and MIMO prediction algorithms. An important feature of these techniques is the use of a decentralized antenna in the case where a known distributed antenna receives uncorrelated complex channel coefficients from a decentralized UE.

In one embodiment of the invention, the spatial and temporal predictor is combined with an estimator in the frequency domain to allow CSI prediction for all available subcarriers in a system, such as an OFDM system. In another embodiment of the invention, the DIDO precoding weights (rather than CSI) are predicted based on previous estimates of DIDO weights.

references

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

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

[3] March 1971, Electronics Letters , pp. 138-139, M. Tomlinson's "New automatic equalizer employing modulo arithmetic".

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

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

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

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

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

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

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

[11] December 2005, "Joint Channel Estimation and Prediction for OFDM Systems" in Proc. IEEE Global Telecommunications Conference , St. Louis, MO, ICWong and BLEvans.

[12] July 2004, Proc. IEEE Signal Processing Advances in Wireless Communications , pp. 59-63, M. Guillaud and D. Slock, "A specular approach to MIMO frequency selective channel tracking and prediction".

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

Embodiments of the invention may include various steps as set forth above. The steps may be embodied in machine executable instructions that cause a general purpose or special purpose processor to perform particular steps. For example, the various components in the base station/AP and client devices described above can be implemented as software executing on a general purpose or special purpose processor. In order to avoid obscuring the relevant aspects of the present invention, various well-known personal computer components such as computer memory, hard disk drives, input devices, and the like have been omitted from the drawings.

Alternatively, in one embodiment, the various functional modules and associated steps described herein may be comprised of specific hardware components (such as special application integrated circuits ("ASICs") that contain fixed-line logic for performing the steps. )) or by any combination of stylized computer components and custom hardware components.

In one embodiment, a particular module, such as the encoding, modulation, and signal processing logic 903 described above, can be implemented in a programmable digital signal processor ("DSP") (or group of DSPs) (such as, Use DSPs from Texas Instruments' TMS320x architecture (eg, TMS320C6000, TMS320C5000, etc.). The DSP in this embodiment can be embedded in an add-on card (such as a PCI card) of a personal computer. Of course, a variety of different DSP architectures can be used while still complying with the basic principles of the invention.

The components of the present invention may also be provided as a machine for storing machine executable instructions Readable media. A machine-readable medium can include, but is not limited to, a flash memory, a compact disc, a CD-ROM, a DVD ROM, a RAM, an EPROM, an EEPROM, a magnetic or optical card, a propagation medium, or other type of machine suitable for storing electronic instructions. Read the media. For example, the present invention can be transmitted from a remote computer (eg, a server) to a data signal embodied in a carrier wave or other propagation medium via a communication link (eg, a data machine or a network connection). Request a computer program (for example, the client) to download.

Numerous specific details are set forth to provide a thorough understanding of the systems and methods of the invention. However, it will be apparent to those skilled in the art that the systems and methods can be practiced without some of the specific details. Therefore, the scope and spirit of the present invention should be judged based on the scope of the following claims.

In addition, numerous publications are cited to provide a more thorough understanding of the invention. All of these cited references are hereby incorporated by reference.

5301‧‧‧User Equipment (UE)

5310‧‧‧Base Transceiver Station (BTS)

5311‧‧‧Base Transceiver Station (BTS)

5312‧‧‧Base Transceiver Station (BTS)

5313‧‧‧Base Transceiver Station (BTS)

5314‧‧‧Base Transceiver Station (BTS)

5320‧‧‧Controller (CTR)

5330‧‧‧Base Station Network (BSN)

5340‧‧‧Centralized Processor (CP)

5350‧‧‧External network

Claims (45)

A multi-user (MU) multi-antenna system (MAS) comprising: one or more centralized units communicatively coupled to a plurality of decentralized transceiver stations or antennas via a network; the network comprising wired or wireless a link or a combination of the two for use as a backhaul communication channel; the one or more centralized units communicate with the distributed transceiver stations or antennas via the network to adaptively reconfigure the Communication between the decentralized transceiver station or antenna and the user to compensate for the Doppler effect due to changes in the user's mobility or propagation environment; wherein the MU-MAS system includes user equipment (UE), base transceiver One or more sets of stations (BTS), controllers (CTRs), centralized processors (CPs), and base station networks (BSNs); and wherein the CP is adaptable based on latency on the BSN These BTSs are selected for low or high mobility UEs. A system as claimed in claim 1, which uses a decentralized antenna that utilizes spatial, polarization and/or field diversity to enhance the data rate and/or coverage of one or more users in the wireless system. The system of claim 1, wherein the UEs are located around or between the distributed antennas or are surrounded by the distributed antennas. A system as claimed in claim 1, wherein the MU-MAS uses complex weights at the receiver of the uplink channel to demodulate independent streams (e.g., data or CSI) from the UEs. The system of claim 4, wherein the complex weights of the uplink receiver are derived from downlink precoding weights or via a maximum ratio combining receiver. A system as claimed in claim 1, wherein the CP and the BTSs are equipped with an encoder/decoder to compress/decompress information exchanged therebetween via the BSN. The system of claim 6, wherein the precoded baseband data streams are compressed before being transmitted from the BTS to the MU-MAS distributed antennas/from the MU-MAS distributed antennas to the BTS Reduce the extra burden on the BSN. The system of claim 2, wherein the adaptation is based on a high data rate versus a low data rate BSN type, or QoS, or average traffic statistics (eg, day or night use of different networks), or on the BSN Instantaneous traffic statistics (for example, temporary network congestion). A method for compensating for a Doppler effect in a multi-user (MU) multi-antenna system (MAS), the MU-MAS comprising at least one centralized unit communicatively coupled to a plurality of via a network a decentralized base transceiver station (BTS), the method comprising: measuring a Doppler speed of a first mobile user relative to the plurality of BTSs; and the centralized unit communicating with the BTSs via the network to adapt Reconfiguring the communication between the BTSs and the user, wherein adaptively reconfiguring includes measuring the Doppler velocity relative to the other BTSs based on a first BTS to dynamically assign the first The mobile user goes to the first BTS or a first BTS set of the plurality of BTSs. The method of claim 9, wherein if the first mobile user has a measured Doppler speed that is relatively higher than a second mobile user, the dynamic assignment includes assigning the first mobile user to a first BTS or a first BTS set, and assigning the second mobile user to a second BTS or a second BTS set, the first BTS or the second BTS or the second BTS set The first set of BTSs has a relatively low associated latency. The method of claim 9, wherein the latency comprises (a) a time taken to transmit a first training signal from the first mobile user to a BTS, and (b) in a BTS connection a round-trip delay on a base station network (BSN) of a centralized processor (CP), and (c) processing, by the CP, a channel of the wireless channel between the BTS and the first mobile user Status information (CSI), based on the CSI, generating a pre-coded data stream for the first mobile user and scheduling the time spent transmitting the different mobile users including the currently transmitted first user. The method of claim 11, wherein the latent time further comprises a time taken to transmit a second training signal from the BTS to the first mobile user. The method of claim 9, wherein the dynamically assigning further comprises a link quality based on a communication channel between each BTS and the first mobile user and associated with the first user with respect to each BTS The measurement is performed by a combination of measured Doppler speeds. The method of claim 13, wherein for a given Doppler speed, the BTS having a relatively high link quality is selected. The method of claim 13, wherein for a given link quality, the BTS having a relatively low Doppler speed is selected. The method of claim 9, further comprising: estimating future complex channel coefficients based on past complex channel coefficients to compensate for adverse Doppler effects of communications between the BTSs and the first mobile user. The method of claim 16, wherein the linear prediction is used for estimation. The method of claim 9, wherein the first mobile user is dynamically assigned to the first BTS or a first BTS set based on both the Doppler speed and the channel state information (CSI), the CSI defining The quality of the communication channel between the user of the action and each of the plurality of BTSs. The method of claim 18, further comprising: constructing, for each of the plurality of BTSs, a matrix of Doppler speeds and link qualities relative to the first mobile user; and A BTS is selected that has a Doppler speed below a specified threshold and a link quality above a specified threshold. A multi-user (MU) multi-antenna system (MAS) for compensating for the Doppler effect, comprising: a plurality of base transceiver stations (BTSs); a network coupled to the plurality of BTSs to at least one centralized a processor (CP); a first mobile user that establishes a communication link with each of the BTSs; the CP communicates with the BTSs via the network to adaptively reconfigure the Communication between the BTS and the user, wherein adaptively reconfiguring includes measuring a Doppler speed of a first mobile user relative to each of the BTSs, and based on a first BTS relative to The Doppler speed measured by the other BTS dynamically assigns the first mobile user to the first BTS of the plurality of BTSs. The system of claim 20, wherein if the first mobile user has a relatively high measured Doppler speed compared to a second mobile user, dynamically assigning includes assigning the first action to use Up to a first BTS and assigning the second mobile user to a second BTS, the first BTS has a relatively lower associated latency than the second BTS. The system of claim 20, wherein the latency comprises (a) a time taken to transmit a first training signal from the first mobile user to a BTS, and (b) to connect the BTS to a centralized Round-trip delay on the base station network (BSN) of the processor (CP), and (c) processing, by the CP, channel state information (CSI) of the wireless channel between the BTS and the first mobile user And generating, based on the CSI, a time taken for the pre-coded data stream of the first mobile user and scheduled to be transmitted by a different mobile user including the currently transmitted first user. The system of claim 22, wherein the latent time further comprises a second training signal The time it takes for the BTS to transmit to the first mobile user. The system of claim 20, wherein the dynamically assigning further comprises a link quality based on a communication channel between each BTS and the first mobile user and associated with the first user with respect to each BTS The measurement is performed by a combination of measured Doppler speeds. A system as claimed in claim 24, wherein for a given Doppler speed, the BTS having a relatively high link quality is selected. A system as claimed in claim 24, wherein for a given link quality, the BTS having a relatively low Doppler speed is selected. A system of claim 20, wherein the CP estimates future complex channel coefficients based on past complex channel coefficients to compensate for adverse Doppler effects of communications between the BTSs and the first mobile user. A system as claimed in item 27, wherein a linear prediction is used for the estimation. The system of claim 20, wherein the first mobile user is dynamically assigned to the first BTS based on the Doppler speed and the channel status information (CSI), the CSI defining the mobile user and the The quality of the communication channel between each of the plurality of BTSs. The system of claim 29, wherein the CP performs the additional operation of: constructing, for each of the plurality of BTSs, a matrix of Doppler speeds and link qualities relative to the first mobile user; and selecting A BTS having a Doppler speed below a specified threshold and a link quality above a specified threshold. A multi-user (MU) multi-antenna system (MAS) comprising: one or more centralized units communicatively coupled to a plurality of decentralized transceiver stations or antennas via a network; the network comprising wired or wireless Link or a combination of the two, used as a back load a communication channel; the one or more centralized units communicate with the decentralized transceiver stations or antennas via the network to adaptively reconfigure communication between the decentralized transceiver stations or antennas and the user Compensating for the Doppler effect due to changes in the user's mobility or propagation environment; wherein the MU-MAS system includes user equipment (UE), base transceiver station (BTS), controller (CTR), centralized processor One or more sets of (CP) and base station networks (BSNs); and wherein the CP adaptively selects the BTSs for low or high motion based on the Doppler speed of each BTS-UE link Sex UE. A system as claimed in claim 31, which uses a distributed antenna that utilizes spatial, polarization and/or field diversity to enhance the data rate and/or coverage of one or more users in the wireless system. The system of claim 31, wherein the UEs are located around or between the distributed antennas or are surrounded by the distributed antennas. A system as claimed in claim 31, wherein the MU-MAS uses complex weights at the receiver of the uplink channel to demodulate independent streams (e.g., data or CSI) from the UEs. The system of claim 34, wherein the complex weights of the uplink receiver are derived from downlink precoding weights or via a maximum ratio combining receiver. The system of claim 31, wherein the CP and the BTSs are equipped with an encoder/decoder to compress/decompress information exchanged therebetween via the BSN. The system of claim 36, wherein the precoded baseband data streams are compressed before being transmitted from the BTS to the MU-MAS distributed antennas/from the MU-MAS distributed antennas to the BTS Reduce the extra burden on the BSN. A multi-user (MU) multi-antenna system (MAS) comprising: one or more centralized units communicatively coupled to a plurality of decentralized transceiver stations or antennas via a network; the network comprising wired or wireless a link or a combination of the two for use as a backhaul communication channel; the one or more centralized units communicate with the distributed transceiver stations or antennas via the network to adaptively reconfigure the Communication between the decentralized transceiver station or antenna and the user to compensate for the Doppler effect due to changes in the user's mobility or propagation environment; wherein the MU-MAS system includes user equipment (UE), base transceiver One or more sets of stations (BTS), controllers (CTRs), centralized processors (CPs), and base station networks (BSNs); and wherein linear predictions are used to estimate future CSIs or such MUs The MAS precodes the weights, thereby eliminating the adverse Doppler effect on the performance of the MU-MAS. The system of claim 38, wherein the linear prediction is used in a time domain, a frequency domain, and/or a spatial domain. The system of claim 38, which uses a decentralized antenna that utilizes spatial, polarization, and/or field diversity to enhance the data rate and/or coverage of one or more users in the wireless system. The system of claim 38, wherein the UEs are located around or between the distributed antennas or are surrounded by the distributed antennas. The system of claim 38, wherein the MU-MAS uses complex weights at the receiver of the uplink channel to demodulate independent streams (e.g., data or CSI) from the UEs. The system of claim 42, wherein the complex weights of the uplink receiver are derived from downlink precoding weights or via a maximum ratio combining receiver Count. The system of claim 38, wherein the CP and the BTSs are equipped with an encoder/decoder to compress/decompress information exchanged therebetween via the BSN. The system of claim 44, wherein the precoded baseband data streams are compressed before being transmitted from the BTS to the MU-MAS distributed antennas/from the MU-MAS distributed antennas to the BTS Reduce the extra burden on the BSN.