CN111262613A

CN111262613A - System and method for handling doppler effect in distributed input-distributed output wireless systems

Info

Publication number: CN111262613A
Application number: CN202010055466.7A
Authority: CN
Inventors: 安东尼奥·福伦扎; 斯蒂芬·G·珀尔曼
Original assignee: Rearden LLC
Current assignee: Rearden LLC
Priority date: 2012-05-04
Filing date: 2013-05-03
Publication date: 2020-06-09
Also published as: SG11201407381VA; TW201407987A; JP6542300B2; IL272481B2; EP2845178A4; BR112014027631A2; HK1209521A1; TWI591976B; EP2845178A1; IL272481A; TW202015357A; CN104603853B; CN104603853A; TW202243428A; JP2017225138A; RU2649078C2; JP2015526917A; TWI756605B; KR20150016299A; AU2013256044A1

Abstract

The present invention relates to a system and method for handling the doppler effect in a distributed input-distributed output wireless system. Systems and methods are disclosed that compensate for the adverse effects of Doppler on DIDO system performance. One embodiment of such a system employs different selection algorithms to adaptively adjust the active BTSs for different UEs based on tracking the changing channel conditions. Another embodiment utilizes channel prediction to estimate future CSI or DIDO precoding weights to eliminate errors due to outdated CSI.

Description

System and method for handling doppler effect in distributed input-distributed output wireless systems

Related information of divisional application

The scheme is a divisional application. The parent of the divisional patent is invention patent application No. 201380035543.0 entitled "system and method for processing doppler effect in distributed input-distributed output wireless system" after international application with application date of 2013, 05 and 03, and application number of PCT/US2013/039580 entered china.

Related patent application

This patent application is a continuation-in-part application of the following co-pending U.S. patent applications:

U.S. patent application serial No. 12/917,257 entitled "Systems And Methods for coordinating transmissions In a Distributed Wireless system Via User Clustering", filed 11/1/2010; united states patent application serial No. 12/802,988 entitled "Interference Management, handover, Power Control In Distributed-Input Distributed-output (DIDO) communication systems" (Interference Management, Handoff, Power Control and link Adaptation In Distributed Input Distributed Output (DIDO) communication system) filed on 16.6.2010; U.S. patent application serial No. 12/802,976 entitled "System and method For Adjusting DIDO Interference Cancellation Based On Signal strength measurements", filed On 16.6.2010, U.S. patent publication No. 8,170,081, published On 1.5.2012; U.S. patent application Ser. No. 12/802,974 entitled "System And Method For Managing Inter-Cluster Multi DIDO Clusters" (System And Method For Managing Inter-Cluster Handoff Of Clients across Multiple DIDO Clusters), filed on 16.6.2010; U.S. patent application Ser. No. 12/802,989 entitled "System And Method For Managing Handoff Of A Client Between differentiated Distributed-Input-Distributed-output (DIDO) network Based On Detected Client speeds" (System And Method For Managing Handoff Of a Client Between different Distributed Input Distributed Output (DIDO) Networks), filed On 16.2010, month 6; U.S. patent application serial No. 12/802,958 entitled "System And Method For Power Control And Antenna Grouping In a Distributed Input Distributed Output (DIDO) Network" (System And Method For Power Control And Antenna Grouping In a Distributed Input Distributed Output (DIDO) Network), filed on 16.6.2010; U.S. patent application serial No. 12/802,975 entitled "System And Method For Link adaptation In DIDO multicarrier Systems" (System And Method For Link adaptation In DIDO multicarrier Systems) filed on 16.2010, 6/month; U.S. patent application serial No. 12/802,938 entitled "System And Method For didopreciding Interpolation In Multicarrier Systems" (System And Method For DIDO precoding Interpolation In Multicarrier Systems) filed on 16.2010, 6.13; U.S. patent application serial No. 12/630,627 entitled "System and Method For Distributed Antenna Wireless Communications" filed 12, 3, 2009; U.S. patent application serial No. 12/143,503 entitled "System and Method For Distributed Input-Distributed Output wireless communications" (System and Method For Distributed Input-Distributed Output wireless communications) filed on 20.6.2008, now U.S. granted patent 8,160,121 published on 17.4.2009; U.S. patent application serial No. 11/894,394 entitled "System and Method for Distributed Input Distributed output wireless Communications" filed on 8/20/2007, now U.S. granted patent 7,599,420 published on 10/6/2009; U.S. patent application serial No. 11/894,362 entitled "System and method for Distributed Input-Distributed wireless Communications" filed on 8/20/2007, now U.S. granted patent 7,633,994 published on 12/15/2009; U.S. patent application serial No. 11/894,540 entitled "System and Method For Distributed Input-Distributed output wireless Communications" filed on 20.8.2007, now U.S. granted patent No.7,636,381 published on 22.12.2009; U.S. patent application serial No. 11/256,478 entitled "System and Method For Spatial-multiplexed tropospheric Scatter Communications" filed on 21/10/2005, now U.S. granted patent 7,711,030 published on 4/5/2010; U.S. patent application serial No. 10/817,731 entitled "System and Method For Enhancing Near vertical incidence sky wave (" NVIS ") Communication Using Space-Time Coding" (System and Method For Enhancing Near vertical incidence sky wave ("NVIS") Communication) filed on

day

2, 4/2004, and U.S. granted patent No.7,885,354, published on day 28, 2011.

Background

A prior art multi-user wireless system may comprise only a single base station or several base stations.

A single WiFi base station (e.g., utilizing 2.4GHz 802.11b, g, or n protocols) connected to a broadband wired internet connection in an area where there are no other WiFi access points (e.g., WiFi access points connected to DSL in rural user homes) is an example of a relatively simple multi-user wireless system of a single base station shared by one or more users within its transmission range. If a user is in the same room as a wireless access point, the user will typically experience a high speed link with little transmission interruption (e.g., there may be packet loss due to a 2.4GHz jammer (e.g., microwave oven), but not due to spectrum sharing with other WiFi devices), and if the user is a medium distance away or there are several obstacles in the path between the user and the WiFi access point, the user will likely experience a medium speed link. If a user is approaching the edge of the range of a WiFi access point, the user will likely experience a low speed link and may experience periodic interruptions if the change in the channel causes the signal SNR to drop below the available level. And eventually, if the user is out of range of the WiFi base station, the user will have no link at all.

When multiple users access the WiFi base station simultaneously, the available data throughput is shared among them. Different users will typically place different throughput demands on the WiFi base station at a given time, but sometimes when the aggregate throughput demand exceeds the available throughput from the WiFi base station to the user, then some or all of the users will receive less data throughput than they are seeking. In the extreme case where a WiFi access point is shared among a very large number of users, the throughput to each user may slow down to the speed of peristalsis, and worse, the data throughput to each user may arrive in short pulses separated by long periods of no data throughput at all, during which other users are served. This "intermittent" data transfer may impair certain applications like media streaming.

Adding additional WiFi base stations in situations with a large number of users would only be helpful to some extent. Within the 2.4GHz 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, the aggregate throughput of the coverage area between multiple users will increase by up to 3 times. But in addition to this, adding more WiFi base stations in the same coverage area will not increase the aggregate throughput because they will start sharing the same available spectrum among them, effectively utilizing Time Division Multiple Access (TDMA) by "taking turns" using that spectrum. This situation is common in coverage areas with high population densities (such as in multi-dwelling units). For example, a user in a large public building with WiFi adapters may experience significantly poor throughput due to many other interfering WiFi networks (e.g., in other apartments) serving other users in the same coverage area, even if the user's access point is in the same room as the client device accessing the base station. While link quality may be good in the 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 limitations include coverage area, Downlink (DL) data rate, and Uplink (UL) data rate. A key goal of next generation wireless systems, such as WiMAX and LTE, is to improve coverage area and DL and UL data rates via multiple-input multiple-output (MIMO) technology. MIMO uses multiple antennas at the transmit and receive sides of a wireless link to improve link quality (resulting in wider coverage) or data rate (by creating 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), it may be desirable to exploit channel spatial diversity in accordance with multi-user MIMO (MU-MIMO) techniques to create non-interfering channels to multiple users (rather than a single user). See, for example, the following references:

the term "On the acceptable throughput of a multiantenna gaussian broadcast channel" in "category of IEEE trans info th, vol.49, pp.1691-1706, July 2003(g. caire and s.shami," achievable throughput for a multiantenna gaussian broadcast channel "," IEEE theories of information ", volume 49, page 1691-1706, month 7 2003).

P.viswanath and d.tse, "Sum capacity of the vector Gaussian broadcast and uplink-downlink duty," IEEE trans. info.th., vol.49, pp.1912-1921, aug.2003(p.viswanath and d.tse, "total capacity of vector Gaussian broadcast channel and duality of uplink and downlink", "IEEE theories of information," volume 49, page 1912 1921, month 2003 8).

Vishwatath, n.jindal, and a.goldsmith, "duty, achievable rates, andcum-rate capacity of Gaussian MIMO channels," IEEE trans. info. th., vol.49, pp.2658-2668, oct.2003 (s.vishwatath, n.jindal, and a.goldsmith, "Duality, achievable rates and total rate capacities of MIMO broadcast channels", "IEEE theories of information, volume 49, page 2658 + 2668, month 10 2003).

Yu and J.Cioffi, "Sum capacity of Gaussian vector broadcasthannels," IEEE trans. Info. Th., vol.50, pp.1875-1892, Sep.2004(W.Yu and J.Cioffi, "Total Capacity of Gaussian vector broadcast channel", "IEEE theoretical institute of information", volume 50, page 1875-1892, 9. 2004).

M.costa, "Writing on dirty paper," IEEE Transactions on information theory, vol.29, pp.439-441, May 1983(M.costa, "Writing on dirty paper", IEEE information theories, Vol.29, p.439-441, 5 months 1983).

M. Bengtsson, "A pragmatic approach to multi-user spatial multiplexing," Proc. of Sensor Array and Multichannel Signal processing symposium, pp.130-134, Aug.2002(M. Bengtsson, "practical methods for Multi-user spatial multiplexing," Sensor Array and Multichannel Signal processing symposium, pp.130-.

K. -k.wong, r.d.mutch, and k.b.letaief, "Performance enhancement of multi-user MIMO wireless communication systems," IEEE trans.comm., vol.50, pp.1960-1970, dec.2002 (k.k.wong, r.d.mutch, and k.b.letaief, "Performance enhancement of MIMO wireless communication systems", "IEEE communication journal", volume 50, p.1960-1970, 2002, 12 months).

Sharif and B.Hastibi, "On the capacity of MIMO broadcast channel with partial side information," IEEE trans. info.Th, vol.51, pp.506-522, Feb.2005(M.Sharif and B.Hastibi, "capacity for MIMO broadcast channel with partial side information", "IEEE information theories proceedings, Vol.51, p.506-522, p.2005, 2.2005).

For example, in a MIMO 4 x 4 system (i.e., four transmit antennas and four receive antennas) with 10MHz bandwidth, 16-QAM modulation, and Forward Error Correction (FEC) coding at 3/4 rate (yielding a spectral efficiency of 3 bps/Hz), the ideal peak data rate achievable at the physical layer for each user is 4 x 30Mbps — 120Mbps, which is much higher than the rate required to deliver high definition video content (which may only require about 10 Mbps). In a MU-MIMO system with four transmit antennas, four users, and a single antenna for each user, in the ideal case (i.e., an independent and equally distributed (i.i.d.) channel), the downlink data rate can be shared among the four users and channel spatial diversity can be exploited to create four parallel 30Mbps data links to the users.

Different MU-MIMO schemes have been proposed as part of the LTE standard, as described, for example, in the following documents: 3GPP, "Multiple Input Multiple Output in UTRA", 3GPP TR25.876V7.0.0, mar.2007(3GPP, "Multiple Input Multiple Output in UTRA", 3GPP TR25.876V7.0.0, 3 month 2007); 3GPP, "Base physics channels and modulation", TS 36.211, v8.7.0, May 2009(3GPP, "basic physical channel and modulation", TS 36.211, v8.7.0, 5 months 2009); and 3GPP, "Multiplexing and channel coding", TS36.212, v8.7.0, May 2009(3GPP, "Multiplexing and channel coding", TS36.212, v8.7.0, 5 months 2009, however, these schemes can only provide up to 2 times improvement in DL data rate through four transmit antennas, actual implementation of MU-MIMO technology in standard and proprietary cellular systems by companies like early communication (ArrayComm) (see, e.g., early communication (ArrayComm), "Field-protected resources" (Field validation results), http:// www.arraycomm.com/service.ph page?produces up to about 3 times increase in DL data rate by multiple access (SDMA) (through four transmit antennas), the key spatial limitation of MU-MIMO schemes in MIMO networks is the lack of spatial diversity in the transmit side and the use of multi-path wireless link in spatial diversity systems with spatial diversity and angular spread of the spatial spreading of the wireless links, the transmit antennas at the base station are typically grouped together and placed only one or two wavelengths apart due to a limited footprint on the antenna support structure (referred to herein as a "tower," whether physically tall or not tall) and due to limitations on where the towers may be located. In addition, because cell towers are typically placed high (10 meters or more) above an obstacle to produce wider coverage, the multipath angular spread is low.

Other practical issues with cellular system deployment include excessive cost of cellular antenna location and limited availability of location (e.g., due to municipal restrictions on antenna placement, cost of real estate, physical obstructions, etc.), as well as cost and/or availability of network connections to the transmitter (referred to herein as "backhaul"). Furthermore, cellular systems are often due to the loss of walls, ceilings, floors, furniture, and other obstructions that make it difficult to reach clients located deep in a building.

Indeed, the entire concept of cellular architecture for wide area wireless networks presupposes fairly fixed placement of cellular towers, alternation of frequencies between adjacent cells, and frequent sectorization in order to avoid interference between transmitters (base stations or users) using the same frequency. Thus, a given sector of a given cell eventually becomes a shared block of DL and UL spectrum among all users in that cell sector, which is then shared among these users primarily only in the time domain. For example, cellular systems based on Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA) both share the spectrum among users in the time domain. By covering such cellular systems with sectorization, it may be possible to achieve 2-3 times the spatial domain benefit. And then by covering such cellular systems with MU-MIMO systems (such as those described previously), it may be possible to achieve additional 2-3 times the space-time domain benefit. However, given that the cells and sectors of a cellular system are typically in fixed locations (often specified by locations where towers may be placed), even these limited benefits are difficult to utilize if the user density (or data rate requirements) does not match well with tower/sector placement at a given time. Cellular smart phone users typically experience the following results: today the user may talk in the phone or download the web page completely without any problems and then after driving (or even walking) to a new location will suddenly find the speech quality to decrease or the web page to slow down to a peristaltic speed, or even lose the connection completely. However, on different days, the user may encounter the exact opposite in each location. Given the same environmental conditions, a situation that users may be experiencing is the fact that the user density (or data rate requirements) is highly variable, but the total spectrum available (and hence the total data rate, using prior art techniques) to be shared between users at a given location is largely fixed.

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

So, in summary, prior art cellular systems may lose perhaps 3 times the spectrum utilization due to the honeycombing, and may boost perhaps 3 times the spectrum utilization by sectorization and perhaps 3 times again via MU-MIMO techniques, resulting in a net 3 × 3/3-3 times the possible spectrum utilization. The bandwidth is then typically divided among users in the time domain based on which sector of which cell the user belongs to at a given time. There are even further inefficiencies 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 rates vary depending on the quality of the link between the user and the base station. For example, users further from a cellular base station will typically have a smaller available data rate than users closer to the base station. Since data rates are typically shared among all users in a given cellular sector, the result of this situation is that all users are subject to high data rate demands from distant users with poor link quality (e.g., at the edge of a cell), as these users will still demand the same amount of data rate, yet they will consume more of the shared spectrum to get the data rate.

Other proposed spectrum sharing systems, such as those used by WiFi (e.g., 802.11b, g, and n) and proposed by White space alliance (White space alliance), share the spectrum very inefficiently because simultaneous transmissions by base stations within range of the users cause interference and thus the systems utilize collision avoidance and sharing protocols. These spectrum sharing protocols are in the time domain and therefore when there is a large number of interfering base stations and users, the base stations are collectively limited to time-domain sharing of the spectrum between each other, regardless of the efficiency of each base station itself in terms of spectrum utilization. Other prior art spectrum sharing systems similarly rely on similar approaches to mitigate interference between base stations, whether cellular base stations with antennas on towers or small scale base stations, such as WiFi Access Points (APs). These methods include: limiting the transmit power from the base station to limit the range of interference; beamforming (via synthetic or physical means) to narrow the region of interference; time domain multiplexing of the spectrum; and/or MU-MIMO technology with multiple clustered antennas on user equipment, base stations, or both. Also, with advanced cellular networks that are scheduled or under planning today, many of these technologies are often used simultaneously.

However, as is evident from the fact that even advanced cellular systems can only achieve approximately a 3-fold increase in spectrum utilization compared to single user utilization, all of these techniques are ineffective at increasing the aggregate data rate between shared users in a given coverage area. In particular, as a given coverage area scales on a user's 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 cellular systems, to increase the aggregate data rate within a given area, cells are typically subdivided into smaller cells (commonly referred to as micro-cells (nano-cells) or femto-cells (femto-cells)). These small cells can become extremely expensive given the limitations on where the towers can be placed, and the requirement that the towers must be placed in an appropriately structured pattern in order to provide coverage with a minimum "dead zone", yet avoid interference between adjacent cells using the same frequency. In essence, the coverage area must be mapped out, the available locations for placing towers or base stations must be identified, and then in view of these constraints, the designer of the cellular system must try to do so as much as it is best. And, of course, if user data rate requirements increase over time, the cellular system designer must again redraw the coverage area, try to find the location of the tower or base station, and again operate within the constraints of the environment. Also, there is often no good solution at all, resulting in dead zones or insufficient aggregate data rate capacity in the coverage area. In other words, the stringent physical placement requirements for cellular systems to avoid interference between towers or base stations utilizing the same frequency result in significant difficulties and constraints in cellular system design, and often fail to meet user data rate and coverage requirements.

So-called prior art "cooperative" and "cognitive" radio systems seek to increase spectrum utilization in a given area by using intelligent algorithms within the radio to enable the radio to minimize interference between each other and/or to enable the radio to potentially "listen" to other spectrum usage in order to wait until the channel is clear. Such systems are proposed to be used in particular in unlicensed spectrum in order to increase the spectrum utilization of this spectrum.

Mobile ad hoc networks (MANETs) (see http:// en. wikipedia. org/wiki/Mobile _ ad _ hoc _ network) are examples of cooperative self-configuring networks intended for providing peer-to-peer communication, and may be used to create communication between radios without cellular infrastructure, and with sufficiently low power communication may potentially mitigate interference between simultaneous transmissions that are out of range of each other. A large number of routing protocols have been proposed and implemented for MANET systems (see http:// en. wikipedia. org/wiki/List of _ ad-hoc _ routing _ protocols for a listing of many routing protocols of various classes), but the common theme between them is that they are all techniques for routing (e.g., repeating) transmissions so as to minimize transmitter interference within the available spectrum in order to achieve a specific efficiency or reliability paradigm goal.

All prior art multi-user wireless systems seek to improve spectrum utilization within a given coverage area by utilizing techniques that allow simultaneous spectrum utilization between a base station and multiple users. Note that in all of these situations, techniques for simultaneous spectrum utilization between a base station and multiple users enable simultaneous spectrum usage by multiple users by mitigating interference between waveforms to the multiple users. For example, in the case where 3 base stations each use a different frequency to transmit to one of 3 users, interference is mitigated therein because the 3 transmissions are at 3 different frequencies. With sectorization from the base station to 3 different users (180 degrees apart each with respect to the base station), interference is mitigated because beamforming prevents 3 transmissions from overlapping at any user.

When such techniques are enhanced by MU-MIMO and, for example, have 4 antennas per base station, then this has the potential to increase downlink throughput by a factor of 4 by creating four non-interfering spatial channels to users in a given coverage area. It is still the case that some techniques must be utilized to mitigate interference between multiple simultaneous transmissions to multiple users in different coverage areas.

Also, as previously mentioned, not only do these prior art techniques (e.g., cellular, sectorization) typically suffer from increasing the cost and/or flexibility of deployment of multi-user wireless systems, but they also typically encounter physical or practical limitations on aggregate throughput in a given coverage area. For example, in a cellular system, there may not be enough available locations to install more base stations to create a smaller cell. Also, in MU-MIMO systems, limited spatial diversity results in diminishing asymptotic gains in throughput as more antennas are added to the base station, given the cluster antenna spacing at each base station location.

And furthermore, in the case of multi-user wireless systems where user location and density are unpredictable, it results in unpredictable throughput (with frequent drastic changes) which is inconvenient for the user and renders some applications (e.g., delivery of services requiring predictable throughput) impractical or of low quality. Therefore, prior art multi-user wireless systems still leave much to be desired in terms of 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 are common topics: transmissions are distributed among different base stations (or ad hoc transceivers) and are structured and/or controlled so as to avoid RF waveform transmissions from different base stations and/or different ad hoc transceivers from interfering with one another at a given user's receiver.

Or, in other words, it is considered a known fact that if a user happens to receive transmissions from more than one base station or ad hoc transceiver at the same time, interference from multiple simultaneous transmissions will result in a reduction in the SNR and/or bandwidth of the signal to the user, 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.

Thus, in multi-user wireless systems, 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 ad hoc transceivers transmitting at the same frequency at the same time. A number of prior art methods exist to avoid such interference, including controlling the physical location of the base station (e.g., cellular), limiting the power output of the base station and/or the ad hoc transceivers (e.g., limiting the transmission range), beamforming/sectorization, and time domain multiplexing. In short, all these spectrum sharing systems seek to address the limitations of multi-user wireless systems, namely: when multiple base stations and/or ad hoc transceivers transmitting simultaneously on the same frequency are received by the same user, the resulting interference is reduced or corrupted to the data throughput of the affected user. If most or all of the users in a multi-user wireless system experience interference from multiple base stations and/or ad hoc transceivers (e.g., in the event of a failure of a component of the multi-user wireless system), it may result in a situation where the aggregate throughput of the multi-user wireless system is drastically reduced or even lost.

Prior art multi-user wireless systems increase complexity and introduce limitations to the wireless network and frequently lead to situations where the experience (e.g., available bandwidth, delay, predictability, reliability) of a given user is impacted by the utilization of the spectrum by other users in the area. Given the increasing demand for aggregated bandwidth within a wireless spectrum shared by multiple users, and the growing applications that may rely on reliability, predictability, and low latency of multi-user wireless networks for a given user, it is apparent that prior art multi-user wireless technologies suffer from a number of limitations. Indeed, due to the limited availability of spectrum suitable for certain types of wireless communication (e.g., at wavelengths that can effectively penetrate building walls), it may be the case that prior art wireless technologies will be insufficient to meet the increasing demand for reliable, predictable, and low-latency bandwidth.

The prior art related to the present invention describes beamforming systems and methods for null steering in a multi-user scenario. Beamforming is originally conceived to maximize the received signal-to-noise ratio (SNR) by dynamically adjusting the phase and/or amplitude (i.e., beamforming weights) of the signals fed to the antennas of the array, thereby concentrating the energy towards the user. In a multi-user scenario, beamforming may 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, weights are calculated to create nulls (null) in the direction of the interferer. When beamforming is used at the transmitter in a multi-user downlink scenario, weights are calculated to cancel the inter-user interference in advance and maximize the SINR to each user. Alternative techniques for multi-user systems, such as BD precoding, compute precoding weights to maximize throughput in the downlink broadcast channel. The co-pending patent applications incorporated by reference herein describe the above-described techniques (see co-pending patent applications for specific citations).

Drawings

A better understanding of the present invention can be obtained from the following detailed description in conjunction with the following drawings, in which:

fig. 1 illustrates a main DIDO cluster surrounded by adjacent DIDO clusters in one embodiment of the invention.

Figure 2 illustrates a Frequency Division Multiple Access (FDMA) technique used in one embodiment of the present invention.

Figure 3 illustrates a Time Division Multiple Access (TDMA) technique used in one embodiment of the present invention.

Fig. 4 illustrates different types of interference regions addressed in one embodiment of the present invention.

Figure 5 shows a frame for use in one embodiment of the invention.

Fig. 6 shows a graph showing SER as a function of SNR assuming SIR of 10dB for a target client in the interference region.

Fig. 7 shows a graph showing SER derived by two IDCI-precoding techniques.

Fig. 8 illustrates an exemplary scenario where a target client moves from a primary DIDO cluster to an interfering cluster.

Fig. 9 shows the signal to interference plus noise ratio (SINR) as a function of distance (D).

Fig. 10 shows the Symbol Error Rate (SER) performance for three cases of 4-QAM modulation in a flat fading narrowband channel.

Fig. 11 illustrates a method for IDCI precoding according to an embodiment of the present invention.

Fig. 12 illustrates SINR variation as a function of client distance from the center of the main DIDO cluster in one embodiment.

Fig. 13 illustrates an embodiment in which the SER is derived for 4-QAM modulation.

Fig. 14 shows an embodiment of the invention in which a finite state machine implements a handoff algorithm.

Figure 15 illustrates one embodiment of a handoff strategy in the presence of shadowing.

Fig. 16 shows a hysteresis loop mechanism when switching between any two states of fig. 15.

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

Fig. 18 shows SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios.

Figure 19 shows the variation of MPE power density with distance from the RF radiation source for different values of transmit power according to an embodiment of the invention.

Fig. 20 a-20 b show different profiles for low power and high power DIDO distributed antennas.

Fig. 21 a-21 b show two power profiles corresponding to the configurations in fig. 20a and 20b, respectively.

Fig. 22 a-22 b show the rate profiles for the two cases shown in fig. 21a and 21b, respectively.

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

Fig. 24 illustrates one embodiment of a method for repeating across antenna groups according to a round robin scheduling strategy for transmitting data.

Fig. 25 shows a comparison of uncoded SER performance with power control of antenna grouping with conventional eigenmode selection in U.S. patent No.7,636,381.

Fig. 26 a-26 c show three scenarios where BD precoding dynamically adjusts the precoding weights to account for different power levels on the wireless link between the DIDO antenna and the client.

Fig. 27 shows 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) for DIDO 2 × 2 system.

Fig. 28 shows one embodiment of the channel matrix frequency response for DIDO 2 x 2 with a single antenna per client.

Fig. 29 shows one embodiment of a channel matrix frequency response for DIDO 2 × 2, where there is a single antenna per client for the channel characterized by high frequency selectivity (e.g., where β ═ 1).

Fig. 30 shows exemplary SERs for different QAM schemes (i.e., 4-QAM, 16-QAM, 64-QAM).

Fig. 31 illustrates one embodiment of a method for implementing a Link Adaptation (LA) technique.

Fig. 32 illustrates SER performance for one embodiment of a Link Adaptation (LA) technique.

FIG. 33 shows a table for where N is_FFT64 and L₀The matrix entries in equation (28) vary with OFDM tone index for an 8 DIDO 2 × 2 system.

FIG. 34 shows for L₀＝8、M＝N_tSER versus SNR for 2 transmit antennas and a variable number of P.

FIG. 35 shows the values for different DIDO orders and L₀SER performance of one embodiment of the interpolation method of 16.

FIG. 36 illustrates one embodiment of a system using super clusters, DIDO-clusters, and user clusters.

FIG. 37 illustrates a system with user clustering according to one embodiment of the invention.

Fig. 38 a-38 b illustrate link quality metric thresholds used in one embodiment of the invention.

Fig. 39-41 show examples of link quality matrices for creating user clusters.

Fig. 42 illustrates an embodiment where clients move across different DIDO clusters.

Fig. 43-46 illustrate the relationship between the resolution of a spherical array and its area a in one embodiment of the invention.

Fig. 47 shows the degrees of freedom of a MIMO system in actual indoor and outdoor propagation scenarios.

Fig. 48 shows the degree of freedom in a DIDO system as a function of array diameter.

Fig. 49 illustrates an embodiment that includes multiple Centralized Processors (CPs) and Distributed Nodes (DNs) that communicate over wired or wireless connections.

Fig. 50 shows an embodiment in which the CP exchanges control information with an unlicensed DN and reconfigures them to close the band for licensed use.

Fig. 51 illustrates an embodiment in which the entire spectrum is allocated to a new service and the CP uses control information to turn off all unauthorized DNs to avoid interfering with authorized DNs.

Fig. 52 illustrates one embodiment of a cloud wireless system that includes a plurality of CPs, distributed nodes, and a network interconnecting the CPs and DNs.

Fig. 53-59 illustrate embodiments of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for doppler effects due to changes in user mobility or propagation environment.

Fig. 60 shows a plurality of BTSs, some of which have good SNRs and some of which have low doppler relative to the UE.

Fig. 61 shows one embodiment of a matrix containing values of SNR and doppler for multiple BTS-UE links recorded by the CP.

Fig. 62 shows channel gain (or CSI) at different times according to one embodiment of the invention.

Detailed Description

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

U.S. patent application Ser. No. 13/232,996 entitled "Systems And Methods To explicit Areas of coherence in Wirless Systems" (Systems And Methods for utilizing coherence regions in wireless Systems) filed on 14.9.2011

U.S. patent application serial No. 13/233,006 entitled "Systems and Methods for planar evolution and obsession of multi-user Spectrum" (system and method for the Planned evolution and Obsolescence of multi-user Spectrum) was filed on 14/9.2011.

U.S. patent application Ser. No. 12/917,257 entitled "Systems And Methods To coordinated transmissions In Distributed Wireless Systems Via User Cluster" (System And method for coordinating transmissions In Distributed Wireless Systems by User Clustering), filed 11/1/2010

U.S. patent application Ser. No. 12/802,988 entitled "Interference Management, Handoff, Power control And Link addition In Distributed-Input Distributed-output (DIDO) Communication Systems" (Interference Management, Handoff, power control And Link Adaptation In Distributed Input Distributed Output (DIDO) Communication System) filed on 16.6.2010

U.S. patent application Ser. No. 12/802,976 entitled "System And Method For Adjusting DIDO interference Cancellation Based On Signal Strength Measurements", filed On 16.2010, 6.D. For Adjusting DIDOInterference Cancellation

U.S. patent application Ser. No. 12/802,974 entitled "System And Method For Managing Inter-Cluster Multiple DIDO Clusters" filed on 16.6.2010, month 6

U.S. patent application Ser. No. 12/802,989 entitled "System And Method For Managing Handoff Of active betweent Distributed output-output (DIDO) network based On Detected Client speed" (System And Method For Managing Handoff Of a Client Between Different Distributed Input Distributed Output (DIDO) networks), filed On 16.2010, 6.month And 16

U.S. patent application Ser. No. 12/802,958 entitled "System And Method For Power Control And antenna Grouping In Distributed-Input-Distributed-output (DIDO) Network" (System And Method For Power Control And antenna Grouping In Distributed-Input Distributed-output (DIDO) Network), filed on 16.6.2010

U.S. patent application serial No. 12/802,975 entitled "System And Method For Link adaptation in DIDO Multicarrier Systems" (System And Method For Link adaptation in DIDO Multicarrier Systems) filed on 16.6.2010, month And year

U.S. patent application serial No. 12/802,938 entitled "System And Method For DIDO precoding interpolation In Multicarrier Systems" (System And Method For DIDO precoding interpolation In Multicarrier Systems) filed on 16.6.2010

U.S. patent application serial No. 12/630,627 entitled "System and Method For Distributed antenna wireless Communications" filed 12,2, 2009

U.S. patent No.7,599,420 entitled "System and Method for Distributed Input Distributed Output Wireless Communication" (System and Method for Distributed Input Distributed Wireless Communication) filed on 20/8/2009 and published on 6/10/2009;

U.S. patent No.7,633,994 entitled "System and Method for Distributed Input Distributed Output Wireless Communication" (System and Method for Distributed Input Distributed Wireless Communication) filed on 20/8/2009 and published on 15/12/2009;

U.S. patent No.7,636,381 entitled "System and Method for Distributed Input Distributed Output Wireless Communication" (System and Method for Distributed Input Distributed Wireless Communication) filed on 20/8/2009 and published on 22/12/2009;

U.S. patent application serial No. 12/143,503 entitled "System and Method For Distributed Input-Distributed Output Wireless Communications" (System and Method For Distributed Input-Distributed Output Wireless Communications) filed on 20.6.2008;

U.S. patent application Ser. No. 11/256,478 entitled "System and Method For Spatial-Multiplexed tropospheric Scatterer Communications" (System and Method For spatially multiplexing tropospheric Scatter Communications) filed on 21/10/2005;

U.S. patent No.7,418,053 entitled "System and Method for Distributed Input Distributed Output Wireless Communication" (System and Method for Distributed Input Distributed Wireless Communication) filed on 30.7.2004 and published on 26.8.2008;

U.S. patent application Ser. No. 10/817,731 entitled "System and Method For Enhancing near vertical Incidence sky wave (" NVIS ") Communication," filed on 2.4.2004, and entitled "System and Method For Enhancing near vertical Incidence sky wave (" NVIS ") Communication.

To reduce the space and complexity of the present patent application, the following does not explicitly list the disclosures of some of the related patents and patent applications. Please refer to related patents and patent applications for a complete detailed description of the present disclosure.

It is noted that section I below (the disclosure from related patent application serial No. 12/802,988) uses its own set of headnotes referring to prior art references and prior patent applications assigned to the assignee of the present patent application. The end-note reference is listed at the end of section I (just before the section II header). Citations used in section II may have numerical designations for citations overlapping those used in section I, even where different references are identified by these numerical designations (listed at the end of section II). Thus, references identified by a particular numerical label can be identified in the section that uses that numerical label.

I. Disclosure from related patent application serial No. 12/802,988

1. Method for removing inter-cluster interference

The following describes a wireless Radio Frequency (RF) communication system and method that utilizes multiple distributed transmit antennas to create a location in space with zero RF energy. When M transmit antennas are used, up to (M-1) points of zero RF energy may be created in a predefined location. In one embodiment of the invention, the zero RF energy point is a wireless device and the transmit antennas are aware of the Channel State Information (CSI) between the transmitter and the receiver. In one embodiment, the CSI is calculated at the receiver and fed back to the transmitter. In another embodiment, the CSI is calculated at the transmitter via training from the receiver, assuming channel reciprocity is exploited. The transmitter may utilize the CSI to determine the interfering signals to be transmitted simultaneously. In one embodiment, Block Diagonalization (BD) precoding is used at the transmit antennas to generate the zero RF energy points.

The systems and methods described herein are different from the conventional receive/transmit beamforming techniques described above. Indeed, receive beamforming calculates weights to suppress interference at the receive side (via null steering), whereas some embodiments of the invention described herein apply weights at the transmit side to create interference patterns that result in one or more locations in space with "zero RF energy". Unlike conventional transmit beamforming or BD precoding, which is designed to maximize signal quality (or SINR) or downlink throughput to each user, respectively, the systems and methods described herein minimize signal quality under certain conditions and/or from certain transmitters, creating a point of zero RF energy at a client device (sometimes referred to herein as a "user"). Furthermore, in the context of Distributed Input Distributed Output (DIDO) systems (described in our related patents and patent applications), transmit antennas distributed in space provide a higher degree of freedom (i.e., higher channel spatial diversity) that can be used to create multiple points of zero RF energy and/or maximum SINR to different users. For example, with M transmit antennas, up to (M-1) RF energy points can be created. In contrast, practical beamforming or BD multi-user systems are typically designed to have a dense array of antennas at the transmit side, limiting the number of simultaneous users that can be served over the wireless link for any number M of transmit antennas.

Considering a system with M transmit antennas and K users, where K < M, we assume that the transmitter knows the CSI between M transmit antennas and K users (H ∈ C)^KxM). For simplicity, it is assumed that each user is equipped with a single antenna, but the same approach can be extended to multiple receive antennas per user. Computing precoding weights that create zero RF energy at K user positions (w ∈ C)^Mx1) To satisfy the following conditions

Hw＝0^Kx1

Wherein 0^Kx1Is a vector with all zero terms, and H is a channel vector (H) by which K users will be served from M transmit antennas_k∈C^1xM) The channel matrix obtained by the combination is as follows

In one embodiment, the Singular Value Decomposition (SVD) of the channel matrix H is computed and the precoding weights w are defined as right singular vectors corresponding to the zero subspace of H (identified with zero singular values).

The transmit antennas transmit RF energy using the weight vectors defined above while creating K points of zero RF energy at the locations of the K users, such that the received signal at the K-th user is given by

r_k＝h_kws_k+n_k＝0+n_k

Wherein n is_k∈C^1x1At the k-th userAdditive White Gaussian Noise (AWGN).

In another embodiment, the wireless system is a DIDO system and creates a point of zero RF energy to pre-cancel interference to clients between different DIDO coverage areas. In U.S. patent application serial No. 12/630,627, a DIDO system is described, which includes:

DIDO client

DIDO distributed antenna

DIDO Base Transceiver Station (BTS)

DIDO Base Station Network (BSN)

Each BTS is connected via the BSN to a plurality of distributed antennas that serve a given coverage area known as a DIDO cluster. In this patent application, we describe systems and methods for removing interference between adjacent DIDO clusters. As shown in fig. 1, we assume that the primary DIDO cluster hosts clients (i.e., user devices served by the multi-user DIDO system) that are affected by interference (or target clients) from neighboring clusters.

In one embodiment, the neighboring clusters operate at different frequencies according to a Frequency Division Multiple Access (FDMA) technique similar to conventional cellular systems. For example, with a frequency reuse factor of 3, the same carrier frequency is reused every third DIDO cluster, as shown in fig. 2. In fig. 2, the different carrier frequencies are identified as F₁、F₂And F₃. While this embodiment may be used in some implementations, this solution produces 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. Furthermore, it requires complex cell planning to associate different DIDO clusters with different frequencies, thus preventing interference. Similar to prior art cellular systems, this cellular planning requires specific placement of antennas and limitations of transmit power to avoid interference between clusters using the same frequency.

In another embodiment, adjacent clusters operate in the same frequency band but at different time slots according to a Time Division Multiple Access (TDMA) technique. For example, as shown in FIG. 3, the time slot T is allowed only for certain clusters₁、T₂And T₃DIDO emission as shown. The time slots may be equally allocated to different clusters such that different clusters are scheduled according to a round robin strategy. If different clusters are characterized by different data rate requirements (i.e., clusters in rural areas with a lesser number of clients per coverage area in a crowded urban environment), different priorities are assigned to the different clusters such that more time slots are assigned to clusters with greater data rate requirements. While TDMA as described above may be used in one embodiment of the present invention, the TDMA approach may require time synchronization across different clusters and may result in lower spectral efficiency because interfering clusters cannot use the same frequency at the same time.

In one embodiment, all neighboring clusters transmit in the same frequency band at the same time, and spatial processing across clusters is used to avoid interference. In this embodiment, a multi-cluster DIDO system: (i) conventional DIDO precoding is used within the primary cluster to transmit simultaneous non-interfering data streams to multiple clients within the same frequency band (as described in related patents and patent applications, including 7,599,420; 7,633,994; 7,636,381; and patent application Ser. No. 12/143,503); (ii) DIDO precoding with interference cancellation is used in neighboring clusters to avoid interference to clients located in interference region 8010 in fig. 4 by creating a point of zero Radio Frequency (RF) energy at the location of the target client. If the target client is in the interference zone 410, it will receive the sum of the RF containing data stream from the main cluster 411 and zero RF energy from the interfering

cluster

412 and 413, which will be only the RF containing data stream from the main cluster. Thus, neighboring clusters can use the same frequency at the same time without causing interference to the target clients in the interference region.

In practical systems, the performance of DIDO precoding may be affected by different factors, such as: channel estimation error or doppler effect (generating outdated information at DIDO distributed antennas)Track status information); intermodulation distortion (IMD) in a multi-carrier DIDO system; time or frequency offset. Due to these effects, achieving a zero RF energy point may be impractical. However, as long as the RF energy from the interfering cluster at the target client is negligible compared to the RF energy from the main cluster, the link performance at the target client is not affected by the interference. For example, we assume that the client needs a 20dB signal-to-noise ratio (SNR) to demodulate a 4-QAM constellation using Forward Error Correction (FEC) coding to achieve 10^-6Target Bit Error Rate (BER). If the RF energy received from the interfering cluster is 20dB lower than the RF energy received from the main cluster at the target client, then the interference is negligible and the client can successfully demodulate the data within the predefined BER target. Thus, as used herein, the term "zero RF energy" does not necessarily mean that the RF energy from interfering RF signals is zero. Conversely, this means that the RF energy is sufficiently low relative to the RF energy of the desired RF signal that the desired RF signal can be received at the receiver. Furthermore, although certain desired thresholds of interfering RF energy relative to desired RF energy are described, the underlying principles of the invention are not limited to any particular threshold.

As shown in fig. 4, there are different types of interference regions 8010. For example, a "type a" region (denoted by the letter "a" in fig. 4) is affected by interference from only one neighboring cluster, while a "type B" region (denoted by the letter "B") accounts for interference from two or more neighboring clusters.

Figure 5 shows a frame for use in one embodiment of the invention. The dots represent DIDO distributed antennas, the crosses refer to DIDO clients and the arrows indicate the direction of propagation of RF energy. The DIDO antennas in the primary cluster transmit precoded data signals to the clients MC 501 in the cluster. Likewise, the DIDO antennas in the interference cluster serve the client ICs 502 within the cluster via conventional DIDO precoding. Green cross 503 represents target client TC 503 in the interference zone. The DIDO antennas in the main cluster 511 transmit the precoded data signals to the target clients via conventional DIDO precoding (black arrows). The DIDO antennas in the interfering cluster 512 use precoding to create zero RF energy towards the target client 503 direction (green arrow).

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

Where K is 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 the interference DIDO cluster 412-_cIs the number of clients in interfering cluster c. Furthermore, r_k∈C^NxMFor a vector containing the received data stream at client k, assume that there are M transmit DIDO antennas and N receive antennas at the client device; s_k∈C^Nx1A vector of transmit data streams to client k in the primary DIDO cluster; s_u∈C^Nx1A vector of transmit data streams to client u in the primary DIDO cluster; s_c，i∈C^Nx1A vector of transmit data streams to client i in the c-th interfering DIDO cluster; n is_k∈C^Nx1A vector of Additive White Gaussian Noise (AWGN) at the N receive antennas for client k; h_k∈C^NxMA DIDO channel matrix from M transmit DIDO antennas to N receive antennas at client k in the primary DIDO cluster; h_c，k∈C^NxMA DIDO channel matrix from M transmit DIDO antennas to N receive antennas at client k in the c-th interfering DIDO cluster; w_k∈C^MxNA matrix of DIDO precoding weights to client k in the primary DIDO cluster; w_k∈C^MxNA matrix of DIDO precoding weights for clients u into the primary DIDO cluster; w_c，i∈C^MxNIs a matrix of DIDO precoding weights to clients i in the c-th interfering DIDO cluster.

To simplify notation and without loss of generality, we assume that all clients are equipped with N receive antennas, and that there are M DIDO distributed antennas in each DIDO cluster, where M ≧ (N · U) and

if M is greater than the total number of receive antennas in the cluster, then using additional transmit antennas to pre-cancel interference to the target clients in the interference region or to improve link robustness to clients within the same cluster through the diversity scheme described in the related patents and patent applications, including 7,599,420; 7,633,994, respectively; 7,636,381, respectively; and patent application serial No. 12/143,503.

DIDO precoding weights are calculated to eliminate inter-client interference in the same DIDO cluster in advance. For example, inter-client interference may be removed using Block Diagonalization (BD) precoding described in related patents and patent applications, including 7,599,420, 7,633,994, 7,636,381, and patent application Ser. Nos. 12/143,503 and [7], such that the following condition is satisfied in the primary cluster

The precoding weight matrices in the neighboring DIDO cluster are designed such that the following conditions are satisfied

To calculate a precoding matrix W_c，iThe downlink channels from the M transmit antennas to the Ic clients in the interference cluster and to client k in the interference zone are estimated and the precoding matrix is calculated by the DIDO BTSs in the interference cluster. If the precoding matrix in the interference cluster is calculated using the BD method, the following effective channel matrix is constructed to calculate the weight to the ith client in the adjacent cluster

Wherein

To derive a channel matrix for interference cluster c

The matrix obtained with the rows corresponding to the ith client removed.

Substituting conditions (2) and (3) into (1), we obtain the data stream received for target client k, with intra-cluster and inter-cluster interference removed

r_k＝H_kW_kS_k+n_k. (5)

Precoding weight W in (1) calculated in neighbor cluster_c，iDesigned to transmit precoded data streams to all clients in those clusters while pre-eliminating interference to target clients in the interference region. The target client receives precoded data only from its master cluster. In various embodiments, the same data stream is sent to the target client from the primary cluster and the neighboring clusters to obtain diversity gain. In this case, the signal model in (5) is expressed as

Wherein W_c，iIs the DIDO precoding matrix from the DIDO transmitter in the c-th cluster to target client k in the interference zone. Note that the method in (6) requires time synchronization across adjacent clusters, which can be complex to achieve in large systems, but nevertheless it is quite feasible if the diversity gain benefits justify implementation costs.

We begin by evaluating the performance of the proposed method in terms of the variation of the Symbol Error Rate (SER) with the signal-to-noise ratio (SNR). Without loss of generality, we define the following signal model assuming each client has a single antenna, and reformulate (1) as

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

Fig. 6 shows the SER as a function of SNR assuming SIR of 10dB for the target client in the interference region. Without loss of generality, we measure SERs for 4-QAM and 16-QAM without Forward Error Correction (FEC) coding. For uncoded systems, we fix the target SER to 1%. The target corresponds to different values of SNR depending on the modulation order (i.e., SNR 20dB for 4-QAM and 28dB for 16-QAM). When FEC coding is used, lower SER targets may be met for the same SNR value due to coding gain. We consider the case of two clusters (one primary and one interfering cluster) with two DIDO antennas and two clients (each equipped with a single antenna) per cluster. One of the clients in the primary cluster is located in the interference zone. We assume a flat fading narrowband channel, but the following results can be extended to a frequency selective multi-carrier (OFDM) system, where each sub-carrier experiences flat fading. We consider two cases: (i) a scenario with DIDO inter-cluster interference (IDCI), where precoding weights W are computed without considering target clients in the interference zone_c，i(ii) a And (ii) another case in which the weight W is calculated_c，iTo remove the IDCI to eliminate the IDCI to the target client. We observed that in the presence of IDCI, SER was high and above the predefined target. By IDCI-precoding at neighboring clusters, interference to target clients is removed and for SNR>The 20dB reaches the SER target.

The results in fig. 6 assume IDCI-precoding as in (5). Additional diversity gain is obtained if IDCI-precoding at neighboring clusters is also used to precode data streams to target clients in the interference zone as in (6). Fig. 7 compares SER derived by two techniques: (i) "method 1" using IDCI-precoding in (5); (ii) "method 2" of IDCI-precoding in (6) is employed, where the neighboring clusters also transmit the precoded data stream to the target client. Method 2 yields approximately 3dB gain compared to conventional IDCI-precoding due to the additional array gain provided by the DIDO antennas in the neighboring cluster used to transmit the precoded data streams to the target clients. 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 neighboring clusters and the factor "1" refers to the primary cluster.

Then, we evaluate the performance of the above method as a function of the location of the target client relative to the interference area. We consider a simple scenario where the target client 8401 moves from the primary DIDO cluster 802 to the interfering cluster 803, as shown in fig. 8. We assume that all DIDO antennas 812 within the main cluster 802 cancel inter-cluster interference using BD precoding to satisfy condition (2). We assume that there is a single interfering DIDO cluster, a single receiver antenna at the client device 801, and equal path loss to the client from all DIDO antennas in the main or interfering cluster (i.e., DIDO antennas placed in a circle around the client). We use a simplified path loss model with a path loss exponent of 4 (as in a typical urban environment) [11 ].

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

Wherein the signal-to-interference ratio (SIR) is derived as SIR ((1-D)/D)⁴. In modeling IDCI, we consider three cases: i) ideal case without IDCI; ii) pre-cancelling the IDCI via BD pre-coding in the interference cluster to satisfy condition (3); iii) has IDCI that are not pre-eliminated by neighboring clusters.

Fig. 9 shows the signal-to-interference-plus-noise ratio (SINR as a function of D) (i.e., when the target client moves from the main cluster 802 towards the DIDO antenna 813 in the interference cluster 8403.) SINR is derived as the ratio of signal power to interference-plus-noise power using the signal model in (8)_o，D_o0.1 and SNR 50 dB. Without IDCI, the wireless link performance is only affected by noise and the SINR decreases due to path loss. In the presence of IDCI (i.e., no IDCI-precoding), interference from DIDO antennas in neighboring clusters helps to reduce SINR.

Fig. 10 shows the Symbol Error Rate (SER) performance for the three cases described above for 4-QAM modulation in a flat fading narrowband channel. These SER results correspond to the SINR in fig. 9. We assume that the SER threshold of 1% for an uncoded system (i.e. no FEC) corresponds to the SINR threshold SINR in fig. 9_T20 dB. The SINR threshold depends on the modulation order used for data transmission. Higher modulation orders are typically passed through higher SINR_TCharacterized to achieve the same target error rate. With FEC, a lower target SER can be achieved for the same SINR value due to coding gain. In case of IDCI without precoding, only in range D<The target SER is achieved within 0.25. Range extension up to D to meet target SER by IDCI-precoding at neighboring clusters<0.6. Outside the range, the SINR increases due to path loss, and the SER target is not met.

One embodiment of a method for IDCI precoding is shown in fig. 11, which comprises the steps of:

SIR estimation 1101: the client estimates the signal power from the primary DIDO cluster (i.e., based on the received precoded data) and the interference-plus-noise signal power from the neighboring DIDO cluster. In a single carrier DIDO system, the framework can be designed with short silent periods. For example, a silent period may be defined between training for channel estimation and precoded data transmission during Channel State Information (CSI) feedback. In one embodiment, the interference plus noise signal power from the neighboring cluster is measured by the DIDO antennas in the main cluster during periods of silence. In practical DIDO multi-carrier (OFDM) systems, zero tones are typically used to prevent Direct Current (DC) offset and attenuation at the band edges due to filtering at the transmit and receive sides. In another embodiment using a multi-carrier system, the interference plus noise signal power is estimated from the null tones. The transmit/receive filter attenuation at the band edges may be compensated for with a correction factor. Once the signal plus interference and noise power (P) of the autonomous cluster is estimated_S) And interference plus noise power (P) from neighboring clusters_IN) The client then calculates the SINR as

Alternatively, SINR estimates are derived from Received Signal Strength Indication (RSSI) used in typical wireless communication systems to measure radio signal power.

We observe that the metric in (9) cannot distinguish between noise and interference power levels. For example, clients that are affected by shadowing in a non-interfering environment (i.e., behind an obstruction that attenuates signal power from all DIDO distributed antennas in the primary cluster) may estimate a low SINR even though they are not affected by inter-cluster interference.

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

Wherein P is_NIs the noise power. In a practical multi-carrier OFDM system, the noise power P in the system is estimated (10) from the zero tones_NAssume that all DIDO antennas from the main cluster and the neighboring cluster use the same set of null tones. Estimating interference plus noise power (P) from silent periods as described above_IN). Finally, signal plus interference and noise power (P) is derived from the data tones_S). From these estimates, the client calculates the SIR in (10).

Channel estimation at neighboring cluster 1102-: if it is determined at 8702 of FIG. 11 that the estimated SIR in (10) is below a predefined threshold (SIR)_T) Then the client starts listening for training signals from neighboring clusters. Note that SIR_TDepending on the modulation and FEC coding scheme (MCS) used for data transmission. Different SIR targets are defined according to the MCS of the client. When the DIDO distributed antennas from different clusters are time synchronized (i.e., locked to the same pulse per second PPS, time reference), the client delivers its channel estimates to the DIDO antennas in the neighboring cluster using the training sequence at 8703. The training sequences used for channel estimation in the neighboring clusters are designed to be orthogonal to the training from the primary cluster. Alternatively, DIDO in different clustersOrthogonal sequences (with good cross-correlation properties) are used for time synchronization in different DIDO clusters when the antennas are not time synchronized. Once the client locks to the time/frequency reference of the neighboring cluster, channel estimation is performed at 1103.

IDCI precoding 1104: IDCI-precoding is computed to satisfy the condition in (3) once channel estimates are available at DIDO BTSs in the neighbor cluster. The DIDO antennas in the neighboring cluster transmit only precoded data streams to the clients in its cluster while pre-canceling interference to clients in the interference region 410 in fig. 4. We observe that if the client is located in the type B interference zone 410 in fig. 4, then the interference to the client is generated by multiple clusters and IDCI-precoding is performed simultaneously by all neighboring clusters.

Handover method

Hereinafter, we describe different handoff methods for clients moving across DIDO clusters populated with distributed antennas located in separate areas or providing different types of services (i.e., low or high mobility services).

a. Handover between adjacent DIDO clusters

In one embodiment, the IDCI precoder used to remove the inter-cluster interference described above is used as a baseline for the handoff method in the DIDO system. Conventional handoff in cellular systems is envisioned as seamless switching of clients across cells served by different base stations. In DIDO systems, handoff allows clients to move from one cluster to another without losing connectivity.

To illustrate one embodiment of a handoff strategy for a DIDO system, we consider again the example of fig. 8 with only two

clusters

802 and 803. As the client 801 moves from the main cluster (C1)802 to the neighbor cluster (C2)803, one embodiment of the handoff method dynamically calculates the signal quality in the different clusters and selects the cluster for the client that yields the lowest error rate performance.

Fig. 12 shows SINR variation as a function of client distance from the center of cluster C1. For 4-QAM modulation without FEC coding, we consider the target SINR to be 20 dB. When both C1 and C2 use DIDO precoding without interference cancellation, the line marked with a circle represents the SINR of the target client served by the DIDO antenna in C1. The SINR decreases with D due to path loss and interference from neighboring clusters. When IDCI-precoding is implemented at neighboring clusters, the SINR loss is due only to path loss (as shown by the lines with triangles) because the interference is completely removed. When a client is served by a neighboring cluster, symmetric behavior is experienced. One embodiment of the handoff strategy is defined such that as the client moves from C1 to C2, the algorithm switches between different DIDO schemes to keep the SINR above a predefined target.

From the graph in fig. 12, we derive the SER for 4-QAM modulation in fig. 13. We observe that the SER is kept within a predefined target by switching between different precoding strategies.

One embodiment of a handoff strategy is as follows.

C1-DIDO and C2-DIDO precoding: when the client is located far away from the interference zone within C1, both clusters C1 and C2 independently work with conventional DIDO precoding.

C1-DIDO and C2-IDCI precoding: as a client moves towards the interference area, its SIR or SINR decreases. When the target SINR is reached_T1At this point, the target client begins estimating channels from all DIDO antennas in C2 and providing CSI to the BTS of C2. The BTS in C2 calculates IDCI-precoding and transmits to all clients in C2 while preventing interference to the target client. As long as the target client is within the interference zone, it will continue to provide its CSI to both C1 and C2.

C1-IDCI and C2-DIDO precoding: as the client moves towards C2, its SIR or SINR continues to decrease until it again reaches the target. At this point, the client decides to switch to the neighboring cluster. In this case, C1 starts to use CSI from the target client through IDCI-precoding to create zero interference towards its direction, while the neighboring clusters use CSI for regular DIDO-precoding. In one embodiment, when the SIR estimate is close to the target, clusters C1 and C2 each alternately try both DIDO-precoding scheme and IDCI-precoding scheme to allow the client to estimate the SIR in both cases. The client then selects the best solution to maximize some error rate performance metrics. When applying this method, the intersection point for the handover strategy occurs at the intersection of the curves with triangles and diamonds in fig. 12. One embodiment uses the modified IDCI-precoding method described in (6), where the neighboring clusters also transmit the precoded data streams to the target clients to provide the array gain. By this approach, the handover strategy is simplified, since the client does not need to estimate the SINR of both strategies at the intersection.

C1-DIDO and C2-DIDO precoding: when the clients move out of the interference zone towards C2, the main cluster C1 stops pre-canceling the interference towards the target client via IDCI-precoding and switches back to conventional DIDO-precoding for all clients remaining in C1. This final crossover point in our handoff strategy can be used to avoid unnecessary CSI feedback from the target client to C1, thereby reducing overhead on the feedback channel. In one embodiment, a second target SINR is defined_T2. When the SINR (or SIR) increases above this target, the strategy switches to C1-DIDO and C2-DIDO. In one embodiment, cluster C1 keeps alternating between DIDO-precoding and IDCI-precoding to allow the client to estimate SINR. The client then chooses to more closely approach the target SINR from above_T1The method of (5) for C1.

The method described above calculates SINR or SIR estimates for the different schemes in real time and uses them to select the best scheme. In one embodiment, the handoff algorithm is designed based on the finite state machine shown in fig. 14. When the SINR or SIR falls below or above the predefined threshold shown in fig. 12, the client keeps track of its current state and switches to the next state. As described above, in state 1201, clusters C1 and C2 each independently work with conventional DIDO precoding, and clients are served by cluster C1; in state 1202, the client is served by cluster C1, the BTSs in C2 compute IDCI-precoding, and cluster C1 works with conventional DIDO precoding; in state 1203, the client is served by cluster C2, the BTSs in C1 compute IDCI-precoding, and cluster C2 works with conventional DIDO precoding; and in state 1204, the client is served by cluster C2, and clusters C1 and C2 each independently operate with conventional DIDO precoding.

In the presence of shadowing effects, the signal quality or SIR may fluctuate around the threshold as shown in fig. 15, causing repeated switching between successive states in fig. 14. Repeatedly changing states is an undesirable effect because it results in significant overhead on the control channel between the client and the BTS to enable switching between transmission schemes. Fig. 15 shows an example of a handover strategy in the presence of shadowing. In one embodiment, the shading coefficients are modeled according to a log-normal distribution with variance 3 [3 ]. In the following, we define some methods to prevent the effects of repeated handoffs during DIDO handoff.

One embodiment of the present invention employs a hysteresis loop to address the state switching effect. For example, the threshold SINR is when switching between the "C1-DIDO, C2-IDCI" 9302 and "C1-IDCI, C2-DIDO" 9303 states in FIG. 14 (or vice versa)_T1Can be adjusted to be in the range A₁And (4) the following steps. The method is used in the signal quality at SINR_T1Repeated switching between states is avoided when oscillating around. For example, fig. 16 illustrates a hysteresis loop mechanism when switching between any two states in fig. 14. To switch from state B to state A, the SIR must be greater than (SIR)_T1+A₁/2), but in order to switch from a back to B, the SIR must be lowered below (SIR)_T1-A₁/2)。

In various embodiments, the threshold SINR is adjusted_T2To avoid toggling between the first and second states (or the third and fourth states) of the finite state machine of fig. 14. For example, the value A may be defined₂So that the threshold SINR is chosen within the range according to the channel conditions and shadowing effects_T2。

In one embodiment, the range [ SINR ] is based on the variance of the expected shadowing on the wireless link_T2,SINR_T2+A₂]The SINR threshold is adjusted dynamically. The client may be based on the received signal as it moves from its current cluster to an adjacent clusterThe variance of the strength (or RSSI) estimates the variance of the log-normal distribution.

The above method assumes that the client triggers the handover strategy. In one embodiment, the handoff decision to the DIDO BTS is postponed, assuming communication is enabled across multiple BTSs.

For simplicity, the above method is derived assuming FEC-free coding and 4-QAM. More generally, SINR or SIR thresholds are derived for different Modulation Coding Schemes (MCSs), and handover strategies are designed in conjunction with link adaptation (see, e.g., U.S. patent No.7,636,381) to optimize the downlink data rate for each client in the interference region.

b. Handover between low-doppler DIDO network and high-doppler DIDO network

DIDO systems employ a closed-loop transmission scheme to precode data streams on downlink channels. The closed loop scheme is inherently constrained by the delay on the feedback channel. In practical DIDO systems, when delivering CSI and baseband precoded data from the BTS to the distributed antennas, the computation time can be shortened by transceivers with high processing power, and most of the delay is expected to be introduced by DIDO BSNs. The BSN may include a variety of network technologies including, but not limited to, Digital Subscriber Line (DSL), cable modem, fiber ring, T1 line, Hybrid Fiber Coax (HFC) network, and/or fixed wireless (e.g., WiFi). Dedicated fiber typically has very large bandwidth and low latency, which may be less than 1 millisecond in a local area, but is deployed less widely than DSL and cable modems. Today, DSL and cable modem connections in the united states typically have a last mile delay between 10-25ms, but are deployed very widely.

The maximum delay on the BSN determines the maximum doppler frequency that can be tolerated on the DIDO wireless link without degrading DIDO precoding performance. For example, in [1], we show that at a carrier frequency of 400MHz, a network with approximately 10 ms delay (i.e., DSL) can tolerate speeds up to 8mph (running speed) for clients, while a network with 1 ms delay (i.e., fiber optic ring) can support speeds up to 70mph (i.e., highway traffic).

We define two or more DIDO subnetworks according to the maximum allowable doppler frequency on the BSN. For example, the BSN of a high-delay DSL connection between a DIDO BTS and distributed antennas can only provide low-mobility or fixed wireless services (i.e., low-doppler networks), while a low-delay BSN on a low-delay fiber ring can tolerate high-mobility (i.e., high-doppler networks). We have observed that most broadband users are not mobile when using broadband, and further that most people are unlikely to be located near areas where many high speed objects are moving (e.g. near motorways), as such locations are often less than ideal residential or office locations. However, there are also broadband users who use broadband at high speeds (e.g., when driving on a highway) or near high-speed objects (e.g., in a store located near a highway). To address these two different user doppler scenarios, in one embodiment, a low doppler DIDO network consists of a generally large number of DIDO antennas with relatively low power (i.e., 1W to 100W for indoor or rooftop installations) dispersed over a wide area, while a high doppler network consists of a generally smaller number of DIDO antennas with high power transmission (i.e., 100W for rooftop or tower installations). Low doppler DIDO networks typically serve a large number of low doppler users and can be implemented with typically low connection costs using inexpensive high latency broadband connections (e.g., DSL and cable modems). High doppler DIDO networks typically serve a smaller number of high doppler users and can be implemented at typically higher connection costs using more expensive low-delay broadband connections (e.g., fiber optics).

To avoid interference between different types of DIDO networks (e.g., low doppler and high doppler), different multiple access techniques may be employed, 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 to allocate clients to different types of DIDO networks and allow for handoff between them. Network selection is based on the type of mobility of each client. According to the following equation [6], the velocity (v) of the client is proportional to the maximum doppler shift,

wherein f is_dFor maximum doppler shift, λ is the wavelength corresponding to the carrier frequency, and θ is the angle between the vector indicating the direction transmitter-client and the velocity vector.

In one embodiment, the doppler shift for each client is calculated by a blind estimation technique. For example, similar to a doppler radar system, doppler frequency shift can be estimated by sending RF energy to the client and analyzing the reflected signal.

In another embodiment, one or more DIDO antennas transmit training signals to the client. Based on those training signals, the client estimates the doppler shift using techniques such as counting the zero-crossing rate of the channel gain or performing spectral analysis. We observe that for a fixed velocity v and trajectory of the client, the angular velocity in v sin θ (11) may depend on the relative distance of the client from each DIDO antenna. For example, a DIDO antenna close to a mobile client produces a greater angular velocity and doppler shift than an antenna far away. In one embodiment, the doppler velocity is estimated by multiple DIDO antennas at different distances from the client and the mean, weighted mean, or standard deviation is used as an indicator of client mobility. Based on the estimated doppler indicator, the DIDO BTS decides whether to assign the client to a low doppler network or a high doppler network.

The doppler indicator is periodically monitored and sent back to the BTS for all clients. As one or more clients change their doppler velocity (i.e., a client riding on a bus versus a client walking or sitting), those clients are dynamically reassigned to different DIDO networks that may tolerate their mobility levels.

While the doppler of a low-speed client may be affected by being near a high-speed object (e.g., near a highway), the doppler is typically much lower than that of a client that is itself in motion. Thus, in one embodiment, the client's velocity is estimated (e.g., by using a method such as monitoring the client location with GPS) and if the velocity is low, the client is assigned to a low doppler network, and if the velocity is high, the client is assigned to a high doppler network.

Method for power control and antenna grouping

Fig. 17 shows a block diagram of a DIDO system with power control. First one or more data streams(s) of each client (1, …, U) are streamed_k) Multiplied by the weight generated by the DIDO precoding unit. The precoded data streams are multiplied by a power scaling factor calculated by the power control unit based on the input Channel Quality Information (CQI). The CQI is fed back by the client to the DIDO BTS or derived from the uplink channel assuming uplink-downlink channel reciprocity. The U pre-coded streams from different clients are then combined and multiplexed into M data streams (t)_m) A data stream is for each of the M transmit antennas. Finally, the stream t_mTo a digital-to-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 one embodiment, the CQI is an average SNR or RSSI. The CQI differs for different clients depending on path loss or shadowing. Our power control method adjusts the transmit power scaling factor P of different clients_kAnd multiplies them by the precoded data streams generated for the different clients. Note that one or more data streams may be generated for each client, depending on the number of client receive antennas.

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

where k is 1, …, U is the number of clients, SNR is P_o/N_oIn which P is_oIs the average transmit power, N_oAs noise power, α_kIs a path loss/mask systemAnd (4) counting. To model the path loss/shadowing, we use the following simplified model

Where a-4 is the path loss exponent and we assume that the path loss increases with the client index (i.e., the client is located at an increasing distance from the DIDO antenna).

Fig. 18 shows SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios. The ideal case assumes that all clients have the same path loss (i.e., a-0), generating P for all clients _k1. The curve with squares refers to the case where the clients have different path loss coefficients and no power control. The curve with points is chosen such that P is based on the power control coefficient_k＝1/α_kThe same situation (with path loss) results. With the power control approach, more power is allocated to the data streams intended for clients where higher path loss/shadowing occurs, resulting in a 9dB SNR gain (for this particular case) compared to the case without power control.

The united states Federal Communications Commission (FCC) (and other international regulatory agencies) defines constraints on the maximum power that can be emitted from wireless devices to limit exposure of the human body to Electromagnetic (EM) radiation. There are two types of restrictions [2 ]: i) "occupational/controlled" restrictions in which people are given complete knowledge of Radio Frequency (RF) sources through barriers, warnings, or signs; ii) "general population/uncontrolled" restriction, with no control over exposure.

Different transmission classes are defined for different types of wireless devices. In general, DIDO distributed antennas for indoor/outdoor applications comply with the requirements of the FCC "mobile" device class, defined as [2 ]:

"transmitting devices designed for use not in a fixed position, typically where the radiating structure is held at a distance of 20cm or more from the body of the user or a nearby person. "

Of "mobile" devicesEM emission is based on Maximum Permissible Exposure (MPE) (in mW/cm)²Representation) of the measured value. Figure 19 shows the variation of MPE power density with distance from the RF radiation source for different values of transmit power at a carrier frequency of 700 MHz. The maximum allowable transmit power to meet the FCC "uncontrolled" limit for devices that typically operate 20cm from the human body is 1W.

Less restrictive power emission constraints are defined for emitters mounted on roofs or buildings far from the "general population". For these "rooftop transmitters," the FCC defines a broad emission limit of 1000W as measured by Effective Radiated Power (ERP).

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

low Power (LP) transmitter: anywhere at any height (i.e., indoors or outdoors), with a maximum transmit power of 1W and a 5Mbps consumer grade broadband (e.g., DSL, cable modem, Fiber To The Home (FTTH)) backhaul connection.

High Power (HP) transmitter: an antenna mounted on a rooftop or building having a height of about 10 meters has a transmit power of 100W and a commercial-grade broadband (e.g., fiber optic loop) backhaul (with a virtually "infinite" data rate compared to the throughput available over a DIDO wireless link).

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

To obtain a practical visual perception of the performance of DIDO systems with different types of LP/HP transmitters, we consider the practical case of DIDO antenna installation at the center of Palo Alto (Palo Alto, CA) Calif. FIG. 20a shows N in Palo Alto (Palo Alto)_LPRandom distribution of 100 low power DIDO distributed antennas. In FIG. 20b, 50 LP antennas are represented by N _HP50 high power transmitters instead.

Based on the DIDO antenna profiles in fig. 20 a-20 b, we derive coverage maps in Palo Alto (Palo Alto) for systems using DIDO technology. Fig. 21a and 21b show two power distributions corresponding to the configurations in fig. 20a and 20b, respectively. The received power distribution (expressed in dBm) is derived assuming a path loss/shadowing model for urban environments defined by the 3GPP standard [3] at a carrier frequency of 700 MHz. We observed that using a 50% HP emitter produced better coverage of the selected area.

Fig. 22 a-22 b show the rate profiles for the above two cases. The throughput (in Mbps) is derived based on the power thresholds for the different modulation coding schemes defined in the 3GPP Long Term Evolution (LTE) standard in [4,5 ]. At a 700MHz carrier frequency, the total available bandwidth is fixed to 10 MHz. Two different frequency allocation plans are considered: i) allocating only 5MHz spectrum to LP stations; ii) 9MHz to HP transmitter and 1MHz to LP transmitter. Note that typically lower bandwidth is allocated to LP stations due to their DSL backhaul connection with limited throughput. Fig. 22 a-22 b show that when a 50% HP transmitter is used, the rate profile can be significantly improved, thereby increasing the average per client data rate from 2.4Mbps in fig. 22a to 38Mbps in fig. 22 b.

Then we define an algorithm to control the power transmission of the LP stations such that higher power is allowed at any given time, thereby increasing the throughput on the downlink channel of the DIDO system in fig. 22 b. We observed that the FCC limit on power density is defined as [2] based on time averaging

Wherein

Is the MPE mean time, t_nTo be exposed to have a power density S_nThe time period of the radiation. For "controlled" exposure, the average time was 6 minutes, while for"uncontrolled" exposure, which increases up to 30 minutes. Any power source is then 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 limit for 30 minute average of "uncontrolled" exposure.

Based on this analysis, we define an adaptive power control method to increase the instantaneous per-antenna transmit power while keeping the average power per DIDO antenna below the MPE limit. We consider a DIDO system with more transmit antennas than active clients. This is a reasonable assumption given that DIDO antennas can be envisioned as inexpensive wireless devices (similar to WiFi access points) and can be placed anywhere there is a DSL, cable modem, fiber optic, or other internet connection.

Fig. 23 shows the framework of a DIDO system with adaptive per antenna power control. The digital signal generated by multiplexer 234 is scaled by a power scaling factor S before being sent to DAC unit 235₁,…,S_MDynamically adjusting its amplitude. The power scaling factor is calculated by the power control unit 232 based on the CQI 233.

In one embodiment, N is defined_gAnd one DIDO antenna group. Each group contains at least as many DIDO antennas as there are active clients (K). At any given time, only one group has a limit of greater than MPE (R) ((R))

) Power level (S) of_o) N transmitted to client_a>K active DIDO antennas. One approach according to the round robin scheduling strategy shown in fig. 24 is repeated across all antenna groups. In another embodiment, a different scheduling technique is employed (i.e., proportional fair scheduling [8]]) Cluster selection is performed to optimize error rate or throughput performance.

Assuming cyclic power allocation, we derive the average transmit power per DIDO antenna from (14) as

Wherein t is_oIs the time period when the antenna group is active, and T_MPE30min is according to FCC rule [2]The defined average time. (15) Is the duty cycle (DF) of the group, which is defined such that the average transmit power from each DIDO antenna meets the MPE limit: (DF) ((m))

). The duty cycle depends on the number of active clients, the number of groups and the number of active antennas per group according to the following definition

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

We observe that the gain in (17) is at G on all DIDO antennas_dBThe extra transmit power is achieved at the expense of.

In general, from all N_gAll N of a group_aIs defined as the total transmission power

Wherein P is_ijFor the average per antenna transmit power, given by

And S_ij(t) is the power spectral density of the ith transmit antenna in the jth group. In one embodiment, the power spectral density in each antenna is designed (19) 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 subscribing to wireless internet services provided via a DIDO system. It is unlikely that every internet connection will always be fully utilized. We assume that 10% of the clients will be actively using wireless internet connections at any given time. The 400 DIDO antennas may then be divided into N _g10 groups of N_aEach group serves 40 active clients at any given time with a duty cycle DF of 0.1. The SNR gain resulting from this transmission scheme is G_dB＝10log₁₀(1/DF) ═ 10dB, provided by 10dB of additional transmit power from all DIDO antennas. However, we observe that the average per antenna transmit power is constant and within MPE limits.

Fig. 25 compares the (uncoded) SER performance with the above described power control of antenna grouping with the conventional eigenmode selection in us 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 (i.e., per antenna transmit SNR). The curve denoted DIDO 4 × 4 assumes four transmit antennas and BD precoding. The curve with squares represents the SER performance with two additional transmit antennas and BD with eigenmode selection, resulting in a 10dB SNR gain (at 1% SER target) relative to conventional BD precoding. Power control with antenna grouping and DF 1/10 also yields a gain of 10dB at the same SER target. We observe that eigenmode selection changes the slope of the SER curve due to diversity gain, while our power control method shifts the SER curve to the left (maintains the same slope) due to increased average transmit power. For comparison, SER with a larger duty cycle DF of 1/50 was shown to provide an additional 7dB gain compared to DF 1/10.

Note that our power control can have a lower complexity than the conventional eigenmode selection method. In practice, the antenna IDs for each group may be pre-computed and shared between the DIDO antennas and the clients via a look-up table, so that only K channel estimates are required at any given time. For eigenmode selection, (K +2) channel estimates are computed and additional computational processing is required to select the eigenmode that minimizes the SER for all clients at any given time.

Then, we describe another approach involving DIDO antenna grouping to reduce CSI feedback overhead in some special cases. Fig. 26a shows a situation where clients (points) are randomly scattered in an area covered by multiple DIDO distributed antennas (crosses). The average power on each transmit-receive wireless link may be calculated as

A＝{|H|²}. (20)

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

The matrix a in fig. 26 a-26 c is obtained numerically by averaging the channel matrix over 1000 instances. Two alternative scenarios are depicted in fig. 26b and 26c, respectively, where clients are grouped together around a subset of DIDO antennas and the clients receive negligible power from the remotely located DIDO antennas. For example, fig. 26b shows two sets of antennas that produce the block-diagonal matrix a. One extreme case is when each client is only very close to one transmitter and the transmitters are far from each other so that power from all other DIDO antennas is negligible. In this case, the DIDO link is degraded among the multiple SISO links and a is a diagonal matrix as in fig. 26 c.

In all three scenarios described above, BD precoding dynamically adjusts the precoding weights to account for different power levels on the wireless link between the DIDO antenna and the client. However, it is convenient to identify multiple groups within the DIDO cluster and operate DIDO precoding only within each group. The grouping method we propose yields the following advantages:

calculate the gain: the DIDO precoding is computed only within each group in the cluster. For example, if BD precoding is used, then 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, then the SVD for each block is calculated with reduced complexity. In practice, if the channel matrix is divided into dimensions n₁And n₂Such that n is n₁+n₂Then the complexity of SVD is only O (n)₁ ³)+O(n₂ ³)<O(n³). In the extreme case, if H is a diagonal matrix, the DIDO link is reduced to multiple SISO links and SVD computation is not required.

Reduced CSI feedback overhead: when the DIDO antennas and clients are grouped into groups, in one embodiment, CSI from the client to the antennas is only calculated within the same group. In a TDD system, assuming channel reciprocity, the antenna grouping reduces the number of channel estimates used to calculate the channel matrix H. In FDD systems where CSI is fed back over the wireless link, antenna grouping further results in a reduction in CSI feedback overhead over the wireless link between the DIDO antennas and the client.

Multiple access technique for DIDO uplink channels

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

Multiple Input Multiple Output (MIMO): the uplink streams are transmitted from the client to the DIDO antenna via an open loop MMO multiplexing scheme. This method assumes that all clients are time/frequency synchronized. In one embodiment, synchronization between clients 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 clients may generate jitter between clocks at different clients, which may affect the performance of the MIMO uplink scheme. After the client sends the uplink stream via the MMO multiplexing scheme, the receiving DIDO antenna may use a non-linear (i.e., maximum likelihood, ML) or linear (i.e., near zero minimum mean square error) receiver to cancel the co-channel interference and demodulate the uplink stream individually.

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, subsets of tones are allocated to different clients that transmit uplink streams simultaneously, thereby reducing latency.

Code Division Multiple Access (CDMA): each client is assigned to a different pseudo-random sequence and orthogonality across clients is achieved in the code domain.

In one embodiment of the invention, the client is a wireless device that transmits at a much lower power than the DIDO antenna. In this case, the DIDO BTS defines a subset of clients based on the uplink SNR information so that interference across subgroups is minimized. Within each subgroup, the multiple access techniques described above are used to create orthogonal channels in the time, frequency, spatial or code domain, thereby avoiding uplink interference across different clients.

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

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

A link adaptation method of the DIDO system using time, frequency and space selectivity of a wireless channel is defined in U.S. patent No.7,636,381. Embodiments of the present invention for link adaptation in a multi-carrier (OFDM) DIDO system that utilizes time/frequency selectivity of a wireless channel are described below.

We simulated rayleigh fading channels according to the exponentially decaying Power Delay Profile (PDP) or the Saleh-valenbuela model (Saleh-valenbuela model) in [9 ]. For simplicity, we assume that a single cluster channel with a multipath PDP is defined as

P_n＝e^-βn(21)

Where n is 0, …, L-1 is the index of the channel taps, L is the number of channel taps, β is 1/σ_DsIs an indicator of the coherence bandwidth of the channel, and the delay spread (σ) of the channel_DS) Inversely proportional PDP index A low value of β yields a frequency flat channel, while a high value of β yieldsA frequency selective channel. Normalizing the PDP in (21) such that the total average power of all L channel taps is uniform

Fig. 27 shows 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) for DIDO 2 × 2 system the first subscript indicates the client and the second subscript indicates the transmit antenna the high frequency selective channel (where β ═ 0.1) is shown in fig. 28.

Next, we investigate the performance of DIDO precoding in frequency selective channels. Assuming that the signal model in (1) satisfies the condition in (2), we calculate DIDO precoding weights via BD. We reformulate the DIDO received signal model in (5) into the conditions in (2)

r_k＝H_eks_k+n_k. (23)

Wherein H_ek＝H_kW_kFor DIDO 2 × 2 for a single antenna per client, the effective channel matrix is reduced to a value with the frequency response shown in fig. 29 and for the channel characterized by high frequency selectivity in fig. 28 (e.g., where β ═ 0.1 the solid line in fig. 29 refers to client 1 and the line with dots refers to client 2 based on the channel quality metric in fig. 29, we define a time/frequency domain Link Adaptation (LA) method that dynamically adjusts the MCS according to changing channel conditions.

We begin by evaluating the performance of different MCSs in AWGN and rayleigh fading SISO channels. For simplicity, we assume FEC-free coding, but the following LA method can be extended to systems that include FEC.

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

The 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 (shown in fig. 28) in the time or frequency domain. In contrast, when the channel is characterized by a large gain, the LA method switches to a higher MCS order to increase the spectrum efficiency. One contribution of the present patent application, compared to us patent No.7,636,381, is to use the effective channel matrix in (23) and fig. 29 as a metric to allow adaptation.

The overall framework of the LA method is shown in fig. 31 and defined as follows:

CSI estimation: at 3171, the DIDO BTS calculates the CSI from all users. A user may be equipped with a single or multiple receive antennas.

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

Link quality metric calculation: at 3173, the BTS calculates a frequency domain link quality metric. In an OFDM system, this metric is calculated based on the CSI and DIDO precoding weights for each tone. In one embodiment of the invention, the link quality metric is the average SNR over all OFDM tones. We define this method as LA1 (based on average SNR performance). In another embodiment, the link quality metric is the frequency response of the active channel in (23). We define this method as LA2 (based on tone-by-tone performance to exploit frequency diversity). If each client has a single antenna, the frequency domain effective channel is shown in fig. 29. If the client has multiple receive antennas, the link quality metric is defined as the Frobenius norm of the effective channel matrix for each tone. Alternatively, a plurality of link quality metrics are defined for each client as singular values of the effective channel matrix in (23).

The bit loading algorithm: at 3174, based on the link quality metrics, the BTS determines the MCS for the different clients and the different OFDM tones. For the LA1 method, the same MCS is used for all clients and all OFDM tones based on the SNR threshold of the rayleigh fading channel in fig. 30. For LA2, different MCSs are assigned to different OFDM tones to exploit channel frequency diversity.

Precoded data transmission: at 3175, the BTS transmits the precoded data streams from the DIDO distributed antennas to the clients using the MCS derived from the bit loading algorithm. One header is attached to the pre-encoded data to transmit the MCS for the different tones to the client. For example, if eight MCSs are available and an OFDM symbol is defined with N-64 tones, then log is needed₂(8) N192 bits to transmit the current MCS to each client. Assuming that those bits are mapped into symbols with 4-QAM (2 bits/symbol spectral efficiency), only 192/2/N ═ 1.5 OFDM symbols are needed to map MCS information. In another embodiment, multiple subcarriers (or OFDM tones) are grouped into subbands and the same MCS is allocated to all tones in the same subband to reduce overhead 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 low doppler), the MCS is recalculated every fraction of the channel coherence time, thereby reducing the overhead required for control information.

Fig. 32 shows SER performance of the LA method described above. For comparison, SER performance in a rayleigh fading channel is plotted for each of the three QAM schemes used. The LA2 method adapts the MCS to the fluctuation of the effective channel in the frequency domain, providing a gain of 1.8bps/Hz and a 15dB gain in SNR (for SNR >35dB) for spectral efficiency at low SNR (i.e. SNR 20dB) compared to LA 1.

System and method for DIDO precoding interpolation in multi-carrier systems

The computational complexity of DIDO systems is limited primarily to centralized processors or BTSs. The most computationally expensive operation is to compute the precoding weights of all clients from their CSI. When BD precoding is used, the BTS must perform as many Singular Value Decomposition (SVD) operations as the number of clients in the system. One way to reduce complexity is through parallel processing, where the SVD is computed on separate processors for each client.

In a multi-carrier DIDO system, each subcarrier experiences a flat fading channel and SVD is performed for each client on each subcarrier. Obviously, the complexity of the system increases linearly with the number of subcarriers. For example, in an OFDM system having a 1MHz signal bandwidth, the cyclic prefix (L)₀) It is necessary to have at least eight channel taps (i.e., 8 microseconds duration) to avoid inter-symbol interference in an outdoor urban macrocell environment with large delay spread 3]. Size (N) of Fast Fourier Transform (FFT) used to generate OFDM symbols_FFT) Is normally set to L₀To reduce the loss of data rate. If N is present_FFT64, the effective spectral efficiency of the system is then determined by a factor N_FFT/(N_FFT+L₀) Limit 89%. N is a radical of_FFTA larger value of (d) results in 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 SVD operations on a subset of the tones (we call pilot tones) and derive the precoding weights for the remaining tones via interpolation. Weight interpolation is one source of error that causes inter-client interference. In one embodiment, optimal weight interpolation techniques are used to reduce inter-client interference, resulting in improved error rate performance and lower computational complexity in multi-carrier systems. In a DIDO system with M transmitting antennas, U clients and N receiving antennas per client, the precoding weight (W) of the k-th client which ensures zero interference to other clients U_k) Is derived from (2) to

Wherein H_uTo correspond to other DIDs in the systemAnd O, channel matrix of the client.

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

Wherein theta is_kFor a set of parameters to be optimized for user k,

is a weight interpolation matrix and | · | | non-woven phosphor_FThe Frobenius norm of the matrix is represented. The optimization problem is formulated as

Wherein Θ is_kTo optimize the feasible set of problems, θ_k，optIs the best solution.

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

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

Interpolating the weights in (25) by a matrix w_k(θ_k) Expressed as a parameter theta_kAs a function of the set of (a). Once the optimal set is determined according to (26) or (27), an optimal weight matrix can be calculated. In one embodiment of the invention, the weight interpolation matrix for a given OFDM tone n is defined as a linear combination of the weight matrices for pilot tones. An example of a weight interpolation function for a beamforming system with a single client is defined in [11]]In (1). In DIDO multi-client systems, we write the weight interpolation matrixBecome into

Wherein L is more than or equal to 0 and less than or equal to (L)₀-1)，L₀Is the number of pilot tones and c_n＝(n-1)/N₀In which N is₀＝N_FFT/L₀. The weight matrix in (28) is then normalized such that

To ensure uniform power transmission from each antenna. If N is 1 (single receive antenna per client), the matrix in (28) becomes a vector normalized to its norm. In one embodiment of the invention, the pilot tones are chosen uniformly across the range of OFDM tones. In another embodiment, pilot tones are adaptively chosen based on CSI to minimize interpolation error.

We have observed that one key difference between the system and method of [11] and the system and method proposed in this patent application is the objective function. Specifically, the system in [11] assumes multiple transmit antennas and a single client, and thus the related method is designed to maximize the product of precoding weight times channel to maximize the reception SNR of the client. However, this approach does not work in a multi-client scenario because it generates inter-client interference due to interpolation errors. In contrast, our approach is designed to minimize inter-client interference, thereby improving error rate performance for all clients.

FIG. 33 shows a table for where N is_FFT64 and L₀The channel PDP is generated from the model in (21) (where β is 1) and the channel consists of only eight channel taps₀Must 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. The interpolation weights match the ideal function for the pilot tones according to the definition in (28). The weights computed on the residual pitch are due to estimation errorsPoor and only approximate to the ideal case.

One way to implement the weight interpolation method is via the feasible set Θ in pairs (26)_kAn exhaustive search is performed. To reduce the complexity of the search, we quantize the feasible set to be uniformly in the

range

0,2 π]P value of (d). FIG. 34 shows for L₀＝8、M＝N_tSER versus SNR for 2 transmit antennas and a variable number of P. SER performance improves as the number of quantization levels increases. We observe a much lower computational complexity due to the reduced number of searches, the case of P-10 approaches the performance of P-100.

FIG. 35 shows the values for different DIDO orders and L₀SER performance of the interpolation method of 16. We assume that the number of clients is the same as the number of transmit antennas and that each client is equipped with a single antenna. As the number of clients increases, SER performance decreases due to increased inter-client interference resulting from weight interpolation errors.

In another embodiment of the invention, weight interpolation functions other than those in (28) are used. For example, a linear predictive autoregressive model [12] may be used to interpolate weights across different OFDM tones based on estimates of channel frequency correlation.

Reference to the literature

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[5]3GPP TR 25.913, "Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN)", V8.0.0(2009-01) (3GPP TR 25.913, "Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN)", V8.0.0, 1 month 2009)

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[10] Paulraj, et al, Introduction to Space-Time Wireless communications, Cambridge University Press,40West 20th Street, New York, NY, USA,2003(A. Paulraj et al, guide for Space-Time Wireless communication, Cambridge University Press,40West Twenty, New York City, N.Y., 2003).

[11] Choice, et al, "interaction Based Transmit Beamforming for MIMO-OFDM with Limited feed," IEEE Trans. on Signal Processing, vol.53, No.11, pp.4125-4135, Nov.2005(J.Choi et al, "Interpolation Based Transmit Beamforming for MIMO-OFDM with Limited Feedback", "IEEE Signal Processing bulletin", Vol.53, No.11, p.4125 & 4135, 11. 2005).

[12] Wong, et al, "Long Range Channel Prediction for Adaptive OFDM Systems," Proc.of the IEEE Adaptive Conf.on Signals, Systems, and Computers, vol.1, pp.723-736, Pacific Grove, CA, USA, Nov.7-10,2004(I.Wong et al, "Long term Channel Prediction for Adaptive OFDM Systems", "IEEE Axico horse Signal, Systems and computer conference paper, Vol.1, p.723-736, Pacific jungle town, Calif., USA, 11.7-10 days.2004).

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[14] B.D. Van Veen, et al, "Beamforming: a versatility adaptation to spatial filtering," IEEE ASSP Magazine, Apr.1988(B.D. Van Veen et al, "Beamforming: general method for spatial filtering," IEEE ASSP journal, 4.1988).

[15] R.G.Vaughan, "On optimal combining at the mobile," IEEE trans.on Vehic. Tech., vol37, n.4, pp.181-188, Nov.1988(R.G.Vaughan, "optimal combination for moving", "IEEE conveyance technology bulletin, volume 37, No.4, page 181-188, 11 months 1988)

[16] Qian, "partial adaptive beamforming for coherent interference rejection," IEEE trans. on Sign. proc., vol.43, n.2, pp.506-515, Feb.1995(F.Qian, "partial adaptive beamforming for coherent interference rejection"; IEEE Signal processing journal, Vol.43, No.2, p.506-515, 1995, 2 months)

[17] Krim, et al, "Two cables of array Signal processing research," IEEE Signal Proc. Magazine, pp.67-94, July 1996(H.Krim et al, "array Signal processing research twenty years", "IEEE Signal processing journal, pp.67-94, 1996, 7 months)

[19] Remley, Digital beamforming system, U.S. Pat. No.4,003,016, Jan.1977(W.R. Remley, Digital beamforming system, U.S. Pat. No.4,003,016, 1 month 1977)

[18] Masak, U.S. Pat. No.4,771,289 Sep.1988(R.J. Masak, "Beamforming/null-steering adaptive array", U.S. Pat. No.4,771,289, 9. 1988)

[20] U.S. Pat. No.5,600,326, 2.9, "Adaptive digital Beamforming architecture and algorithm for nulling the main lobe and multiple side lobe radar jammers while maintaining the angular estimation accuracy of the monopulse ratio", U.S. Pat. No.5,600,326, Feb.1997 (K.B. Yu et al, "Adaptive digital Beamforming architecture and algorithm for nulling the main lobe and multiple side lobe radar jammers"), and

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[25] q.h. spencer, a.l. swindlehurst, and m.haardt, "Zero for tuning method for downlink spatial multiplexing in muusuers MIMO channels," IEEE trans.sig.proc., vol.52, pp.461-471, feb.2004(q.h. spencer, a.l. swindlehurst, and m.haardt, "Zero forcing method for downlink spatial multiplexing in multi-user MIMO channels", IEEE signal processing article, volume 52, page 461-471, 2 months 2004).

Disclosure from related patent application serial No. 12/917,257

The following describes wireless Radio Frequency (RF) communication systems and methods that use multiple distributed transmit antennas that operate cooperatively to create a wireless link to a given user while suppressing interference to other users. Allowing coordination across different transmit antennas via user clustering. A user cluster is a subset of transmit antennas whose signals can be reliably detected by a given user (i.e., received signal strength above a noise or interference level). Each user in the system defines its own user-cluster (user-router). The waveforms transmitted by the transmit antennas within the same user cluster coherently combine to create RF energy at the location of the target user and a point of zero RF interference at the location of any other user reachable by those antennas.

Consider a system with M transmit antennas in one user cluster and K users reachable by those M antennas, where K ≦ M. We assume that the transmitter knows the CSI between M transmit antennas and K users (H ∈ C)^KxM). For simplicity, it is assumed that each user is equipped with a single antenna, but the same approach can be extended to multiple receive antennas per user. Consider a channel vector (h) by which K users are to be transmitted from M transmit antennas_k∈C^1xM) Combined to obtain the following channel matrix H

Precoding weights (w) are calculated to create RF energy to user K and zero RF energy to all other K-1 users_k∈C^Mx1) To satisfy the following conditions

Wherein

Is an effective channel matrix for user k obtained by removing the k-th row of matrix H, and O^Kx1Is a vector with all zero terms.

In one embodiment, the wireless system is a DIDO system and uses user clustering to create a wireless communication link to a target user while pre-canceling interference to any other users reachable by antennas located within the user cluster. In U.S. patent 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 base station cooperatively operating to transmit precoded data streams to a plurality of users, thereby suppressing inter-user interference;

DIDO Base Transceiver Station (BTS): a centralized processor that generates precoded waveforms to the DIDO distributed antennas;

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

The DIDO distributed antennas are grouped into different subsets according to their spatial distribution relative to the BTS or DIDO client location. We define three types of clusters, as shown in fig. 36:

super cluster 3640: for a DIDO distributed antenna group connected to one or more BTSs, such that the round-trip delay between all BTSs and the corresponding user is within the constraints of the DIDO precoding loop;

DIDO cluster 3641: is a DIDO distributed antenna group connected to the same BTS. When a super cluster contains only one BTS, its definition is consistent with the DIDO cluster;

user cluster 3642: a DIDO distributed antenna group for cooperatively transmitting precoded data to a given user.

For example, the BTS is a local hub connected to other BTSs and DIDO distributed antennas via the BSN. The BSN may comprise a variety of network technologies including, but not limited to, Digital Subscriber Line (DSL), ADSL, VDSL [6], cable modems, fiber optic rings, T1 lines, Hybrid Fiber Coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi). All BTSs within the same supercluster share information about DIDO precoding via the BSN, so that the round-trip delay is within the DIDO precoding loop.

In fig. 37, the dots represent DIDO distributed antennas, the crosses are users and the dashed lines indicate user clusters of users U1 and U8, respectively. The method described hereinafter is designed to create a communication link to the target user U1 while creating a point of zero RF energy for any other user (U2 to U8) inside or outside the user cluster.

We propose a similar approach in [5], where a zero RF energy point is created to remove interference in the overlap region between DIDO clusters. Additional antennas are needed to transmit signals to clients within the DIDO cluster while suppressing inter-cluster interference. One embodiment of the method proposed in this patent application does not attempt to remove DIDO inter-cluster interference; but rather it assumes that the cluster is bound to the client (i.e., user-cluster) and is guaranteed not to generate interference (or negligible interference) to any other clients in the neighborhood.

One idea associated with the proposed method is that users far enough from the user-cluster are not affected by radiation from the transmit antennas due to large path loss. Users near or within the user-cluster receive non-interfering signals due to precoding. Furthermore, additional transmit antennas may be added to the user-cluster (as shown in FIG. 37) such that the condition K ≦ M is satisfied.

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

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

In one embodiment, the DIDO distributed antennas transmit training signals, 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 tone) at one particular frequency (i.e., a beacon channel), and the user estimates the link quality based on the beacon signal. One threshold is defined as the minimum signal amplitude (or power) above the noise level to successfully demodulate the data, as shown in fig. 38 a. Any link quality metric value below this threshold is assumed to be zero. The link quality metric is quantized by a limited number of bits and fed back to the transmitter.

In a different embodiment, a training signal or beacon is sent from the user and the link quality is estimated at the DIDO transmit antenna (as shown in fig. 38 b), assuming reciprocity between Uplink (UL) path loss and Downlink (DL) path loss. Note that when the UL and DL bands are relatively close, path loss reciprocity is a realistic assumption in Time Division Duplex (TDD) systems (with UL and DL channels at the same frequency) and Frequency Division Duplex (FDD) systems.

As shown in fig. 37, information about link quality metrics is shared across different BTSs via the BSN so that all BTSs are aware of the link quality between each antenna/user coupling across different DIDO clusters.

b. Definition of user-clusters: the link quality metric for all wireless links in the DIDO cluster is an entry of a link quality matrix shared across all BTSs via the BSN. An example of the link quality matrix for the case in fig. 37 is shown in fig. 39.

The user clusters are defined by a link quality matrix. For example, fig. 39 shows a selection of a user cluster for user U8. A subset of transmitters (i.e., active transmitters) of user U8 having a non-zero link quality metric is first identified. These transmitters populate the user-cluster for user U8. A submatrix is then selected that contains non-zero entries from the transmitters in the user-cluster to other users. Note that because the link quality metric is only used to select the user cluster, it can be quantized by only two bits (i.e., to identify states above or below the threshold in fig. 38), thereby reducing feedback overhead.

Another example for a user U1 is shown in fig. 40. In this case, the number of active transmitters is lower than the number of users in the sub-matrix, violating the condition K ≦ M. Thus, one or more columns are added to the sub-matrix to satisfy the condition. Additional antennas may be used for diversity schemes (i.e., antenna or eigenmode selection) if the number of transmitters exceeds the number of users.

Fig. 41 shows yet another example for user U4. We observe that the sub-matrix can be obtained as a combination of two sub-matrices.

c. CSI reporting to BTS: once a user cluster is selected, the CSI from all transmitters within the user-cluster to each user reached by those transmitters is made available to all BTSs. CSI information is shared across all BTSs via the BSN. In a TDD system, UL/DL channel reciprocity may be exploited to derive CSI from training on the UL channel. In FDD systems, a feedback channel from all users to the BTS is required. To reduce the amount of feedback, only CSI corresponding to non-zero entries of the link quality matrix is fed back.

DIDO precoding: finally, DIDO precoding is applied to each CSI sub-matrix corresponding to different user clusters (e.g., as described in related U.S. patent applications).

In one embodiment, an effective channel matrix is calculated

And (2) Singular Value Decomposition (SVD) of and precoding weights w to be used for users k_kIs defined as corresponding to

Right singular vectors of the null subspace. Or, if M is>K and SVD decompose the effective channel matrix into

The DIDO precoding weight for user k is given by

w_k＝U_o(U_o ^H·h_k ^T)

Wherein U is_oIs listed as

A matrix of singular vectors of the zero subspace.

From basic linear algebraic considerations, we observe matrices

Is equal to the eigenvector of C corresponding to the zero eigenvalue.

Wherein the effective channel matrix is decomposed into SVD

Then, calculate

An alternative to SVD of (a) is to compute the eigenvalue decomposition of C. There are several methods of computing the eigenvalue decomposition, such as the power method. Since we are only interested in the feature vectors of the null subspace corresponding to C, we use the inverse power method described by the following iteration

Vector (u) where the first iteration_i) Is a random vector.

Considering that the eigenvalues (λ) of the null subspace are known (i.e., zero), the inverse power method requires only one iteration to converge, thereby reducing computational complexity. Then, we write the precoding weight vector as

w＝C^-1u₁

Wherein u is₁Is a vector with real term equal to 1 (i.e. the precoding weight vector is C)^-1The sum of columns of (c).

The DIDO precoding calculation requires a matrix inversion. Several numerical solution schemes exist to reduce the complexity of matrix inversion, such as Strassen's algorithm [1] or Coppersmith-Winograd's algorithm [2,3 ]. Since C is by definition a hermitian matrix, an alternative solution is to decompose C into its real and imaginary parts and compute 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. As the client moves across different DIDO clusters as shown in fig. 42, the user-cluster follows its movement. In other words, when the client changes its location, the subset of transmit antennas is continuously updated and the effective channel matrix (and corresponding precoding weights) is recalculated.

The method proposed herein works within the super cluster in fig. 36 because the link between BTSs via the BSN must be low latency. To suppress interference in the overlapping regions of different super clusters, our approach in [5] can be used, which uses extra antennas to create points of zero RF energy in the interference region between DIDO clusters.

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

Reference to the literature

[1] Robinson, "forward an Optimal Algorithm for matrix multiplication," SIAM News, Volume 38, Number 9, November 2005(S. Robinson, "Optimal Algorithm for matrix multiplication," American society for Industrial and applied mathematics, News, Vol.38, 9 th, 11 th month 2005).

[2] Coppers mith and s.winograd, "Matrix Multiplication of multiple analysis of progress", j.symb.comp.vol.9, p.251-280,1990 (d.opersmith and s.winograd, "Matrix Multiplication via arithmetic series", "journal of symbol calculation", volume 9, page 251-

[3] H.Cohn, R.Kleinberg, B.Szegedy, C.Umans, "Group-the organic Algorithmsfor Matrix Multiplication", p.379-388, Nov.2005(H.Cohn, R.Kleinberg, B.Szegedy, C.Umans, "Group theory algorithm for Matrix Multiplication", p.379-.

[4] W.H.Press, S.A.Teukolsky, W.T.Vetterling, B.P.Flannery "NUMERICALRECIPES IN C: THE ART OF SCIENTIFIC COMPUTING", Cambridge University Press,1992(W.H.Press, S.A.Teukolsky, W.T.Vetterling, B.P.Flannery, "numerical methods IN C language: ARTs OF SCIENTIFIC computation", Cambridge University Press, 1992)

[5] Forenza and s.g. perlman, "Interference Management, handover, power addition in Distributed-Input Distributed-output (DIDO) Communication Systems" (a.forenza and s.g. perlman, "Interference Management, handover, power control and Link Adaptation in Distributed Input Distributed Output (DIDO) Communication system"), patent application serial No. 12/802,988 filed 6/16/2010.

[6]Per-Erik Eriksson and

Odenhammar, "VDSL 2: next import broadband technology ", Ericsson Review No.1, 2006(Per-Erik Eriksson and Erik)

Odenhammar, "VDSL 2: the next important broadband technology, "(review by ericsson telephone company, No.1, 2006).

Systems and methods for utilizing areas of coherence in wireless systems

The capacity of a multi-antenna system (MAS) in a practical propagation environment varies with the spatial diversity available over the wireless link. Spatial diversity is determined by the distribution of scatterers in the wireless channel and the geometry of the transmit and receive antenna arrays.

One popular model of MAS channel is the so-called cluster channel model, which defines groups of scatterers as clusters positioned around the transmitter and receiver. Generally, the more clusters and the greater their angular spread, the higher the spatial diversity and capacity achievable over the wireless link. The clustered channel model has been validated by actual measurements [1-2], and variations of those models have been adopted by different indoor (i.e., ieee802.11ln technology set [3] for WLAN) and outdoor (3GPP technical specification set [4] for 3G cellular systems) wireless standards.

Other factors that determine spatial diversity in a wireless channel are the 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 patterns [14-28 ].

[29] A unified model is proposed that describes the antenna array design and the effect of the characteristics of the propagation channel on the spatial diversity (or degrees of freedom) of the radio link. [29] The received signal model in (1) is given by

y(q)＝∫c(q，p)x(p)dp+z(q)

Wherein x (p) e C³To describe the polarization vector of the transmitted signal, p, q ∈ R³C (· c) epsilon c to describe the polarization vector positions of the transmit and receive arrays, respectively^3x3To describe the matrix of the system response between the transmit vector position and the receive vector position, it is given by

Wherein A is_t(·，·)，A_r(·，·)∈C^3x3Are a transmit array response and a receive array response, respectively and

is a channel response matrix in which the terms are the transmit directions

And the receiving direction

A complex gain in between. In the DIDO system, the user equipment may have a single or multiple antennas. For simplicity, we assume a single antenna receiver with an ideal isotropic pattern and rewrite the system response matrix to be

In which only the transmitting antenna pattern is considered

The array response can be approximated as [29] based on the Maxwell's system of equations and the far field terms of the Green function

Wherein

P is a space defining an antenna array and wherein

Wherein

For an unpolarized antenna, studying the array response is equivalent to studying the integral kernel above. In the following, we show the closure of the expressions for the integral kernels for 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 as follows [29]

a(cosθ，p_z)＝exp(-j2πp_zcosθ).

By extending the above equation into a series of shifted dyads, we obtain that the sine function has a resolution of 1/L and the dimension (i.e., degree of freedom) of the array-limited and approximately wavevector-limited subspace is

D_F＝L|Ω_θL where Ω_θ{ cos θ: theta belongs to theta. We observe that for a broadside array | Ω₉| Θ | and | Ω for an endfire array_θ|≈|Θ|²/2.

Unpolarized spherical array

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

The resolution of the spherical array obtained by decomposing the function by the sum of Bessel functions of the first kind is 1/(pi R)²) And the degree of freedom is given by:

D_F＝A|Ω|＝πR²|Ω|

wherein A is the area of the spherical array, and

areas of coherence in wireless channels

The relationship between the resolution of the spherical arrays and their area a is shown in fig. 43. The middle sphere is a spherical array of area a. The projection of the channel clusters onto the unit sphere defines different scattering regions whose size is proportional to the angular spread of the clusters. The region of size 1/A within each cluster (which we refer to as the "coherence region") represents the projection of the basis function of the radiation field of the array and defines the resolution of the array in the wavevector domain.

Comparing fig. 43 with fig. 44, we observe that the size of the areas of coherence decreases with the inverse of the size of the array. In fact, a larger array may concentrate energy into a smaller area, resulting in a larger number of degrees of freedom D_F. Note that the total number of degrees of freedom also depends on the angular spread of the clusters, as indicated in the definition above.

FIG. 45 shows another example where the array size covers an even larger area than FIG. 44, resulting in an additional degree of freedom. In a DIDO system, the array aperture may be approximated by the total area covered by all DIDO emitters (assuming the antennas are spaced apart by a fraction of the wavelength). Next, fig. 45 shows that the DIDO system can achieve an increased number of degrees of freedom by distributing antennas in space, thereby reducing the size of the coherence region. Note that these maps are generated assuming an ideal spherical array. In practical situations, the DIDO antennas are randomly scattered throughout a wide area, and the shape of the resulting areas of coherence may not be as regular as in the figure.

Fig. 46 shows that as the array size increases, more clusters are contained in the wireless channel as the radio waves are scattered by an increasing number of objects between the DIDO emitters. Thus, an increased number of basis functions (across the radiation field) may be excited, resulting in an additional degree of freedom as defined above.

The multi-user (MU) multi-antenna system (MAS) described in this patent application utilizes the coherence region of the wireless channel to create multiple simultaneous independent non-interfering data streams to different users. For a given channel condition and user distribution, the basis functions of the radiated field are selected to create independent and simultaneous wireless links to different users so that each user experiences a link without interference. When the channel between each transmitter and each user is known by the MU-MAS, the precoded transmissions are adjusted based on the information to create individual regions of coherence to the different users.

In one embodiment of the invention, the MU-MAS employs non-linear precoding such as Dirty Paper Coding (DPC) [30-31] or Tomlinson-Harashima (TH) [32-33] precoding. In another embodiment of the present invention, MU-MAS employs non-linear precoding such as Block Diagonalization (BD) or zero-forcing beamforming (ZF-BF) [34] as described in our previous patent application in the related patent application section of this specification.

To allow precoding to be implemented, the MU-MAS needs to know the Channel State Information (CSI). Via the feedback channel, CSI can be used for MU-MAS, or CSI can be estimated on the uplink channel (assuming uplink/downlink channel reciprocity is possible in Time Division Duplex (TDD) systems). One way to reduce the amount of feedback required for CSI is to use a limited feedback technique [35-37 ]. In one embodiment, MU-MAS uses a limited feedback technique to reduce CSI overhead for the control channel. In the limited feedback technique, codebook design is critical. One embodiment defines a codebook from basis functions across the radiated field of the transmit array.

As a user moves in space or the propagation environment changes over time due to moving objects (such as people or cars), the coherence region changes its position and shape. Due to the well-known doppler effect in wireless communications. The MU-MAS described in this patent application adjusts the precoding to continuously adapt to the areas of coherence for each user as the environment changes due to doppler effects. This adaptation of the coherence region is to create simultaneous non-interfering channels to different users.

Another embodiment of the present invention adaptively selects a subset of antennas of a MU-MAS system to create regions of coherence of different sizes. For example, if users are sparsely distributed in space (i.e., rural areas or time of day with low usage of wireless resources), only a small subset of antennas is selected, and the size of the coherence region is large relative to the size of the array in fig. 43. Alternatively, in densely populated areas (i.e., urban areas or times of day with peak usage of wireless service), more antennas are selected to create small areas of coherence for users in direct proximity to each other.

In one embodiment of the present invention, the MU-MAS is a DIDO system as described in previous patent applications as described in the related patent applications section of this specification. DIDO systems use linear or non-linear precoding and/or limited feedback techniques to create areas of coherence to different users.

Numerical results

We begin by computing the number of degrees of freedom in a conventional multiple input multiple output (MMO) system based on the array size. We consider an unpolarized linear array and two types of channel models: such as the indoor model in the IEEE802.11 n standard for WiFi systems and the outdoor model in the 3GPP-LTE standard for cellular systems. [3] The indoor channel model in (1) defines the number of clusters in the range [2,6] and the angular spread within the range [15 °,40 ° ]. The outdoor channel model for urban micro-cells defines an angular spread of about 20 ° at about 6 clusters and base stations.

Fig. 47 shows the degrees of freedom of the MIMO system in actual indoor and outdoor propagation scenarios. For example, considering a linear array with 10 antennas separated by one wavelength, the maximum degree of freedom (or number of spatial channels) available on a wireless link is defined to be about 3 for the outdoor case and 7 for the indoor case. Of course, the indoor channel provides more degrees of freedom due to the greater angular spread.

Next, we calculate the degrees of freedom in the DIDO system. We consider the case where the antennas are distributed in 3D space, such as the case where DIDO access points can be distributed in city centers on different floors of adjacent buildings. Therefore, we model DIDO transmit antennas (both connected to each other via a fiber or DSL backbone) as a spherical array. In addition, we assume that the clusters are uniformly distributed in solid angle.

Fig. 48 shows the variation of degrees of freedom with array diameter in a DIDO system. We observe that for a diameter equal to 10 wavelengths, about 1000 degrees of freedom can be used in DIDO systems. Theoretically, up to 1000 non-interfering channels to the user can be created. The increased spatial diversity due to the spatially distributed antennas is a key to the multiplexing gain provided by DIDO over conventional MMO systems.

We assume that the clusters are distributed in elevation [ α, pi- α ] and define the solid angle of the cluster as | Ω | ═ 4 pi cos α, for example, in a suburban situation with two stories buildings, the elevation of the scatterers may be α ═ 60 °, in which case the number of degrees of freedom varies with wavelength as shown in fig. 48.

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System and method for planned evolution and obsolescence of multiuser spectrum

The growing demand for high-speed wireless services and the increasing number of cellular telephone users have revolutionized the wireless industry over the last three decades, from the initial analog voice services (AMPS [1-2]) to standards that support digital voice (GSM [3-4], IS-95CDMA [5]), data traffic (EDGE [6], EV-DO [7]), and Internet browsing (WiFi [8-9], WiMAX [10-11], 3G [12-13], 4G [14-15 ]). The development of wireless technology over the years has been achieved due to two main efforts:

i) the Federal Communications Commission (FCC) [16] in the united states has been allocating new spectrum to support emerging standards. For example, in the first generation AMPS systems, the number of channels allocated by the FCC increased from the first 333 in 1983 to 416 in the late eighties of the twentieth century to support an ever-increasing number of cellular clients. More recently, commercialization of technologies like Wi-Fi, Bluetooth, and ZigBee has been achieved through the use of the unlicensed ISM band [17] allocated by the FCC as early as 1985.

ii) the wireless industry is constantly creating new technologies that more efficiently utilize the limited available spectrum to support higher data rate links and an ever increasing number of users. A major revolution in the wireless domain is the migration from analog AMPS systems to digital D-AMPS and GSM, which allow a higher number of calls for a given frequency band due to increased spectral efficiency, in the nineties of the twentieth century. Another fundamental transition was created early in the twenty-first century by spatial processing techniques such as multiple-input multiple-output (MIMO), resulting in a 4-fold increase in data rates over previous wireless networks, and adopted by different standards (i.e., IEEE802.11 n for Wi-Fi, IEEE 802.16 for WiMAX, 3GPP for 4G-LTE).

Despite the tremendous efforts to provide high-speed wireless connectivity solutions, the wireless industry is facing new challenges: providing High Definition (HD) video streaming to meet the growing demand for game-like services, and providing wireless coverage anywhere, including rural areas where constructing a wired backbone is costly and impractical. Currently, especially when the network is overloaded with a large number of concurrent links, the most advanced wireless standard system (i.e., 4G-LTE) cannot provide the data rate requirements and delay constraints to support HD streaming services. Again, the main drawbacks are limited spectrum availability and the lack of techniques that can truly increase data rates and provide high spectral efficiency with full coverage.

In recent years a new technology called Distributed Input Distributed Output (DIDO) [18-21] has emerged, which is described in our previous patent application as described in the section "related patent applications" of this specification. DIDO technology promises orders of magnitude increase in spectral efficiency, making HD wireless streaming services possible in overloaded networks.

Meanwhile, the U.S. government has been addressing the spectrum shortage problem by implementing a plan to release 500MHz of spectrum in the next 10 years. The plan was released at 28 days 6 months 2010 with the goal of allowing emerging wireless technologies to operate in new frequency bands and provide high speed wireless coverage in urban and rural areas [22 ]. As part of this program, the FCC opened VHF and UHF spectrum of about 200MHz for unauthorized use on 23/9/2010, which is called "white space" [23 ]. One limitation to operating in those frequency bands is that harmful interference to existing wireless microphone devices operating in the same frequency bands must not be generated. Thus, on day 22 of 7/2011, the IEEE 802.22working group mandates standards for new wireless systems employing cognitive radio technology (or spectrum sensing) with key features to dynamically monitor spectrum and operate in available frequency bands, thereby avoiding harmful interference to co-existing wireless devices [24 ]. The debate of allocating a portion of white space to authorized use and opening it for spectrum auctions has not recently emerged [25 ].

The coexistence of unauthorized devices in the same band, and the spectrum contention between unauthorized and authorized use, have been two major problems with the FCC spectrum allocation program over the years. For example, in white space, coexistence of wireless microphones and wireless communication devices has been enabled by cognitive radio technology. However, cognitive radio may only provide a portion of the spectral efficiency of other technologies that use spatial processing like DIDO. Similarly, in the last decade, the performance of Wi-Fi systems has decreased significantly due 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. One drawback of unlicensed spectrum is the unregulated use of RF devices, which will continue to contaminate the spectrum in the coming years. RF contamination also hinders 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 dynamic allocation of wireless spectrum to allow different services and standards to coexist and evolve. One embodiment of our method dynamically assigns permissions to RF transceivers to operate in certain portions of the spectrum and allows obsolescence of the same RF device to provide:

i) reconfigurability of the spectrum to allow new types of wireless operation (i.e., licensed versus unlicensed) and/or to comply with new RF power emission limits. This feature allows for spectrum auctions to be conducted whenever necessary without planning ahead for the use of licensed spectrum relative to unlicensed spectrum. It also allows the transmit power level to be adjusted to meet the new power transmit levels mandated by the FCC.

ii) coexistence of different technologies operating in the same band (i.e., white space and wireless microphone, WiFi and bluetooth/ZigBee) so that the band can be dynamically reallocated when a new technology is created, while avoiding interference with existing technologies.

iii) seamless evolution of the wireless infrastructure can be achieved when the system migrates to more advanced technologies that can provide higher spectral efficiency, better coverage and improved performance to support new services requiring higher QoS (i.e., HD video streaming).

In the following, we describe systems and methods for planned evolution and obsolescence of multiuser spectrum. One embodiment of the system includes one or more Centralized Processors (CP)4901 and 4904 and one or more Distributed Nodes (DN)4911 and 4913 that communicate via wired or wireless connections as shown in FIG. 49. For example, in the context of a 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 distributed nodes are Wi-Fi access points connected to the ISP via a modem or directly to a cable or DSL. In another embodiment of the present invention, the system is a Distributed Input Distributed Output (DIDO) system with one centralized processor (or BTS) and distributed nodes that are DIDO access points (or DIDO distributed antennas connected to the BTS via a BSN), such as the DIDO system described in the related patent application section of this specification.

DN 4911 and 4913 communicate with

CP

4901 and 4904. The information exchanged from the DN to the CP is used to dynamically adjust the configuration of the node to the evolving design of the network architecture. In one embodiment, DN 4911 and 4913 share their identification numbers with the CP. The CP stores the identities of all DNs connected via the network in a look-up table or shared database. Those look-up tables or databases may be shared with other CPs and the information synchronized so that all CPs always have access to the most up-to-date information about all DNs on the network.

For example, the FCC may decide to allocate some portion of the spectrum to unauthorized use and the proposed system may be designed to operate in that spectrum. Due to the lack of spectrum, the FCC may then need to allocate a portion of the spectrum to authorized use for commercial operators (i.e., american telegraph and telephone company (AT & T), virginson telecommunications (Verizon), or spruce corporation (Sprint)), defense, or public safety. In conventional wireless systems, such coexistence would not be possible because existing wireless devices operating in unlicensed frequency bands would create harmful interference to licensed RF transceivers. In our proposed system, distributed nodes exchange control information with

CP

4901 and 4903 to adapt their RF transmissions to the evolving band plan. In one embodiment, DN 4911 and 4913 are initially designed to operate on different frequency bands within the available frequency spectrum. When the FCC allocates one or more portions of the spectrum to licensed operations, the CP exchanges control information with the unlicensed DNs and reconfigures the DNs to close the frequency band for licensed use so that the unlicensed DNs does not interfere with the licensed DNs. This situation is illustrated in figure 50, where unauthorized nodes (e.g., 5002) are represented by filled circles and authorized nodes are represented by open circles (e.g., 5001). In another embodiment, the entire spectrum may be allocated to a new licensed service and control information is used by the CP to turn off all unlicensed DNs to avoid interfering with licensed DNs. This situation is illustrated in fig. 51, where outdated unauthorized nodes are covered with crosses.

By way of 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, the wireless system may initially be designed for a fixed wireless link, where DN 4911 and 4913 are connected to an outdoor rooftop transceiver antenna. Subsequently, the same system can be updated to support DNs with indoor portable antennas to provide better indoor coverage. Because of the potential for closer proximity to the human body, the FCC exposure limits for portable devices are more severely limited than for rooftop transmitters. In this case, the old DN designed for outdoor applications can be reused for indoor applications as long as the transmit power setting is adjusted. In one embodiment of the invention, the DNs are designed to have a predefined set of transmit power levels, and the CP 4901 along with 4903 sends control information to the DN 4911 along with 4913 to select new power levels when the system is upgraded to meet FCC exposure limits. In another embodiment, DNs are manufactured to have only one power transmission setting, and those DNs that exceed the new power transmission level will be remotely turned off by the CP.

In one embodiment, CP 4901 + 4903 periodically monitors all DNs 4911 + 4913 in the network to define its authority to operate as RF transceivers according to a certain standard. Those DNs that are not up-to-date may be marked as outdated and removed from the network. For example, a DN operating within the current power limit and frequency band remains active in the network and all other DNs are turned off. Note that the DN parameters controlled by the CP are not limited to power transmission and frequency band; it may be any parameter that defines the wireless link between the DN and the client device.

In another embodiment of the invention, DN 4911 and 4913 may be reconfigured to allow different standard systems to coexist in the same spectrum. For example, the power transmission, frequency band, or other configuration parameters of certain DNs operating in the context of WLANs may be adjusted to accommodate adoption of new DNs designed for WPAN applications while avoiding harmful interference.

As new wireless standards are developed to increase data rates and coverage in wireless networks, DN 4911 and 4913 may be updated to support those standards. In one embodiment, DN is a Software Defined Radio (SDR) equipped with programmable computing capabilities, such as an FPGA, DSP, CPU, GPU, and/or GPGPU that executes algorithms for baseband signal processing. If the standard is upgraded, a new baseband algorithm may be uploaded remotely from the CP to the DN to reflect the new standard. For example, in one embodiment, the first standard is a CDMA-based standard and is then replaced by OFDM technology to support different types of systems. Similarly, the sampling rate, power, and other parameters may be updated to the DN remotely. This SDR feature of the DN allows for continuous upgrades to the network as 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, distributed nodes, and a network interconnecting the CPs and DNs. Fig. 52 shows an example of a cloud wireless system in which nodes (e.g., 5203) identified with solid circles communicate with a CP5206, nodes identified with open circles communicate with a CP 5205, and the

CP

5205 and 5206 communicate with each other all via a network 5201. In one embodiment of the invention, the cloud wireless system is a DIDO system, and the DN connects to the CP and exchanges information to periodically or immediately reconfigure system parameters and dynamically adjust to changing conditions of the wireless infrastructure. In the DIDO system, the CP is a DIDO BTS, the distributed nodes are DIDO distributed antennas, the network is a BSN, and the BTSs are interconnected with each other via a DIDO centralized processor as described in our previous patent application as set forth in the "related patent applications" section of this specification.

All DN 5202-5203 in the cloud wireless system can be grouped into different groups. These groups of DNs may simultaneously create non-interfering wireless links to many client devices, while each group supports different multiple access techniques (e.g., TDMA, FDMA, CDMA, OFDMA, and/or SDMA), different modulations (e.g., QAM, OFDM), and/or coding schemes (e.g., convolutional coding, LDPC, enhanced codes). Similarly, each client may be served with a different multiple access technology and/or a different modulation/coding scheme. Based on the active clients in the system and the criteria it employs for its wireless links, the

CP

5205 and 5206 dynamically select a subset of DNs that can support those criteria and are within range of the client device.

Reference to the literature

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J.Karlsson,M.Meyer,S.Parkvall,J.Torsner,andM.Wahlqvist“Technical Solutions for the 3G Long-Term Evolution”，IEEECommunications Magazine,pp.38-45,Mar.2006(H.

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Karlsson, m.meyer, s.parkvall, j.torsner and m.wahlqvist, "technique for 3G long term evolutionSolution, "IEEE journal of communication", pages 38-45, 3 months 2006)

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[18] S.Perlman and A.Forenza "Distributed-input Distributed-output (DIDO) wireless technology: a new ap reach to multi-user wireless", Aug.2011http:// www.rearden.com/DIDO/DIDO _ White _ Paper _110727.pdf (S.Perlman and A.Forenza, "Distributed Input Distributed Output (DIDO) wireless technology: new method for multi-user wireless", 8 months 2011, http:// www.rearden.com/DIDO/DIDO _ White _ Paper _110727.pdf)

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[26]H.

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Karlsson, M.Meyer, S.Parkval, J.Torsner and M.Wahlqvist, "technical solution for 3G Long term evolution", IEEE journal of communications, pages 38-45, 3 months 2006)

[27] FCC, "Evaluating compatibility with FCC guidelines for human exposure to radio frequency electromagnetic fields," OET Bulletin 65, Edition 97-01, Aug.1997(FCC, "compatibility evaluation of FCC guidelines for human exposure to radio frequency electromagnetic fields," OET Bulletin 65, 97-01 Edition, 8 months 1997)

System and method for compensating for doppler effect in distributed input-distributed output wireless systems

In this section of the detailed description, we describe a multi-user (MU) multi-antenna system (MAS) for multi-user wireless transmission that adaptively reconfigures parameters to compensate for doppler effects due to changes in user mobility or propagation environment. In one embodiment, the MAS is a distributed input-distributed output (DIDO) system, as described in the co-pending patent application set forth in the related patent applications section of this specification and shown in fig. 53. The DIDO system of one embodiment includes the following components:

user Equipment (UE): UE 5301 for one embodiment includes an RF transceiver for fixed or mobile clients that receives data streams on Downlink (DL) channels from and transmits data to a DIDO backhaul over Uplink (UL) channels.

Base Transceiver Station (BTS) BTS 5310-5314 of one embodiment interfaces the DIDO backhaul with the wireless channel. The BTS 5310-5314 is an access point including a DAC/ADC and a Radio Frequency (RF) chain to convert a baseband signal into an RF signal. In some cases, the BTS is a simple RF transceiver equipped with a power amplifier/antenna, and the RF signal is transmitted to the BTS by radio frequency fiber optic transmission techniques, as described in our patent application.

Controller (CTR): the CTR 5320 in one embodiment is a specific type of BTS designed for certain specific purposes, such as transmitting training signals for time/frequency synchronization of the BTS and/or the UE, accepting or transmitting control information from or to the UE, receiving Channel State Information (CSI) or channel quality information from the UE.

Centralized Processor (CP): CP 5340 of one embodiment is a DIDO server that interfaces the internet or other type of external network 5350 with a DIDO backhaul. The CP computes the DIDO baseband processing and sends the waveform to the distributed BTS for DL transmission.

Base Station Network (BSN): the BSN 5330 of one embodiment is a network that connects CPs to distributed BTSs that carry information for either the DL channel or the UL channel. 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 local area network or the internet.

As described in co-pending patent applications, the DIDO system creates independent channels for multiple users such that each user receives a clear channel. In DIDO systems, this is achieved by employing distributed antennas or BTSs to exploit spatial diversity. In one embodiment, the DIDO system employs spatial, polarization, and/or pattern diversity to improve the degrees of freedom within each channel. The increased freedom of the radio link is used to transmit independent data streams (i.e. multiplexing gain) to a larger number of UEs and/or to increase the coverage (i.e. diversity gain).

The BTS 5310-5314 are located anywhere that facilitates access to the internet or BSN. In one embodiment of the invention, the UE 5301-5305 is optionally disposed between, around, and/or surrounded by the BTSs or distributed antennas, as shown in fig. 54.

In one embodiment, BTS 5310-5314 transmit the training signal and/or independent data stream to UE 5301 over the DL channel, as shown in fig. 55. The training signals are used by the UE for different purposes, such as time/frequency synchronization, channel estimation, and/or estimation of Channel State Information (CSI). In one embodiment of the invention, the MU-MAS DL employs non-linear precoding such as Dirty Paper Coding (DPC) [1-2] or Tomlinson-Harashima (TH) [3-4] precoding. In another embodiment of the present invention, MU-MAS DL employs non-linear precoding, such as Block Diagonalization (BD) or zero-forcing beamforming (ZF-BF) [5] as described in the co-pending patent applications described in the related patent applications section of this specification. If the number of BTSs is greater than the UEs, then additional BTSs are used to improve the link quality to each UE through a diversity scheme, such as the antenna selection or eigenmode selection described in the related patent application section of this specification. If the number of BTSs is smaller than the UE, additional UEs share the radio link with other UEs through conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDMA).

The UL channel is used to transmit data from the UE 5301 to the CSI (or channel quality information) used by the CP 5340 and/or the DIDO precoder. In one embodiment, the UL channel from the UE is multiplexed to the CTR as shown in fig. 56 or to the nearest BTS by conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDMA). In another embodiment of the present invention, the UL channel from UE 5301 to distributed BTS 5310-5314 is separated using spatial processing techniques, as shown in fig. 57. For example, UL streams are transmitted from the client to the DIDO antennas through a multiple-input multiple-output (MIMO) multiplexing scheme. The MIMO multiplexing scheme includes transmitting independent data streams from clients and removing co-channel interference using linear or non-linear receivers at DIDO antennas. In another embodiment, downlink weights are used on the uplink to demodulate the uplink streams, assuming UL/DL channel reciprocity is maintained and the channel does not differ significantly between DL and UL transmissions due to doppler effects. In another embodiment, a Maximum Ratio Combining (MRC) receiver is used on the UL channel to improve the signal quality from the DIDO antennas of each client.

Data, control information, and CSI transmitted through the DL/UL channel are shared between the CP 5340 and the BTS 5310-5314 through the BSN 5330. The known training signals for the DL channel may be stored in memory at BTS 5310-5314 to reduce overhead through BSN 5330. Depending on the type of network (i.e., wireless/wireline, DSL/cable or fiber), the data rate available on the BSN 5330 may not be sufficient to exchange information between the CP 5340 and the BTS 5310-5314, especially when delivering baseband signals to the BTS. For example, we assume that the BTS transmits 10Mbps independent data streams (depending on the digital modulation and FEC coding scheme used on the wireless link) over a 5MHz bandwidth to each UE. If 16 bits are quantized for the real part and 16 bits are quantized for the imaginary part, the baseband signal requires 160Mbps data throughput from the CP to the BTS through the BSN. In one embodiment, the CP and BTS are equipped with encoders and decoders to compress and decompress information sent over the BSN. In the forward link, the precoded baseband data transmitted from the CP to the BTS is compressed to reduce the number of bits and the overhead transmitted over the BSN. Similarly, in the reverse link, CSI and data (transmitted from the UE to the BTS over the uplink channel) are compressed and transmitted from the BTS to the CP over the BSN. Different compression algorithms are employed to reduce the number of bits and overhead transmitted over the BSN, including but not limited to lossless and/or lossy techniques [6 ].

One feature of the DIDO system used in one embodiment is to make the CP 5340 aware of CSI or channel quality information between all BTSs 5310-5314 and the UE 5301 to allow precoding. As described above, 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 variations in channel multiplexing gain are caused by changes in UE mobility and/or propagation environment that cause doppler effects. According to the channel coherence time (T) which is inversely proportional to the maximum Doppler shift_c) The rate of change of the channel is measured. For DIDO transmission to proceed reliably, the delay due to CSI feedback must be a fraction of the channel coherence time (e.g., 1/10 or less). In one embodiment, the delay on the CSI feedback loop, i.e. the time between when the CSI training is sent and when the precoded data is demodulated at the UE side, is measured as shown in fig. 58.

In a Frequency Division Duplex (FDD) DIDO system, BTS 5310-5314 send CSI training to UE 5301, which estimates the CSI and feeds back to the BTSs. The BTS then sends the CSI to CP 5340 through the BSN, which computes DIDO precoded data streams and sends them back to the BTS through BSN 5330. Finally, the BTS sends the pre-coded stream to the UE which demodulates the data. Referring to FIG. 58, the total delay of the DIDO feedback loop is given by

2*T_DL+T_UL+T_BSN+T_CP

Wherein T is_DLAnd T_ULIncluding the time, T, at which downlink and uplink frames are constructed, transmitted and processed, respectively_BSNFor round-trip delay, T, on BSN_CPThe time it takes to process the CSI for the CP, generate a precoded data stream for the UE, and schedule a different UE for the current transmission. In this case, T is taken into account for the training signal time (from BTS to UE) and the feedback signal time (from UE to BTS)_DLMultiplied by 2. In Time Division Duplexing (TDD), the first step (i.e., transmitting CSI training signals from BTS to UE) is skipped when the UE sends CSI training to the BTS, which calculates CSI and sends it to CP, if channel reciprocity can be exploited. Thus, in this embodiment, the total delay of the DIDO feedback loop is T_DL+T_UL+T_BSN+T_CP

Delay T_BSNDepending on whether the type of BSN is a dedicated cable, DSL, fiber optic connection, or the general internet. Typical values may vary between the range of 1 millisecond to 50 milliseconds. If DIDO processing is implemented at the CP on a special-purpose processor (such as an ASIC, FPGA, DSP, CPU, GPU, and/or GPGPU), the computation time at the CP may be reduced. Furthermore, if the number of BTSs 5310-5314 exceeds the number of UEs 5301, all UEs can be served simultaneously, thereby eliminating the delay due to multi-user scheduling. Thus, with T_BSNCompared with the delay T_CPAnd can be ignored. Finally, the transmit and receive processing for the DL and UL is typically implemented on ASICs, FPGAs, or DSPs with negligible computation time, and if the signal bandwidth is relatively large (e.g., greater than 1MHz), the frame duration can become very short (i.e., less than 1 millisecond). Thus, with T_BSNIn contrast, T_DLAnd T_ULAnd may be ignored.

In bookIn one embodiment of the invention, CP 5340 tracks the doppler velocity of all UEs 5301 and will have the lowest velocity_BSNBTS 5310-5314 are dynamically allocated to UEs with higher doppler. The adaptation is based on different criteria:

·type of BSN: for example, dedicated fiber optic links typically experience lower delays than cable modems or DSL. The lower-latency BSNs are used for high-mobility UEs (e.g., cars, trains on highways), while the higher-latency BSNs are used for fixed wireless or low-mobility UEs (e.g., home devices, pedestrians, and vehicles in a residential area).

·Type of QoS: for example, the BSN may support different types of DIDO or non-DIDO communications. Quality of service (QoS) of different priorities may be defined for different communication types. For example, the BSN assigns a high priority to DIDO communications and a low priority to non-DIDO communications. Alternatively, a high priority QoS is assigned to communications for high mobility UEs and a low priority QoS is assigned to UEs with low mobility.

·Long term statistical value: for example, communications on a BSN may vary significantly depending on the time of day (e.g., night home use, day office use). Higher traffic loads result in higher delays. Then, at different times of the day, for low mobility UEs if the BSN with higher communication results in higher delay, and for high mobility UEs if the BSN with lower communication results in lower delay.

·Short term statistics: for example, any BSN may be affected by temporary network congestion resulting in higher latency. However, the CP may adaptively select a BTS from the congested BSNs (if the congestion results in higher delay) for low mobility UEs and the remaining BSNs (if their delay is lower) for high mobility UEs.

In another embodiment of the present invention, the BTS 5310- > 5314 is selected based on the Doppler experienced on each individual BTS-UE link. For example, in line of sight (LOS) link B in FIG. 59, the maximum Doppler shift is a function of the angle (φ) between the BTS-UE link and the vehicle speed (v), according to well-known equations

Where λ is the wavelength corresponding to the carrier frequency. Thus, in the LOS channel, the doppler shift is greatest for link a and near zero for link C in fig. 59. In non-los (nlos), the maximum doppler shift depends on the direction of the multipath around the UE, but in general, because of the distributed nature of the BTSs in the DIDO system, some BTSs will experience higher doppler for a given UE (e.g., BTS 5312), while other BTSs will experience lower doppler for a given UE (e.g., BTS 5314).

In one embodiment, the CP tracks the doppler velocity on each BTS-UE link and selects only the link with the lowest doppler effect for each UE. Similar to the described technology, CP 5340 defines a "user cluster" for each UE 5301. The users are clustered into groups of BTSs with good link quality (based on a certain signal-to-noise ratio, SNR, threshold definition) and low doppler (e.g., based on a predefined doppler threshold definition) for the UEs, as shown in fig. 60. In fig. 60, BTSs 5-10 all have good SNRs for UE1, but only BTSs 6-9 experience low doppler effects (e.g., below a specified threshold).

The CP of this embodiment records all SNR and doppler values for each BTS-UE link into a matrix and selects a sub-matrix for each UE that meets the SNR and doppler thresholds. In the example shown in FIG. 61, the submatrix is surrounded by C_2,6、C_2,7、C_3,9、C_4,7、C_4,8、C_4,9And C_5,6Is indicated by the green dotted line. And calculating DIDO precoding weights of the UE based on the submatrices. Note that BTSs 5and 10 are reachable by

UEs

2,3, 4,5, and 7, as shown in the table of fig. 61. Then, to avoid interference to the UE1 when transmitting to those other UEs, the BTSs 5and 10 must shut down or assign to different orthogonal channels based on conventional multiplexing techniques (such as TDMA, FDMA, CDMA, or OFDMA).

In another embodiment, reducing Doppler effect through linear prediction for DIDO precoding system performanceAdversely affecting, the linear prediction is a technique for estimating future complex channel coefficients based on past channel estimates. By way of example and not limitation, [7-11 ]]Different prediction algorithms for single-input single-output (SISO) and OFDM wireless systems are proposed. Knowing the future channel complex coefficients may reduce errors due to outdated CSI. For example, fig. 62 shows channel gain (or CSI) at different times: i) t is t_CTRTime to receive CSI from a UE in an FDD system for the CTR in fig. 58 (or equivalently, the BTS estimates CSI from the UL channel using DL/UL reciprocity in a TDD system); ii) t_CPTime to deliver CSI to CP by BSN; iii) t_BTSTime to use CSI for precoding on the wireless link. In FIG. 62, we observe that T is due to the delay_BSN(also shown in FIG. 58), at time t_CTRFor estimating CSI at time t_BTSWill be outdated (i.e., the complex channel gain has changed) while wirelessly transmitting on the DL channel. One way to avoid this effect due to doppler is to run the prediction method at the CP. At time t_CTRAnd available CSI estimate at CP delays T due to CTR-CP delay _BSN2 and corresponds to time t in FIG. 62₀The channel gain of (c). CP usage then at time t₀All or part of the CSI previously estimated and stored in memory to predict time t₀+T_BSN＝t_CPFuture channel coefficients of (c). If the prediction algorithm has minimal error propagation, then at time t_CPThe predicted CSI reproduces the channel gain reliably in the future. The time difference between the predicted CSI and the current CSI is called the prediction time domain and is usually scaled by the channel coherence time in a SISO system.

In DIDO systems, the prediction algorithm is more complex because it estimates the future channel coefficients in both the time and space domains. Linear prediction algorithms that exploit the spatio-temporal characteristics of MIMO wireless channels are described in [12-13 ]. In [13], it is shown that the performance of the prediction algorithm (measured in terms of mean square error or MSE) in a MIMO system improves for higher channel coherence time (i.e. reduced doppler effect) and lower channel coherence distance (due to lower spatial correlation). Thus, the predicted time domain (in seconds) of the space-time method is proportional to the channel coherence time and inversely proportional to the channel coherence distance. In DIDO systems, the low coherence distance is due to the high spatial selectivity produced by the distributed antennas.

Prediction techniques are described herein that exploit the temporal and spatial diversity of the DIDO system to predict the future vector channel (i.e., CSI from BTS to UE). These embodiments exploit the spatial diversity available in the wireless channel to obtain negligible CSI prediction error and extended prediction time domain of any existing SISO and MIMO prediction algorithms. One important feature of these techniques is the utilization of distributed antennas because they receive uncorrelated complex channel coefficients from distributed UEs.

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

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Embodiments of the present invention may include various steps as shown above. The steps may be embodied in machine-executable instructions that cause a general-purpose or special-purpose processor to perform certain steps. For example, various components within the base station/AP and client devices described above may be implemented as software executing on a general purpose or special purpose processor. In order to avoid obscuring aspects of the invention, various well-known personal computer components, such as computer memory, hard drives, input devices, and the like, are not listed.

Alternatively, in one embodiment, the various functional blocks and associated steps illustrated herein may be performed by specific hardware components that contain hardwired logic for performing the steps, such as an application specific integrated circuit ("ASIC"), or by any combination of programmed computer components and custom hardware components.

In one embodiment, certain modules, such as the encoding, modulation, and signal processing logic 903 described above, may be implemented on a programmable digital signal processor ("DSP") (or set of DSPs), such as a DSP (e.g., TMS320C6000, TMS320C5000, …, etc.) using the TMS320x architecture of texas instruments, usa. The DSP in this embodiment may be embedded within an add-on card (such as a PCI card) of the personal computer. Of course, many different DSP architectures may be used while still complying with the underlying principles of the invention.

Elements of the present invention may also be provided as a machine-readable medium for storing the machine-executable instructions. The machine-readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of machine-readable media suitable for storing electronic instructions. For example, the invention may be downloaded as a computer program which may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection).

Throughout the foregoing description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present systems and methods. It will be apparent, however, to one skilled in the art that the systems and methods may be practiced without some of these specific details. Accordingly, the scope and spirit of the invention should be judged in terms of the claims which follow.

Furthermore, throughout the foregoing specification, numerous publications are cited in order to provide a more thorough understanding of the present invention. All of these cited references are incorporated by reference into this patent application.

Claims

1. A distributed antenna system, comprising:

a plurality of subsets of distributed antennas are selected for wireless communication with the plurality of subsets of users based on doppler spread of the users with respect to the distributed antennas.

2. The system of claim 1, wherein the doppler spread is due to user movement or a change in propagation environment.

3. The system of claim 1, the plurality of subsets of users equal to one user.

4. The system of claim 1, the plurality of subsets of users being equal to all users.

5. The system of claim 1, wherein the one or more subsets of the distributed antennas are equal to all of the distributed antennas.

6. The system of claim 1, wherein a first distributed subset of antennas serving the first plurality of users comprises a second distributed subset of antennas serving a second plurality of users.

7. The system of claim 1, wherein a first distributed antenna subset serving the first plurality of users does not include a second distributed antenna subset serving a second plurality of users.

8. The system of claim 1, wherein the plurality of subsets of distributed antennas communicate with the plurality of users at different times.

9. The system of claim 1, wherein the plurality of subsets of distributed antennas communicate with the plurality of users at different frequencies.

10. The system of claim 1, wherein the plurality of subsets of distributed antennas communicate with the plurality of users at different spatial locations.

11. The system according to claim 1, wherein different subsets of distributed antennas are assigned different quality of service indicators, such as data rate, reliability or delay.

12. The system of claim 1, wherein the distributed antenna system reconfigures communications between the distributed antennas and the users to compensate for doppler effects due to user movement or changes in propagation environment.

13. The system of claim 1, employing distributed antennas that utilize spatial, polarization, and/or pattern diversity to increase data rate and/or coverage to one or more users in a wireless system.

14. The system of claim 1, wherein the users are located around or between or surrounded by the distributed antennas.

15. The system of claim 1, wherein the distributed antenna system employs complex weights at a receiver of the uplink channel to demodulate independent data streams (e.g., data or channel state information, CSI) from the users.

16. A method implemented within a distributed antenna system, the method comprising:

17. The method of claim 16, wherein the doppler spread is due to user movement or the propagation environment changes.

18. The method of claim 16, the plurality of subsets of users being equal to one user.

19. The method of claim 16, the plurality of subsets of users being equal to all users.

20. The method of claim 16, wherein the one or more subsets of distributed antennas are equal to all of the distributed antennas.

21. The method of claim 16, wherein serving a first distributed subset of antennas of the first plurality of users comprises serving a second distributed subset of antennas of a second plurality of users.

22. The method of claim 16, wherein a first distributed antenna subset serving the first plurality of users does not include a second distributed antenna subset serving a second plurality of users.

23. The method of claim 16, wherein the multiple subsets of distributed antennas communicate with the multiple users at different times.

24. The method of claim 16, wherein the plurality of subsets of distributed antennas communicate with the plurality of users at different frequencies.

25. The method of claim 16, wherein the multiple subsets of distributed antennas communicate with the multiple users at different spatial locations.

26. The method according to claim 16, wherein different subsets of distributed antennas are assigned different quality of service indicators, such as data rate, reliability or delay.

27. The method of claim 16, wherein the distributed antenna system reconfigures communications between the distributed antennas and the users to compensate for doppler effects due to user movement or changes in propagation environment.

28. The method of claim 16, employing distributed antennas that utilize spatial, polarization, and/or pattern diversity to increase data rate and/or coverage to one or more users in a wireless system.

29. The method of claim 16, wherein the users are located around or between or surrounded by the distributed antennas.

30. The method of claim 16, wherein the distributed antenna system employs complex weights at a receiver of the uplink channel to demodulate independent data streams (e.g., data or channel state information, CSI) from the users.

31. A multi-user, MU, multi-antenna system, MAS, comprising:

a plurality of users;

a plurality of distributed transceiver stations or antennas that cooperatively precode a plurality of data streams to establish a plurality of parallel interference-free data links with the user;

one or more centralized units communicatively coupled to the plurality of distributed transceiver stations or antennas via a network;

the network comprises wired links or wireless links, or a combination of both, employed as backhaul communication channels;

the one or more centralized units communicate with the distributed transceiver stations or antennas via the network to adaptively reconfigure communications between the distributed transceiver stations or antennas and users to compensate for doppler effects due to user movement or changes in propagation environment.

32. A multi-user, MU, multi-antenna system, MAS, comprising:

one or more centralized units communicatively coupled to a plurality of distributed transceiver stations or antennas via a network;

the one or more centralized units communicating with the distributed transceiver stations or antennas through the network to adaptively reconfigure communications between the distributed transceiver stations or antennas and users to compensate for doppler effects due to user movement or changes in propagation environment;

wherein the MU-MAS system comprises one or more groups of user equipments, UEs, base transceiver stations, BTSs, controllers, CTRs, a centralized processor, CP, and base station networks, BSNs; and

wherein the CP adaptively selects the distributed transceiver base station (BTS) for low mobility UEs or high mobility UEs based on the Doppler velocity of each BTS-UE link.

33. A multi-user, MU, multi-antenna system, MAS, comprising:

wherein the CP adaptively selects the distributed Base Transceiver Station (BTS) for low mobility UEs or high mobility UEs based on a delay of the BSN.

34. A multi-user, MU, multi-antenna system, MAS, comprising:

wherein linear prediction is employed to estimate the CSI or MU-MAS precoding weights in the future, thereby eliminating the adverse effect of Doppler effect on the performance of the MU-MAS.

35. A multi-user, MU, multi-antenna system, MAS, comprising:

the plurality of distributed transceiver stations are communicatively coupled to a plurality of user devices over a plurality of wireless links using a plurality of precoding weights;

wherein the radio link between at least two distributed transceiver stations and at least one user equipment experiences a plurality of uncorrelated complex channel coefficients, an

The centralized unit estimates the precoding weights using linear prediction, compensates for mobility effects in the propagation environment and improves the performance of the radio link between the transceiver station and the user equipment.

36. A method implemented within a multi-user, MU, multi-antenna system, MAS, comprising:

The method comprises the following steps: the centralized unit estimates the precoding weights using linear prediction, compensates for mobility effects in the propagation environment and improves the performance of the radio link between the transceiver station and the user equipment.