NZ717370A - Systems and methods to exploit areas of coherence in wireless systems - Google Patents

Abstract

A multiple user (MU)-multiple antenna system (MAS) comprising a plurality of distributed transceivers and a plurality of users. The users are placed in between a subset or the plurality of distributed transceivers, and the distributed transceivers cooperatively create volumes of coherent signals in wireless channels to generate multiple non-interfering data streams with the plurality of users, wherein each volume is confined around only one user and is used to transfer the data stream only to that user. The volumes of coherent signals are created by adding a time offset to the signal component for each user-transceiver pair to negate differences in propagation delays between each user-transceiver pair.

Description

Our Ref: BST195NZ Patents Form No. 5 PATENTS ACT 1953 Divisional Application out of: New Zealand Patent Application No. 622137 which entered the National Phase in New Zealand on 7 March 2014 from PCT/U82012/054937 dated 12 September 2012 and ng priority from US Patent ation Nos. 13/232,996 and 13/233,006 filed 14 September 201 1 COMPLETE SPECIFICATION SYSTEMS AND METHODS TO EXPLOlT AREAS OF COHERENCE IN WIRELESS SYSTEMS We, Rearden, LLC, of 355 Bryant Street, Suite 110, San Francisco, rnia 94107, United States of a, do hereby declare the invention for which we pray that a patent may be granted to us, and the method by which it is to be performed, to be particularly described in and by the following statement: (followed by page 1a) 24XPCT [CORRECTED SPECIFICATION] SYSTEMS AND METHODS TO EXPLOIT AREAS OF COHERENCE IN WIRELESS SYSTEMS RELATED APPLICATIONS This application is a continuation—in-part of the following co-pending US.

Patent Applications: US. Application Serial No. 12/917,257, filed November 1, 2010, entitled ms And Methods To nate Transmissions In Distributed Wireless Systems Via User Clustering” US. Application Serial No. 12/802,988, filed June 16,2010, entitled ference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO) Communication Systems" US. Application Serial No. 12/802,976, filed June 16, 2010, entitled “System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements” US. ation Serial No. 12/802,974, filed June 16, 2010, entitled “System And Method For Managing Inter-Cluster Handoff Of Clients Which Traverse Multiple DIDO Clusters” US. Application Serial No. 12/802,989, filed June 16, 2010, entitled “System And Method For Managing f OfA Client Between Different Distributed-lnput-Distributed-Output (DIDO) Networks Based On Detected Velocity Of The Client” US. Application Serial No. 12/802,958, filed June 16, 2010, entitled m And Method For Power l And Antenna Grouping In A Distributed— lnput—Distributed-Output (DIDO) Network" US. ation Serial No. 12/802,975, filed June 16, 2010, entitled m And Method For Link adaptation In DIDO Multicarrier Systems” US. Application Serial No. 12/802,938, filed June 16, 2010, entitled “System And Method For DIDO Precoding Interpolation In Multicarrier Systems” US. Application Serial No. 12/630,627, filed December 3, 2009, entitled ”System and Method For Distributed Antenna ss Communications” US. Application Serial No. 12/143,503, filed June 20, 2008 entitled ”System and Method For Distributed Input—Distributed Output Wireless Communications”; (followed by page 2) 6181P524XPCT [CORRECTED ICATION] U.S. Application Serial No. 11/894,394, filed August 20, 2007 entitled, ”System and Method for Distributed Input Distributed Output ss ications”; U.S. Application Serial No. 11/894,362, filed August 20, 2007 entitled, “System and method for Distributed Input-Distributed Wireless ications”; U.S. Application Serial No. 11/894,540, filed August 20, 2007 entitled “System and Method For Distributed Input-Distributed Output Wireless Communications” U.S. Application Serial No. 11/256,478, filed October 21, 2005 ed “System and Method For Spatial-Multiplexed Tropospheric Scatter Communications”; U.S. Application Serial No. 10/817,731, filed April 2, 2004 entitled m and Method For Enhancing Near Vertical Incidence Skywave ”) Communication Using Space-Time Coding.

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

A single WiFi base station (e.g., utilizing 2.4 GHz 802.11b, g or n protocols) attached to a broadband wired Internet connection in an area where there are no other WiFi access points (e.g. a WiFi access point attached to DSL within a rural home) is an example of a relatively simple multi-user wireless system that is a single base station that is shared by one or more users that are within its transmission range. If a user is in the same room as the wireless access point, the user will typically experience a high-speed link with few transmission disruptions (e.g. there may be packet loss from 2.4GHz interferers, like microwave ovens, but not from spectrum sharing with other WiFi devices), If a user is a medium distance away or with a few ctions in the path between the user and WiFi access point, the user will likely experience a medium-speed link. If a user is approaching the edge of the range of the WiFi access point, the user will likely experience a low-speed link, and may be subject to periodic drop-outs if changes to the channel result in the signal SNR ng below usable levels. And, finally, if the user is beyond the range of the WiFi base station, the user will have no link at all.

When multiple users access the WiFi base station simultaneously, then the available data throughput is shared among them. Different users will typically 6181P524XPCT [CORRECTED SPECIFICATION] place different hput demands on a WiFi base n at a given time, but at times when the aggregate throughput demands exceed the available throughput from the WiFi base station to the users, then some or all users will receive less data throughput than they are seeking. In an extreme ion where a WiFi access point is shared among a very large number of users, throughput to each user can slow down to a crawl, and worse, data throughput to each user may arrive in short bursts separated by long periods of no data throughput at all, during which time other users are served. This “choppy” data delivery may impair certain applications, like media streaming.

Adding additional WiFi base stations in situations with a large number of users will only help up to a point. Within the 2.4GHz ISM band in the U.S., there are 3 non-interfering channels that can be used for WiFi, and if 3 WiFi base stations in the same coverage area are configured to each use a different non-interfering channel, then the aggregate throughput of the coverage area among multiple users will be increased up to a factor of 3. But, beyond that, adding more WiFi base stations in the same coverage area will not increase aggregate throughput, since they will start g the same available spectrum among them, ually utilizing ivision multiplexed access (TDMA) by “taking turns” using the spectrum. This situation is often seen in coverage areas with high population density, such as within multi-dwelling units. For example, a user in a large apartment building with a WiFi adapter may well experience very poor throughput due to dozens of other interfering WiFi ks (e.g. in other apartments) g other users that are 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. Although the link quality is likely good in that situation, the user would be receiving interference from neighbor WiFi adapters ing in the same frequency band, ng the effective throughput to the user.

Current multiuser wireless systems, including both unlicensed spectrum, such as WiFi, and licensed spectrum, suffer from several limitations. These include coverage area, downlink (DL) data rate and uplink (UL) data rate. Key goals of next generation wireless systems, such as WiMAX and LTE, are to improve coverage area and DL and UL data rate via multiple-input multiple-output (MIMO) technology.

MIMO employs multiple antennas at transmit and receive sides of wireless links to improve link quality ting in wider coverage) or data rate (by creating multiple terfering spatial channels to every user). If enough data rate is available for 6181P524XPCT [CORRECTED SPECIFICATION] every user (note, the terms “user” and t” are used herein interchangeably), r, it may be desirable to exploit channel spatial diversity to create noninterfering channels to multiple users (rather than single user), ing to multiuser MIMO (MU-MIMO) ques. See, e.g., the following references: G. Caire and S. Shamai, “On the achievable throughput of a multiantenna Gaussian broadcast channel,” IEEE Trans. Info.Th., vol. 49, pp. 1691– 1706, July 2003.

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

S. Vishwanath, N. , and A. Goldsmith, “Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels,” IEEE Trans. Info.

Th., vol. 49, pp. 2658–2668, Oct. 2003.

W. Yu and J. Cioffi, “Sum ty of Gaussian vector broadcast channels,” IEEE Trans. Info. Th., vol. 50, pp. 1875–1892, Sep. 2004.

M. Costa, “Writing on dirty paper,” IEEE Transactions on Information Theory, vol. 29, pp. 439–441, May 1983.

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

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

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

For example, in MIMO 4x4 systems (i.e., four transmit and four receive as), 10MHz bandwidth, 16-QAM modulation and forward error correction (FEC) coding with rate 3/4 ing spectral efficiency of 3bps/Hz), the ideal peak data rate achievable at the physical layer for every user is 4x30Mbps=120Mbps, which is much higher than required to deliver high definition video content (which may only e ~10Mbps). In O systems with four transmit antennas, four users and single antenna per user, in ideal scenarios (i.e., independent identically distributed, i.i.d., channels) downlink data rate may be shared across the four users and channel l ity may be exploited to create four parallel 30Mbps data 6181P524XPCT [CORRECTED SPECIFICATION] links to the users.

Different MU-MIMO schemes have been ed as part of the LTE standard as described, for example, in 3GPP, “Multiple Input Multiple Output in UTRA”, 3GPP TR .876 V7.0.0, Mar. 2007; 3GPP, “Base Physical channels and modulation”, TS 36.211, V8.7.0, May 2009; and 3GPP, “Multiplexing and channel coding”, TS 36.212, V8.7.0, May 2009. However, these schemes can provide only up to 2X ement in DL data rate with four transmit as. Practical implementations of MU-MIMO techniques in standard and proprietary cellular systems by companies like ArrayComm (see, e.g., ArrayComm, “Field-proven results”, http://www.arraycomm.com/serve.php?page=proof ) have yielded up to a ~3X se (with four transmit antennas) in DL data rate via space division le access (SDMA). A key limitation of MU-MIMO schemes in cellular networks is lack of l diversity at the transmit side. Spatial diversity is a function of antenna g and multipath angular spread in the ss links. In cellular systems employing MU-MIMO techniques, transmit antennas at a base station are typically red together and placed only one or two wavelengths apart due to limited real estate on antenna support structures (referred to herein as “towers,” whether physically tall or not) and due to tions on where towers may be located.

Moreover, multipath angular spread is low since cell towers are typically placed high up (10 meters or more) above obstacles to yield wider coverage.

Other practical issues with cellular system deployment include excessive cost and limited bility of locations for cellular antenna locations (e.g. due to municipal restrictions on antenna placement, cost of real-estate, physical obstructions, etc.) and the cost and/or availability of network connectivity to the transmitters (referred to herein as “backhaul”). Further, cellular s often have difficulty reaching clients located deeply in buildings due to losses from walls, ceilings, floors, furniture and other impediments.

Indeed, the entire concept of a cellular structure for wide-area network wireless presupposes a rather rigid placement of cellular towers, an alternation of frequencies n adjacent cells, and frequently sectorization, so as to avoid interference among transmitters (either base ns or users) that are using the same frequency. As a result, a given sector of a given cell ends up being a shared block of DL and UL spectrum among all of the users in the cell sector, which is then shared among these users primarily in only the time domain. For example, cellular 6181P524XPCT [CORRECTED SPECIFICATION] systems based on Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA) both share spectrum among users in the time domain. By overlaying such cellular systems with sectorization, perhaps a 2-3X spatial domain benefit can be achieved. And, then by overlaying such cellular systems with a MUMIMO , such as those described previously, perhaps another 2-3X spacetime domain benefit can be achieved. But, given that the cells and sectors of the cellular system are typically in fixed locations, often dictated by where towers can be placed, even such limited benefits are difficult to exploit if user density (or data rate demands) at a given time does not match up well with tower/sector placement. A cellular smart phone user often ences the consequence of this today where the user may be talking on the phone or downloading a web page t any trouble at all, and then after driving (or even walking) to a new location will suddenly see the voice quality drop or the web page slow to a crawl, or even lose the connection entirely. But, on a different day, the user may have the exact te occur in each location. What the user is probably experiencing, ng the environmental conditions are the same, is the fact that user density (or data rate demands) is highly variable, but the ble total spectrum (and thereby total data rate, using prior art techniques) to be shared among users at a given location is largely fixed.

Further, prior art cellular s rely upon using different frequencies in different adjacent cells, lly 3 different frequencies. For a given amount of spectrum, this reduces the available data rate by 3X.

So, in summary, prior art cellular systems may lose perhaps 3X in spectrum utilization due to cellularization, and may improve um utilization by perhaps 3X through sectorization and perhaps 3X more through MU-MIMO techniques, resulting in a net 3*3/3 = 3X potential spectrum utilization. Then, that bandwidth is typically divided up among users in the time domain, based upon what sector of what cell the users fall into at a given time. There are even further inefficiencies that result due to the fact that a given user’s data rate s are typically independent of the user’s location, but the available data rate varies depending on the link quality between the user and the base station. For e, a user further from a cellular base station will typically have less available data rate than a user closer to a base station. Since the data rate is lly shared among all of the users in a given cellular sector, the result of this is that all users are impacted 6181P524XPCT [CORRECTED SPECIFICATION] by high data rate demands from distant users with poor link quality (e.g. on the edge of a cell) since such users will still demand the same amount of data rate, yet they will be consuming more of the shared spectrum to get it.

Other proposed spectrum sharing systems, such as that used by WiFi (e.g., 802.11b, g, and n) and those proposed by the White Spaces Coalition, share spectrum very ciently since simultaneous transmissions by base stations within range of a user result in interference, and as such, the systems utilize collision avoidance and g protocols. These spectrum sharing protocols are within the time domain, and so, when there are a large number of interfering base stations and users, no matter how efficient each base station itself is in spectrum utilization, collectively the base ns are limited to time domain sharing of the spectrum among each other. Other prior art spectrum sharing s rly rely upon r methods to mitigate interference among base stations (be they cellular base stations with antennas on towers or small scale base stations, such as WiFi Access Points (APs)). These methods include limiting transmission power from the base station so as to limit the range of interference, beamforming (via synthetic or physical means) to narrow the area of interference, time-domain multiplexing of spectrum and/or O techniques with multiple red as on the user device, the base station or both. And, in the case of advanced cellular ks in place or d today, frequently many of these techniques are used at once.

But, what is apparent by the fact that even advanced cellular systems can achieve only about a 3X increase in spectrum utilization compared to a single user utilizing the spectrum is that all of these techniques have done little to increase the aggregate data rate among shared users for a given area of coverage. In particular, as a given coverage area scales in terms of users, it becomes increasingly difficult to scale the available data rate within a given amount of spectrum to keep pace with the growth of users. For example, with cellular systems, to increase the aggregate data rate within a given area, typically the cells are subdivided into smaller cells (often called nano-cells or femto-cells). Such small cells can become extremely expensive given the limitations on where towers can be placed, and the requirement that towers must be placed in a fairly ured pattern so as to provide coverage with a minimum of “dead zones”, yet avoid interference between nearby cells using the same frequencies. Essentially, the coverage area must be mapped out, the available locations for placing towers or base stations must be identified, and then 6181P524XPCT [CORRECTED SPECIFICATION] given these constraints, the designers of the cellular system must make do with the best they can. And, of course, if user data rate demands grow over time, then the designers of the cellular system must yet again remap the coverage area, try to find locations for towers or base stations, and once again work within the constraints of the circumstances. And, very often, there simply is no good solution, resulting in dead zones or inadequate aggregate data rate capacity in a coverage area. In other words, the rigid physical placement requirements of a cellular system to avoid interference among towers or base stations utilizing the same frequency results in significant difficulties and constraints in cellular system design, and often is unable to meet user data rate and coverage requirements.

So-called prior art “cooperative” and “cognitive” radio systems seek to increase the al utilization in a given area by using intelligent algorithms within radios such that they can minimize interference among each other and/or such that they can potentially “listen” for other spectrum use so as to wait until the channel is clear. Such systems are proposed for use particularly in unlicensed spectrum in an effort to increase the spectrum utilization of such spectrum.

A mobile ad hoc k (MANET) (see /en.wikipedia.org/wiki/ _ad_hoc_network) is an example of a cooperative self-configuring network intended to provide o-peer communications, and could be used to establish communication among radios without cellular infrastructure, and with sufficiently lowpower communications, can potentially mitigate erence among simultaneous transmissions that are out of range of each other. A vast number of routing protocols have been ed and implemented for MANET systems (see http://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols for a list of dozens of routing protocols in a wide range of classes), but a common theme among them is they are all techniques for g (e.g. ing) transmissions in such a way to minimize itter erence within the available spectrum, towards the goal of particular efficiency or reliability paradigms.

All of the prior art multi-user wireless s seek to improve spectrum utilization within a given coverage area by utilizing techniques to allow for simultaneous spectrum utilization among base stations and multiple users. Notably, in all of these cases, the techniques utilized for simultaneous spectrum utilization among base ns and multiple users achieve the simultaneous spectrum use by multiple users by mitigating interference among the waveforms to the multiple users. 6181P524XPCT [CORRECTED SPECIFICATION] For example, in the case of 3 base stations each using a different frequency to transmit to one of 3 users, there interference is ted because the 3 transmissions are at 3 different frequencies. In the case of sectorization from a base station to 3 different users, each 180 degrees apart relative to the base station, interference is mitigated because the beamforming prevents the 3 transmissions from overlapping at any user.

When such techniques are augmented with MU-MIMO, and, for example, each base station has 4 antennas, then this has the potential to increase downlink throughput by a factor of 4, by creating four non-interfering spatial channels to the users in given coverage area. But it is still the case that some que must be utilized to mitigate the interference among le simultaneous transmissions to le users in different coverage areas.

And, as previously discussed, such prior art techniques (e.g. cellularization, sectorization) not only typically suffer from increasing the cost of the user wireless system and/or the flexibility of deployment, but they typically run into physical or practical limitations of 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 smaller cells. And, in an MU-MIMO system, given the clustered antenna spacing at each base station location, the limited spatial diversity results in asymptotically diminishing returns in hput as more antennas are added to the base station.

And r, in the case of multi-user wireless systems where the user location and density is ictable, it results in ictable (with frequently abrupt changes) in throughput, which is inconvenient to the user and s some applications (e.g. the delivery of services requiring predictable throughput) impractical or of low quality. Thus, prior art multi-user wireless systems still leave much to be desired in terms of their ability to e predictable and/or high-quality services to users.

Despite the extraordinary sophistication and complexity that has been developed for prior art user wireless systems over time, there exist common themes: transmissions are distributed among ent base stations (or ad hoc transceivers) and are structured and/or controlled so as to avoid the RF waveform transmissions from the different base stations and/or different ad hoc transceivers from ering with each other at the receiver of a given user. 6181P524XPCT CTED SPECIFICATION] Or, to put it r way, it is taken as a given that if a user happens to receive transmissions from more than one base station or ad hoc transceiver at the same time, the interference from the multiple aneous transmissions will result in a reduction of the SNR and/or bandwidth of the signal to the user which, if severe enough, will result in loss of all or some of the potential data (or analog information) that would otherwise have been received by the user.

Thus, in a multiuser wireless system, it is necessary to utilize one or more spectrum sharing approaches or another to avoid or mitigate such interference to users from multiple base stations or ad hoc transceivers itting at the same frequency at the same time. There are a vast number of prior art ches to avoiding such interference, including controlling base stations’ physical locations (e.g. cellularization), limiting power output of base stations and/or ad hoc transceivers (e.g. limiting transmit range), beamforming/sectorization, and time domain multiplexing. In short, all of these spectrum sharing systems seek to address the limitation of multiuser wireless systems that when multiple base stations and/or ad hoc transceivers transmitting simultaneously at the same frequency are received by the same user, the resulting interference reduces or destroys the data throughput to the affected user. If a large percentage, or all, of the users in the multi-user wireless system are subject to interference from multiple base stations and/or ad hoc transceivers (e.g. in the event of the malfunction of a component of a multi-user wireless system), then it can result in a ion where the aggregate throughput of the multi-user wireless system is dramatically reduced, or even rendered ctional..

Prior art multi-user wireless systems add complexity and introduce limitations to wireless networks and frequently result in a situation where a given user’s experience (e.g. available bandwidth, latency, predictability, reliability) is impacted by the utilization of the spectrum by other users in the area. Given the increasing demands for aggregate dth within ss spectrum shared by multiple users, and the increasing growth of applications that can rely upon multiuser wireless network ility, predictability and low latency for a given user, it is apparent that prior art multi-user wireless technology suffers from many limitations.

Indeed, with the limited availability of spectrum suitable for particular types of wireless ications (e.g. at wavelengths that are ent in ating building walls), it may be the case that prior art wireless techniques will be 6181P524XPCT CTED SPECIFICATION] cient to meet the increasing demands for bandwidth that is le, predictable and low-latency.

Prior art related to the current invention describes beamforming systems and methods for null-steering in multiuser scenarios. Beamforming was originally conceived to maximize received —to-noise ratio (SNR) by dynamically adjusting phase and/or ude of the signals (i.e., rming weights) fed to the antennas of the array, thereby focusing energy toward the user’s direction. In ser scanarios, beamforming can be used to suppress interfering sources and maximize signal-to-interference-plus-noise ratio (SINR). For example, when beamforming is used at the receiver of a wireless link, the weights are computed to create nulls in the direction of the interfering sources. When beamforming is used at the transmitter in multiuser downlink scenarios, the weights are calculated to pre- cancel inter-user interfence and maximize the SINR to every user. Alternative techniques for multiuser systems, such as BD precoding, compute the precoding weights to maximize throughput in the downlink broadcast channel. The co-pending applications, which are incorporated herein by reference, describe the foregoing techniques (see co—pending applications for specific citations).

It is an object of the invention to provide an improved multiple antenna system and/or method, or at least to provide the public with a useful choice.

In one aspect the invention provides a multiple user (MU)—multiple a system (MAS) comprising a ity of buted transceivers that cooperatively create volumes of coherent signals in wireless channels to generate multiple non-interfering data streams to a plurality of users.

In another aspect the invention provides a method comprising: ng volumes of coherent signals in wireless channels in a multiple user (M U)-multiple antenna system (MAS) with a plurality of distributed cooperative transceivers to generate multiple non-interfering data s to a plurality of users. (followed by page 11a) BRIEF DESCRIPTION OF THE DRAWINGS A better understanding of the present ion can be obtained from the following detailed description in conjunction with the drawings, in which: rates a main DIDO cluster surrounded by neighboring DIDO clusters in one embodiment of the invention. illustrates frequency division multiple access (FDMA) techniques employed in one embodiment of the invention. illustrates time division le access (TDMA) ques employed in one embodiment of the invention. illustrates different types of interfering zones addressed in one embodiment of the invention. illustrates a framework employed in one embodiment of the illustrates a graph showing SER as a function of the SNR, assuming SIR=10dB for the target client in the interfering zone. illustrates a graph showing SER derived from two IDCI—precoding techniques.

I la (followed by page l2) 6181P524XPCT CTED SPECIFICATION] illustrates an exemplary scenario in which a target client moves from a main DIDO cluster to an interfering cluster. illustrates the signal-to-interference-plus-noise ratio (SINR) as a function of distance (D). illustrates the symbol error rate (SER) performance of the three scenarios for 4-QAM tion in flat-fading narrowband channels. illustrates a method for IDCI precoding according to one embodiment of the invention. illustrates the SINR variation in one embodiment as a function of the client’s ce from the center of main DIDO clusters. illustrates one embodiment in which the SER is derived for 4- QAM modulation. illustrates one embodiment of the invention in which a finite state machine implements a handoff algorithm. illustrates depicts one embodiment of a f strategy in the presence of shadowing. illustrates a hysteresis loop ism when switching between any two states in Fig. 93. illustrates one embodiment of a DIDO system with power control. illustrates the SER versus SNR ng four DIDO transmit antennas and four clients in different scenarios. illustrates MPE power density as a function of distance from the source of RF radiation for different values of transmit power according to one embodiment of the invention.

FIGS. 20a-b illustrate different distributions of low-power and high-power DIDO distributed antennas.

FIGS. 21a-b illustrate two power distributions ponding to the urations in Figs. 20a and 20b, respectively. a-b illustrate the rate distribution for the two scenarios shown in Figs. 99a and 99b, respectively. illustrates one ment of a DIDO system with power control. illustrates one embodiment of a method which iterates across all antenna groups according to Round-Robin scheduling policy for transmitting data. illustrates a ison of the uncoded SER performance of 24XPCT [CORRECTED SPECIFICATION] power control with antenna grouping against conventional eigenmode selection in U.S. Patent No. 381.

FIGS. 26a-c illustrate three scenarios in which BD precoding dynamically adjusts the precoding weights to account for different power levels over the wireless links between DIDO as and clients. illustrates the amplitude of low frequency selective ls (assuming ߚ ൌ ͳ) over delay domain or instantaneous PDP (upper plot) and frequency domain (lower plot) for DIDO 2x2 systems illustrates one embodiment of a l matrix frequency response for DIDO 2x2, with a single antenna per client. illustrates one embodiment of a channel matrix frequency response for DIDO 2x2, with a single antenna per client for ls characterized by high frequency selectivity (e.g., with ߚ ൌ ͲǤͳ). illustrates exemplary SER for different QAM schemes (i.e., 4- QAM, 16-QAM, 64-QAM). illustrates one embodiment of a method for enting link adaptation (LA) techniques. illustrates SER performance of one embodiment of the link adaptation (LA) techniques. illustrates the entries of the matrix in equation (28) as a function of the OFDM tone index for DIDO 2x2 systems with ܰிி் ൌ ͸Ͷ and ܮ଴ ൌ ͺ. illustrates the SER versus SNR for ܮ଴ ൌ ͺ, M=Nt=2 transmit antennas and a variable number of P. illustrates the SER mance of one ment of an interpolation method for different DIDO orders and ܮ଴ ൌ ͳ͸. illustrates one embodiment of a system which employs super- clusters, DIDO-clusters and user-clusters. illustrates a system with user clusters according to one embodiment of the invention.

FIGS. 38a-b illustrate link quality metric olds employed in one embodiment of the invention.

FIGS. 39-41 illustrate examples of link-quality matrices for establishing user clusters. 6181P524XPCT [CORRECTED SPECIFICATION] illustrates an embodiment in which a client moves across different different DIDO clusters.

FIGS. 43-46 illustrate relationships between the resolution of spherical arrays and their area A in one embodiment of the invention. illustrates the degrees of freedom of an ary MIMO system in practical indoor and outdoor propagation scenarios. rates the degrees of freedom in an exemplary DIDO system as a function of the array diameter. illustrates a plurality of centralized processors and buted nodes. illustrates a configuration with both unlicensed nodes and licensed nodes. illustrates an embodiment where obsolete unlicensed nodes are covered with a cross. illustrates one embodiment of a cloud wireless system where different nodes communicate with different lized processors.

DETAILED DESCRIPTION One solution to overcome many of the above prior art limitations is an ment of Distributed-Input Distributed-Output (DIDO) technology. DIDO logy is described in the following patents and patent applications, all of which are assigned the assignee of the present patent and are incorporated by reference.

These patents and applications are sometimes referred to tively herein as the “related patents and applications”: U.S. Application Serial No. 12/917,257, filed November 1, 2010, entitled “Systems And Methods To Coordinate issions In Distributed Wireless Systems Via User Clustering” U.S. ation Serial No. 12/802,988, filed June 16, 2010, entitled “Interference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO) ication Systems” U.S. Application Serial No. 12/802,976, filed June 16, 2010, entitled “System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements” U.S. Application Serial No. 12/802,974, filed June 16, 2010, entitled “System And Method For Managing Inter-Cluster Handoff Of s Which Traverse 6181P524XPCT [CORRECTED SPECIFICATION] le DIDO Clusters” U.S. Application Serial No. 12/802,989, filed June 16, 2010, ed “System And Method For Managing Handoff Of A Client Between Different Distributed-Input-Distributed-Output (DIDO) Networks Based On Detected Velocity Of The ” U.S. Application Serial No. 12/802,958, filed June 16, 2010, entitled m And Method For Power Control And Antenna Grouping In A Distributed- Input-Distributed-Output (DIDO) Network” ] U.S. ation Serial No. 12/802,975, filed June 16, 2010, entitled “System And Method For Link adaptation In DIDO Multicarrier Systems” U.S. ation Serial No. 12/802,938, filed June 16, 2010, entitled “System And Method For DIDO Precoding Interpolation In Multicarrier Systems” U.S. Application Serial No. 12/630,627, filed December 2, 2009, entitled ”System and Method For Distributed Antenna Wireless Communications” U.S. Patent No. 7,599,420, filed August 20, 2007, issued Oct. 6, 2009, entitled “System and Method for Distributed Input Distributed Output Wireless Communication”; U.S. Patent No. 7,633,994, filed August 20, 2007, issued Dec. 15, 2009, entitled “System and Method for Distributed Input Distributed Output Wireless Communication”; U.S. Patent No. 7,636,381, filed August 20, 2007, issued Dec. 22, 2009, entitled “System and Method for Distributed Input Distributed Output Wireless Communication”; U.S. Application Serial No. 12/143,503, filed June 20, 2008 entitled, ”System and Method For buted Input-Distributed Output Wireless Communications”; U.S. Application Serial No. 11/256,478, filed October 21, 2005 entitled “System and Method For Spatial-Multiplexed Tropospheric Scatter Communications”; U.S. Patent No. 7,418,053, filed July 30, 2004, issued August 26, 2008, entitled “System and Method for Distributed Input Distributed Output ss Communication”; U.S. ation Serial No. ,731, filed April 2, 2004 entitled “System and Method For Enhancing Near Vertical Incidence Skywave (“NVIS”) 6181P524XPCT [CORRECTED SPECIFICATION] Communication Using Space-Time Coding.

To reduce the size and complexity of the present patent application, the disclosure of some of the related patents and applications is not explicitly set forth below. Please see the d patents and ations for a full detailed description of the sure.

Note that section I below (Disclosure From Related Application Serial No. 12/802,988) utilizes its own set of endnotes which refer to prior art references and prior applications assigned to the assignee of the t application. The endnote citations are listed at the end of section I (just prior to the heading for n II).

Citations in Section II uses may have numerical designations for its citations which overlap with those used in Section I even through these numerical designations identify different references (listed at the end of Section II). Thus, references fied by a particular numerical ation may be identified within the n in which the numerical designation is used.

I. Disclosure From Related Application Serial No. 12/802,988 1. Methods to Remove Inter-cluster Interference Described below are wireless radio ncy (RF) communication systems and methods employing a plurality of distributed transmitting antennas to create locations in space with zero RF energy. When M transmit antennas are employed, it is possible to create up to (M-1) points of zero RF energy in predefined locations. In one embodiment of the invention, the points of zero RF energy are wireless devices and the transmit antennas are aware of the l state information (CSI) between the transmitters and the receivers. In one embodiment, the CSI is computed at the receivers and fed back to the transmitters. In another embodiment, the CSI is computed at the transmitter via training from the receivers, assuming channel reciprocity is exploited. The transmitters may e the CSI to determine the interfering signals to be simultaneously transmitted. In one embodiment, block alization (BD) precoding is employed at the transmit antennas to generate points of zero RF energy.

The system and methods bed herein differ from the conventional e/transmit beamforming techniques described above. In fact, receive beamforming computes the 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 patters that result in one or 6181P524XPCT [CORRECTED SPECIFICATION] multiple locations in space with “zero RF energy.” Unlike conventional transmit beamforming or BD precoding designed to maximize signal quality (or SINR) to every user or downlink throughput, respectively, the systems and methods bed herein minimize signal quality under certain ions and/or from certain transmitters, thereby creating points of zero RF energy at the client s imes referred to herein as “users”). Moreover, in the t of distributedinput distributed-output (DIDO) systems (described in our related patents and applications), transmit antennas distributed in space provide higher degrees of freedom (i.e., higher channel spatial ity) that can be ted to create le points of zero RF energy and/or maximum SINR to different users. For e, with M transmit antennas it is possible to create up to (M-1) points of RF energy. By contrast, practical beamforming or BD multiuser systems are typically designed with closely spaced antennas at the transmit side that limit the number of simultaneous users that can be serviced over the wireless link, for any number of transmit antennas M.

Consider a system with M transmit antennas and K users, with K<M. We assume the transmitter is aware of the CSI (۶ א ׋௄୶ெ) between the M transmit antennas and K users. For simplicity, every user is assumed to be equipped with single antenna, but the same method can be extended to multiple receive as per user. The precoding weights (א ܟ ׋ெ୶ଵ ) that create zero RF energy at the K users’ locations are computed to satisfy the ing condition ۶ܟ ൌ ૙௄୶ଵ where ૙௄୶ଵ is the vector with all zero entries and H is the channel matrix obtained by ing the channel vectors (ܐ௞ א ׋ଵ୶ெ ) from the M transmit antennas to the K users as ڭ ۍ ې ۶ ൌ ܐێ௞ ڭ ێ ۑ ܐۏ௄ے In one embodiment, singular value decomposition (SVD) of the channel matrix H is computed and the precoding weight w is defined as the right singular vector corresponding to the null subspace (identified by zero singular value) of H.

The transmit antennas employ the weight vector defined above to transmit RF energy, while creating K points of zero RF energy at the locations of the K users 6181P524XPCT [CORRECTED SPECIFICATION] such that the signal received at the kth user is given by ”௞ ൌ ܐ௞ܟ• ௞ ൅ ௞ ൌ Ͳ ൅ ௞ where ௞ א ׋ଵ୶ଵ is the additive white Gaussian noise (AWGN) at the kth user.

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

In another ment, the wireless system is a DIDO system and points of zero RF energy are created to pre-cancel interference to the clients between different DIDO coverage areas. In U.S. ation Serial No. 12/630,627, a DIDO system is described which includes: x DIDO clients x DIDO distributed antennas x DIDO base transceiver stations (BTS) x DIDO base station network (BSN) Every BTS is connected via the BSN to multiple buted antennas that provide service to given coverage area called DIDO r. In the present patent application we describe a system and method for removing erence n adjacent DIDO clusters. As illustrated in Figure 1, we assume the main DIDO cluster hosts the client (i.e. a user device served by the multi-user DIDO system) affected by interference (or target client) from the neighbor clusters.

In one embodiment, neighboring clusters operate at different frequencies according to frequency division multiple access (FDMA) techniques similar to conventional cellular systems. For example, with frequency reuse factor of 3, the same carrier frequency is reused every third DIDO cluster as illustrated in Figure 2. In Figure 2, the different carrier ncies are identified as F1, F2 and F3.

While this embodiment may be used in some implementations, this solution yields loss in al efficiency since the available spectrum is d in multiple subbands and only a subset of DIDO rs operate in the same subband.

Moreover, it requires complex cell planning to associate different DIDO clusters to different frequencies, thereby preventing interference. Like prior art cellular systems, such cellular planning requires specific placement of antennas and limiting of transmit power to as to avoid interference between clusters using the same 6181P524XPCT [CORRECTED SPECIFICATION] frequency.

In another embodiment, neighbor clusters e in the same frequency band, but at different time slots according to time division multiple access (TDMA) technique. For example, as illustrated in Figure 3 DIDO transmission is d only in time slots T1, T2, and T3 for certain rs, as illustrated. Time slots can be assigned equally to different clusters, such that different clusters are scheduled according to a Round-Robin . If different clusters are characterized by different data rate requirements (i.e., clusters in crowded urban environments as opposed to clusters in rural areas with fewer number of clients per area of coverage), different priorities are assigned to different clusters such that more time slots are assigned to the clusters with larger data rate requirements. While TDMA as described above may be employed in one embodiment of the invention, a TDMA approach may require time synchronization across different clusters and may result in lower spectral efficiency since interfering rs cannot use the same frequency at the same time.

In one embodiment, all neighboring clusters transmit at the same time in the same frequency band and use spatial processing across clusters to avoid interference. In this ment, the multi-cluster DIDO system: (i) uses conventional DIDO precoding within the main cluster to transmit simultaneous erfering data streams within the same frequency band to multiple s (such as described in the related patents and applications, including 7,599,420; 994; 7,636,381; and Application Serial No. ,503); (ii) uses DIDO precoding with interference cancellation in the neighbor clusters to avoid interference to the s lying in the interfering zones 8010 in Figure 4, by creating points of zero radio frequency (RF) energy at the locations of the target clients. If a target client is in an interfering zone 410, it will receive the sum of the RF containing the data stream from the main cluster 411 and the zero RF energy from the interfering cluster 412- 413, which will simply be the RF containing the data stream from the main cluster.

Thus, adjacent clusters can utilize the same frequency simultaneously without target clients in the interfering zone suffering from interference.

In practical systems, the performance of DIDO precoding may be affected by different s such as: l estimation error or Doppler effects ing obsolete channel state information at the DIDO distributed antennas); odulation distortion (IMD) in multicarrier DIDO systems; time or frequency 6181P524XPCT [CORRECTED SPECIFICATION] offsets. As a result of these effects, it may be impractical to achieve points of zero RF energy. r, as long as the RF energy at the target client from the interfering rs is negligible ed to the RF energy from the main cluster, the link performance at the target client is unaffected by the interference. For example, let us assume the client requires 20dB signal-to-noise ratio (SNR) to demodulate 4-QAM constellations using forward error correction (FEC) coding to achieve target bit error rate (BER) of 10-6 . If the RF energy at the target client received from the interfering cluster is 20dB below the RF energy received from the main cluster, the interference is negligible and the client can demodulate data successfully within the predefined BER target. Thus, the term “zero RF ” as used herein does not necessarily mean that the RF energy from interfering RF signals is zero. Rather, it means that the RF energy is sufficiently low relative to the RF energy of the desired RF signal such that the desired RF signal may be received at the receiver. er, while n desirable olds for interfering RF energy relative to desired RF energy are described, the underlying principles of the invention are not limited to any particular threshold values.

There are ent types of interfering zones 8010 as shown in Figure 4. For e, “type A” zones (as indicated by the letter “A” in Figure 80) are affected by interference from only one neighbor cluster, whereas “type B” zones (as indicated by the letter “B”) account for interference from two or multiple neighbor clusters.

Figure 5 depicts a framework employed in one ment of the invention. The dots denote DIDO distributed antennas, the crosses refer to the DIDO clients and the arrows indicate the directions of propagation of RF energy. The DIDO antennas in the main cluster transmit precoded data s to the clients MC 501 in that cluster. Likewise, the DIDO antennas in the interfering cluster serve the clients IC 502 within that cluster via conventional DIDO precoding. The green cross 503 denotes the target client TC 503 in the interfering zone. The DIDO antennas in the main cluster 511 transmit precoded data signals to the target client (black arrows) via conventional DIDO precoding. The DIDO antennas in the interfering cluster 512 use precoding to create zero RF energy towards the directions of the target client 503 (green arrows).

The received signal at target client k in any interfering zone 410A, B in Figure 4 is given by 6181P524XPCT [CORRECTED SPECIFICATION] ܚ௞ ൌ ۶௞܅௞ܛ௞ ൅۶ ௎ ூ೎ ௞ σ௨ୀଵ ܅௨ ܛ௨ ൅ σ ௖ୀଵ ۶௖ǡ௞ σ ௜ୀଵ ܅௖ǡ௜ ܛ௖ǡ௜ ൅ܖ ௞ (1) where k=1,…, K, with K being the number of clients in the interfering zone 8010A, B, U is the number of clients in the main DIDO cluster, C is the number of interfering DIDO clusters 412-413 and ܫ௖ is the number of clients in the interfering cluster c.

Moreover, ܚ௞ א ׋ே୶ெ is the vector containing the receive data streams at client k, assuming mit DIDO antennas and N receive antennas at the client devices; ܛ௞ א ׋ே୶ଵ is the vector of transmit data streams to client k in the main DIDO cluster; ܛ௨ א ׋ே୶ଵ is the vector of transmit data streams to client u in the main DIDO cluster; ܛ௖ǡ௜ א ׋ே୶ଵ is the vector of transmit data streams to client i in the cth interfering DIDO cluster; ܖ௞ א ׋ே୶ଵ is the vector of additive white Gaussian noise (AWGN) at the N receive antennas of client k; ۶௞ א ׋ே୶ெ is the DIDO channel matrix from the M transmit DIDO antennas to the N receive antennas at client k in the main DIDO cluster; ۶௖ǡ௞ א ׋ே୶ெ is the DIDO channel matrix from the M it DIDO antennas to the N receive antennas t client k in the cth interfering DIDO cluster; ܅௞ א ׋ெ୶ே is the matrix of DIDO precoding weights to client k in the main DIDO cluster; ܅௞ א ׋ெ୶ே is the matrix of DIDO precoding weights to client u in the main DIDO r; ܅௖ǡ௜ א ׋ெ୶ே is the matrix of DIDO precoding s to client i in the cth interfering DIDO cluster.

] To simplify the notation and without loss of generality, we assume all clients are equipped with N receive antennas and there are M DIDO distributed antennas in every DIDO cluster, with ܯ ൒ ሺܰ ȉܷሻ and ܯ ൒ ሺܰ ȉܫ௖ሻǡ׊ܿ ൌ ͳǡǥǡܥ. If M is larger than the total number of receive antennas in the cluster, the extra transmit as are used to pre-cancel interference to the target s in the ering zone or to improve link robustness to the clients within the same cluster via diversity schemes described in the related patents and applications, including 7,599,420; 994; 7,636,381; and Application Serial No. ,503.

The DIDO precoding weights are computed to pre-cancel inter-client interference within the same DIDO cluster. For example, block diagonalization (BD) precoding bed in the related patents and applications, including 7,599,420; 7,633,994; 7,636,381; and Application Serial No. 12/143,503 and [7] can be used to remove inter-client interference, such that the following condition is satisfied in the main cluster ۶௞܅௨ ൌ ૙ே୶ே Ǣ ׊ ݑ ൌ ͳǡǥǡܷǢ ™‹–Š ݑ ് ݇Ǥ (2) 6181P524XPCT CTED SPECIFICATION] The precoding weight matrices in the or DIDO clusters are edsuch that the ing condition is satisfied ۶௖ǡ௞ ܅௖ǡ௜ ൌ ૙ே୶ே Ǣ ׊ ܿ ൌ ͳǡ ǥ ǡ ܥ ƒ† ׊ ݅ ൌ ͳǡ ǥ ǡ ܫ௖Ǥ (3) To compute the ing matrices ܅௖ǡ௜ , the downlink channel from the M transmit antennas to the ܫ௖ clients in the interfering cluster as well as to client k in the interfering zone is estimated and the precoding matrix is computed by the DIDO BTS in the interfering r. If BD method is used to compute the precoding matrices in the interfering clusters, the following effective channel matrix is built to compute the weights to the ith client in the neighbor clusters ۶௖ǡ௞ ۶ഥ௖ǡ௜ ൌ ቈ ቉ (4) ۶෩௖ǡ௜ where ۶෩௖ǡ௜ is the matrix obtained from the channel matrix ۶௖ א ׋ሺேȉூ ೎ሻ୶ெ for the interfering cluster c, where the rows corresponding to the ith client are removed.

Substituting ions (2) and (3) into (1), we obtain the received data streams for target client k, where intra-cluster and inter-cluster interference is removed ܚ௞ ൌ ۶௞܅௞ܛ௞ ൅ ܖ௞. (5) The precoding weights ܅௖ǡ௜ in (1) computed in the or clusters are designed to transmit precoded data streams to all clients in those clusters, while pre-cancelling interference to the target client in the interfering zone. The target client receives precoded data only from its main cluster. In a different embodiment, the same data stream is sent to the target client from both main and neighbor clusters to obtain ity gain. In this case, the signal model in (5) is expressed as ܚ௞ ൌ ൫۶௞܅௞ ൅ σ஼௖ୀଵ ۶௖ǡ௞ ܅௖ǡ௞ ൯ܛ ௞ ൅ ܖ௞ (6) where ܅௖ǡ୩ is the DIDO precoding matrix from the DIDO transmitters in the cth cluster to the target client k in the interfering zone. Note that the method in (6) requires time synchronization across neighboring clusters, which may be complex to achieve in large systems, but eless, is quite feasible if the diversity gain benefit justifies the cost of implementation.

We begin by evaluating the performance of the proposed method in terms of symbol error rate (SER) as a function of the signal-to-noise ratio (SNR).

Without loss of generality, we define the following signal model assuming single 6181P524XPCT [CORRECTED SPECIFICATION] antenna per client and reformulate (1) as ݎ௞ ൌ ξ ܐ ܐ σூ ௞ܟ௞ݏ௞ ൅ ξ ௖ǡ௞ ௜ୀଵ ܟ௖ǡ௜ ݏ௖ǡ௜ ൅ ݊௞ (7) where INR is the interference-to-noise ratio defined as R/SIR and SIR is the signal-to-interference ratio.

Figure 6 shows the SER as a function of the SNR, ng SIR=10dB for the target client in the interfering zone. Without loss of generality, we measured the SER for 4-QAM and 16-QAM without forwards error tion (FEC) coding. We fix the target SER to 1% for uncoded systems. This target corresponds to different values of SNR depending on the modulation order (i.e., dB for 4- QAM and SNR=28dB for 16-QAM). Lower SER targets can be satisfied for the same values of SNR when using FEC coding due to coding gain. We consider the io of two rs (one main cluster and one interfering cluster) with two DIDO antennas and two clients (equipped with single antenna each) per cluster. One of the clients in the main cluster lies in the interfering zone. We assume flat-fading narrowband channels, but the following results can be extended to frequency selective arrier (OFDM) systems, where each subcarrier undergoes flat-fading. We consider two ios: (i) one with inter-DIDO-cluster interference (IDCI) where the precoding weights ܟ௖ǡ୧ are computed t accounting for the target client in the interfering zone; and (ii) the other where the IDCI is removed by computing the weights ܟ௖ǡ୧ to cancel IDCI to the target client. We observe that in presence of IDCI the SER is high and above the ined target. With IDCI-precoding at the neighbor cluster the interference to the target client is removed and the SER targets are reached for SNR>20dB.

The results in Figure 6 assumes IDCI-precoding as in (5). If IDCI- precoding at the neighbor clusters is also used to precode data streams to the target client in the interfering zone as in (6), additional diversity gain is obtained. Figure 7 compares the SER derived from two techniques: (i) “Method 1” using the IDCI- precoding in (5); (ii) “Method 2” employing IDCI-precoding in (6) where the neighbor clusters also transmit precoded data stream to the target . Method 2 yields ~3dB gain compared to conventional IDCI-precoding due to additional array gain provided by the DIDO antennas in the neighbor cluster used to transmit precoded data stream to the target client. More generally, the array gain of Method 2 over Method 1 is proportional to 10*log10(C+1), where C is the number of neighbor 6181P524XPCT [CORRECTED ICATION] clusters and the factor “1” refers to the main cluster.

Next, we te the performance of the above method as a function of the target client’s location with respect to the interfering zone. We consider one simple scenario where a target client 8401 moves from the main DIDO cluster 802 to the interfering cluster 803, as depicted in Figure 8. We assume all DIDO antennas 812 within the main cluster 802 employ BD precoding to cancel intra-cluster interference to satisfy condition (2). We assume single interfering DIDO cluster, single receiver a at the client device 801 and equal pathloss from all DIDO antennas in the main or interfering cluster to the client (i.e., DIDO antennas placed in circle around the client). We use one simplified pathloss model with pathloss nt 4 (as in typical urban environments) [11].

The analysis ter is based on the following simplified signal model that extends (7) to account for pathloss ర ర ݎ௞ ൌ ටୗ୒ୖȉୈ ౥ ܐ௞ܟ௞ݏ௞ ൅ ටୗ୒ୖȉୈ ౥ ܐ௖ǡ௞ σூ௜ୀଵ ܟ௖ǡ௜ ݏ௖ǡ௜ ൅ ݊௞ (8) ୈర ሺଵିୈ ሻర where the signal-to-interference (SIR) is d as SIR=((1-D)/D)4. In modeling the IDCI, we er three scenarios: i) ideal case with no IDCI; ii) IDCI pre-cancelled via BD precoding in the interfering cluster to y condition (3); iii) with IDCI, not pre-cancelled by the neighbor cluster.

Figure 9 shows the signal-to-interference-plus-noise ratio (SINR) as a on of D (i.e., as the target client moves from the main cluster 802 towards the DIDO antennas 813 in the interfering cluster 8403). The SINR is derived as the ratio of signal power and interference plus noise power using the signal model in (8). We assume that Do=0.1 and SNR=50dB for D=Do. In absence of IDCI the wireless link performance is only affected by noise and the SINR decreases due to pathloss. In presence of IDCI (i.e., without IDCI-precoding) the interference from the DIDO antennas in the neighbor cluster contributes to reduce the SINR.

Figure 10 shows the symbol error rate (SER) performance of the three scenarios above for 4-QAM modulation in flat-fading narrowband channels. These SER results correspond to the SINR in Figure 9. We assume SER threshold of 1% for uncoded systems (i.e., without FEC) corresponding to SINR threshold SINR T=20dB in Figure 9. The SINR threshold depends on the modulation order used for data transmission. Higher modulation orders are lly characterized by higher SINRT to achieve the same target error rate. With FEC, lower target SER can 6181P524XPCT [CORRECTED SPECIFICATION] be ed for the same SINR value due to coding gain. In case of IDCI t ing, the target SER is achieved only within the range D<0.25. With IDCI- precoding at the or cluster the range that satisfies the target SER is extended up to D<0.6. Beyond that range, the SINR increases due to pathloss and the SER target is not satisfied.

One embodiment of a method for IDCI precoding is shown in Figure 11 and consists of the following steps: x SIR estimate 1101: Clients estimate the signal power from the main DIDO cluster (i.e., based on received precoded data) and the interference-plus-noise signal power from the neighbor DIDO clusters. In single-carrier DIDO systems, the frame structure can be designed with short s of silence. For example, periods of silence can be defined between training for channel estimation and precoded data transmissions during channel state information (CSI) feedback. In one embodiment, the interference-plus-noise signal power from or clusters is measured during the s of silence from the DIDO antennas in the main cluster. In practical DIDO multicarrier (OFDM) systems, null tones are lly used to prevent direct current (DC) offset and attenuation at the edge of the band due to filtering at transmit and receive sides. In another embodiment employing multicarrier systems, the interference-plus-noise signal power is ted from the null tones. Correction factors can be used to compensate for transmit/receive filter attenuation at the edge of the band. Once the signal-plus-interference-and-noise power (PS) from the main cluster and the interference-plus-noise power from neighbor clusters (PIN ) are estimated, the client computes the SINR as ൌ ୔౏ି୔ ౅ొ . (9) Alternatively, the SINR estimate is derived from the received signal strength indication (RSSI) used in typical wireless communication systems to measure the radio signal power.

We observe the metric in (9) cannot discriminate between noise and interference power level. For example, clients affected by shadowing (i.e., behind obstacles that attenuate the signal power from all DIDO distributed antennas in the main cluster) in interference-free environments may estimate low SINR even though they are not ed by inter-cluster interference. 6181P524XPCT CTED ICATION] A more reliable metric for the proposed method is the SIR computed as ൌ ୔౏ି୔ ౅ొ (10) ୔౅ొ ି୔ ొ where PN is the noise power. In cal arrier OFDM systems, the noise power PN in (10) is estimated from the null tones, assuming all DIDO antennas from main and neighbor clusters use the same set of null tones. The interference-plusnoise power (PIN ), is estimated from the period of silence as mentioned above.

Finally, the signal-plus-interference-and-noise power (PS) is derived from the data tones. From these estimates, the client computes the SIR in (10). x Channel estimation at neighbor clusters 1102-1103: If the estimated SIR in (10) is below predefined threshold (SIRT), determined at 8702 in Figure 11, the client starts listening to training signals from neighbor clusters. Note that SIRT depends on the modulation and FEC coding scheme (MCS) used for data transmission. Different SIR targets are defined depending on the client’s MCS.

When DIDO distributed antennas from different clusters are time-synchronized (i.e., locked to the same pulse-per-second, PPS, time reference), the client exploits the training sequence to deliver its channel estimates to the DIDO antennas in the neighbor clusters at 8703. The training sequence for channel estimation in the or clusters are designed to be orthogonal to the training from the main cluster.

Alternatively, when DIDO antennas in different clusters are not time-synchronized, orthogonal sequences (with good cross-correlation properties) are used for time synchronization in different DIDO clusters. Once the client locks to the time/frequency reference of the neighbor clusters, channel estimation is carried out at 1103. x IDCI Precoding 1104: Once the channel estimates are available at the DIDO BTS in the neighbor rs, IDCI-precoding is computed to satisfy the condition in (3). The DIDO antennas in the neighbor clusters transmit precoded data streams only to the s in their cluster, while pre-cancelling interference to the clients in the interfering zone 410 in Figure 4. We observe that if the client lies in the type B ering zone 410 in Figure 4, erence to the client is generated by multiple clusters and recoding is carried out by all neighbor clusters at the same time. 6181P524XPCT [CORRECTED SPECIFICATION] Methods for Handoff Hereafter, we describe different handoff methods for clients that move across DIDO clusters populated by distributed antennas that are located in te areas or that provide different kinds of services (i.e., low- or high-mobility services). a. Handoff Between Adjacent DIDO Clusters In one embodiment, the IDCI-precoder to remove inter-cluster interference described above is used as a baseline for handoff s in DIDO systems. tional handoff in cellular systems is conceived for clients to switch seamlessly across cells served by different base stations. In DIDO systems, handoff allows clients to move from one cluster to r without loss of connection.

To illustrate one embodiment of a f strategy for DIDO systems, we er again the example in Figure 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 a f method dynamically calculates the signal y in different clusters and s the cluster that yields the lowest error rate performance to the client.

Figure 12 shows the SINR variation as a function of the client’s distance from the center of clusters C1. For 4-QAM modulation t FEC coding, we consider target SINR=20dB. The line identified by circles represents the SINR for the target client being served by the DIDO antennas in C1, when both C1 and C2 use DIDO precoding without erence cancellation. The SINR decreases as a function of D due to pathloss and interference from the neighboring cluster. When IDCI-precoding is implemented at the neighboring cluster, the SINR loss is only due to pathloss (as shown by the line with triangles), since interference is completely removed. Symmetric behavior is experienced when the client is served from the neighboring cluster. One embodiment of the handoff strategy is defined such that, as the client moves from C1 to C2, the algorithm switches n different DIDO schemes to maintain the SINR above predefined target.

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

One embodiment of the handoff strategy is as follows. x C1-DIDO and C2-DIDO precoding: When the client lies within C1, away from the interfering zone, both clusters C1 and C2 e with conventional DIDO 6181P524XPCT [CORRECTED SPECIFICATION] precoding independently. x O and I precoding: As the client moves towards the interfering zone, its SIR or SINR degrades. When the target SINRT1 is reached, the target client starts estimating the channel from all DIDO antennas in C2 and provides the CSI to the BTS of C2. The BTS in C2 computes IDCI-precoding and transmits to all clients in C2 while preventing interference to the target client. For as long as the target client is within the interfering zone, it will ue to provide its CSI to both C1 and C2. x C1-IDCI and C2-DIDO precoding: As the client moves towards C2, its SIR or SINR keeps decreasing until it again s a target. At this point the client decides to switch to the neighbor cluster. In this case, C1 starts using the CSI from the target client to create zero interference towards its direction with IDCI-precoding, whereas the neighbor cluster uses the CSI for tional DIDO-precoding. In one embodiment, as the SIR estimate approaches the target, the clusters C1 and C2 try both DIDO- and recoding schemes alternatively, to allow the client to estimate the SIR in both cases. Then the client selects the best scheme, to ze certain error rate performance metric. When this method is d, the cross-over point for the handoff strategy occurs at the intersection of the curves with triangles and rhombus in Figure 12. One embodiment uses the modified IDCI-precoding m ethod described in (6) where the neighbor cluster also transmits precoded data stream to the target client to provide array gain. With this approach the handoff strategy is simplified, since the client does not need to estimate the SINR for both strategies at the cross-over point. x O and C2-DIDO precoding: As the client moves out of the interference zone towards C2, the main cluster C1 stops pre-cancelling interference towards that target client via recoding and switches back to conventional DIDO-precoding to all s remaining in C1. This final cross-over point in our handoff strategy is useful to avoid unnecessary CSI feedback from the target client to C1, thereby reducing the overhead over the ck channel. In one embodiment a second target SINRT2 is defined. When the SINR (or SIR) increases above this target, the strategy is switched to C1-DIDO and C2-DIDO. In one embodiment, the cluster C1 keeps alternating between DIDO- and IDCI-precoding to allow the client to estimate the SINR. Then the client selects the method for C1 that 6181P524XPCT [CORRECTED SPECIFICATION] more closely approaches the target SINRT1 from above.

The method described above es the SINR or SIR estimates for different schemes in real time and uses them to select the optimal scheme. In one embodiment, the handoff algorithm is designed based on the finite-state machine rated in Figure 14. The client keeps track of its current state and switches to the next state when the SINR or SIR drops below or above the predefined thresholds illustrated in Figure 12. As discussed above, in state 1201, both clusters C1 and C2 operate with conventional DIDO precoding independently and the client is served by cluster C1; in state 1202, the client is served by cluster C1, the BTS in C2 computes IDCI-precoding and cluster C1 operates using conventional DIDO precoding; in state 1203, the client is served by cluster C2, the BTS in C1 computes IDCI-precoding and cluster C2 operates using conventional DIDO precoding; and in state 1204, the client is served by cluster C2, and both clusters C1 and C2 operate with conventional DIDO precoding independently.

In presence of shadowing effects, the signal quality or SIR may fluctuate around the thresholds as shown in Figure 15, g repetitive switching between consecutive states in Figure 14. ng states repetitively is an undesired , since it results in significant ad on the control channels between clients and BTSs to enable ing between transmission schemes.

Figure 15 depicts one example of a handoff strategy in the presence of shadowing.

In one embodiment, the shadowing coefficient is simulated according to log-normal bution with ce 3 [3]. Hereafter, we define some methods to prevent tive switching effect during DIDO handoff.

One embodiment of the invention employs a hysteresis loop to cope with state switching effects. For example, when ing n “C1-DIDO,C2- IDCI” 9302 and “C1-IDCI,C2-DIDO” 9303 states in Figure 14 (or vice versa) the threshold SINRT1 can be adjusted within the range A1. This method avoids tive switches between states as the signal quality oscillates around SINRT1 . For example, Figure 16 shows the hysteresis loop mechanism when switching between any two states in Figure 14. To switch from state B to A the SIR must be larger than (SIR T1 +A 1/2), but to switch back from A to B the SIR must drop below (SIRT1 -A 1/2).

In a different embodiment, the threshold SINRT2 is adjusted to avoid repetitive switching between the first and second (or third and fourth) states of the finite-state machine in Figure 14. For example, a range of values A2 may be defined 6181P524XPCT [CORRECTED SPECIFICATION] such that the threshold SINRT2 is chosen within that range depending on channel condition and shadowing effects.

In one embodiment, depending on the variance of shadowing expected over the wireless link, the SINR threshold is dynamically adjusted within the range [SINR T2 , SINRT2 +A 2]. The ce of the log-normal distribution can be ted from the variance of the received signal strength (or RSSI) as the client moves from its current cluster to the or cluster.

The methods above assume the client triggers the handoff strategy. In one embodiment, the handoff decision is deferred to the DIDO BTSs, assuming communication across multiple BTSs is enabled.

] For simplicity, the methods above are derived assuming no FEC coding and 4-QAM. More generally, the SINR or SIR thresholds are derived for different tion coding schemes (MCSs) and the f strategy is designed in combination with link adaptation (see, e.g., U.S. Patent No. 7,636,381) to optimize nk data rate to each client in the interfering zone. b. Handoff Between Low- and High-Doppler DIDO Networks DIDO systems employ closed-loop transmission schemes to precode data streams over the nk channel. Closed-loop schemes are inherently constrained by latency over the feedback channel. In practical DIDO systems, computational time can be reduced by transceivers with high processing power and it is expected that most of the y is introduced by the DIDO BSN, when delivering CSI and baseband precoded data from the BTS to the distributed antennas. The BSN can be comprised of s k technologies including, but not limited to, digital subscriber lines (DSL), cable modems, fiber rings, T1 lines, hybrid fiber coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi). ted fiber typically has very large bandwidth and low latency, potentially less than a millisecond in local region, but it is less widely ed than DSL and cable modems. Today, DSL and cable modem connections typically have between 10- 25ms in last-mile latency in the United States, but they are very widely deployed.

The maximum latency over the BSN determines the maximum Doppler frequency that can be ted over the DIDO wireless link without performance degradation of DIDO precoding. For example, in [1] we showed that at the carrier frequency of 400MHz, networks with latency of about 10msec (i.e., DSL) can tolerate clients’ velocity up to 8mph (running , whereas networks with 1msec latency 6181P524XPCT [CORRECTED ICATION] (i.e., fiber ring) can support speed up to 70mph (i.e., y traffic).

We define two or multiple DIDO sub-networks depending on the maximum Doppler frequency that can be tolerated over the BSN. For example, a BSN with high-latency DSL connections between the DIDO BTS and distributed antennas can only deliver low mobility or fixed-wireless services (i.e., low-Doppler network ), whereas a low-latency BSN over a low-latency fiber ring can tolerate high mobility (i.e., oppler network). We observe that the majority of broadband users are not moving when they use broadband, and further, most are unlikely to be d near areas with many high speed objects moving by (e.g., next to a y) since such locations are typically less desirable places to live or operate an office.

However, there are broadband users who will be using broadband at high speeds (e.g., while in a car driving on the highway) or will be near high speed objects (e.g., in a store located near a highway). To address these two differing user Doppler scenarios, in one ment, a ppler DIDO network consists of a typically larger number of DIDO antennas with vely low power (i.e., 1W to 100W, for indoor or rooftop installation) spread across a wide area, whereas a oppler network consists of a typically lower number of DIDO antennas with high power transmission (i.e., 100W for p or tower installation). The low-Doppler DIDO network serves the typically larger number of low-Doppler users and can do so at typically lower connectivity cost using inexpensive high-latency broadband connections, such as DSL and cable modems. The high-Doppler DIDO network serves the typically fewer number of high-Doppler users and can do so at typically higher tivity cost using more expensive low-latency broadband connections, such as fiber.

To avoid interference across different types of DIDO networks (e.g. low-Doppler and high-Doppler), different multiple access techniques can be employed such as: time division multiple access (TDMA), ncy division multiple access (FDMA), or code division multiple access .

Hereafter, we propose methods to assign clients to different types of DIDO networks and enable handoff between them. The network selection is based on the type of mobility of each client. The client’s velocity (v) is proportional to the maximum Doppler shift according to the following equation [6] ݂ௗ ൌ ௩ •‹ ߠ (11) 6181P524XPCT [CORRECTED SPECIFICATION] where fd is the maximum Doppler shift, ߣ is the wavelength corresponding to the carrier frequency and ߠ is the angle between the vector indicating the direction itter-client and the velocity vector.

In one embodiment, the Doppler shift of every client is ated via blind estimation techniques. For example, the Doppler shift can be estimated by sending RF energy to the client and analyzing the reflected signal, similar to Doppler radar s.

] In another embodiment, one or multiple DIDO antennas send training s 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 spectrum analysis. We observe that for fixed velocity v and client’s trajectory, the angular velocity ݒ •‹ ߠ in (11) may depend on the relative ce of the client from every DIDO antenna. For example, DIDO antennas in the proximity of a moving client yield larger angular velocity and Doppler shift than faraway as.

In one embodiment, the Doppler velocity is estimated from multiple DIDO antennas at different distances from the client and the average, ed average or standard deviation is used as an indicator for the client’s mobility. Based on the estimated Doppler indicator, the DIDO BTS decides whether to assign the client to low- or high- Doppler networks.

The Doppler indicator is periodically monitored for all clients and sent back to the BTS. When one or multiple clients change their Doppler velocity (i.e., client riding in the bus versus client g or sitting), those s are dynamically re-assigned to different DIDO network that can tolerate their level of mobility.

Although the Doppler of low-velocity s can be affected by being in the vicinity of high-velocity objects (e.g. near a highway), the Doppler is typically far less than the Doppler of clients that are in motion themselves. As such, in one embodiment, the velocity of the client is estimated (e.g. by using a means such as monitoring the clients position using GPS), and if the velocity is low, the client is ed to a low-Doppler network, and if the velocity if high, the client is ed to a high-Doppler network.

Methods for Power Control and Antenna Grouping The block m of DIDO systems with power control is depicted in 6181P524XPCT [CORRECTED SPECIFICATION] Figure 17. One or multiple data streams (sk) for every client (1,…,U) are first multiplied by the weights generated by the DIDO precoding unit. ed data streams are lied by power scaling factor computed by the power control unit, based on the input channel y information (CQI). The CQI is either fed back from the clients to DIDO BTS or derived from the uplink channel assuming downlink channel reciprocity. The U precoded s for different clients are then combined and multiplexed into M data s (tm), one for each of the M transmit antennas. Finally, the streams tm are sent to the digital-to-analog converter (DAC) unit, the radio frequency (RF) unit, power amplifier (PA) unit and finally to the antennas.

] The power control unit measures the CQI for all clients. In one embodiment, the CQI is the average SNR or RSSI. The CQI varies for different clients depending on pathloss or shadowing. Our power control method adjusts the transmit power scaling factors Pk for different clients and lies them by the precoded data streams generated for different clients. Note that one or multiple data streams may be generated for every client, depending on the number of clients’ e antennas.

To evaluate the performance of the proposed , we defined the following signal model based on (5), including pathloss and power control parameters ܚ௞ ൌ ඥ ୩ Ƚ ୩ ۶ ௞܅௞ܛ௞ ൅ܖ ௞ (12) where k=1,…, U, U is the number of clients, SNR=Po/N o, with Po being the average transmit power, No the noise power and Ƚ୩ the pathloss/shadowing coefficient. To model pathloss/shadowing, we use the following simplified model Ƚ୩ ൌ ‡ି௔ ೆ (13) where a=4 is the pathloss exponent and we assume the pathloss ses with the s’ index (i.e., clients are located at increasing distance from the DIDO antennas).

Figure 18 shows the SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios. The ideal case assumes all clients have the same pathloss (i.e., a=0), yielding Pk=1 for all clients. The plot with squares refers to the case where clients have different pathloss coefficients and no power control. The curve with dots is derived from the same scenario (with pathloss) where 6181P524XPCT [CORRECTED SPECIFICATION] the power control coefficients are chosen such that ܲ௞ ൌ ͳȀȽ୩. With the power control method, more power is assigned to the data streams intended to the clients that undergo higher pathloss/shadowing, resulting in 9dB SNR gain (for this particular scenario) ed to the case with no power control.

The Federal Communications Commission (FCC) (and other international regulatory agencies) defines constraints on the maximum power that can be transmitted from ss devices to limit the exposure of human body to electromagnetic (EM) radiation. There are two types of limits [2]: i) “occupational/controlled” limit, where people are made fully aware of the radio frequency (RF) source via fences, warnings or labels; ii) “general population/uncontrolled” limit where there is no control over the exposure.

Different on levels are defined for different types of wireless devices. In l, DIDO distributed antennas used for indoor/outdoor applications qualify for the FCC category of “mobile” devices, defined as [2]: “transmitting devices designed to be used in other than fixed locations that would normally be used with radiating structures maintained 20 cm or more from the body of the user or nearby persons.” ] The EM emission of e” devices is measured in terms of maximum permissible exposure (MPE), expressed in mW/cm2. Figure 19 shows the MPE power density as a function of distance from the source of RF radiation for different values of transmit power at 700MHz carrier frequency. The maximum allowed transmit power to meet the FCC “uncontrolled” limit for devices that typically operate beyond 20cm from the human body is 1W.

Less ctive power emission aints are d for transmitters installed on rooftops or buildings, away from the “general population”. For these “rooftop transmitters” the FCC defines a looser emission limit of 1000W, measured in terms of effective radiated power (ERP).

Based on the above FCC constraints, in one embodiment we define two types of DIDO distributed antennas for practical systems: x Low-power (LP) itters: located re (i.e., indoor or outdoor) at any height, with maximum transmit power of 1W and 5Mbps consumer-grade 6181P524XPCT [CORRECTED SPECIFICATION] broadband (e.g. DSL, cable modem, Fibe To The Home (FTTH)) backhaul connectivity. x ower (HP) transmitters: rooftop or building mounted antennas at height of approximately 10 meters, with transmit power of 100W and a commercialgrade broadband (e.g. optical fiber ring) backhaul (with effectively “unlimited” data rate compared to the throughput available over the DIDO wireless links).

Note that LP transmitters with DSL or cable modem connectivity are good candidates for ppler DIDO networks (as described in the previous section), since their clients are mostly fixed or have low mobility. HP transmitters with commercial fiber connectivity can tolerate higher client’s ty and can be used in high-Doppler DIDO ks.

To gain practical intuition on the mance of DIDO systems with different types of LP/HP transmitters, we consider the practical case of DIDO antenna installation in wn Palo Alto, CA. Figure 20a shows a random distribution of NLP =100 wer DIDO distributed antennas in Palo Alto. In Figure 20b , 50 LP antennas are substituted with NHP =50 high-power transmitters.

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

Figures 22a-b depict the rate distribution for the two scenarios above.

The throughput (expressed in Mbps) is derived based on power thresholds for different modulation coding s defined in the 3GPP long-term evolution (LTE) standard in [4,5]. The total ble bandwidth is fixed to 10MHz at 700MHz carrier frequency. Two different frequency allocation plans are considered: i) 5MHz spectrum allocated only to the LP stations; ii) 9MHz to HP transmitters and 1MHz to LP transmitters. Note that lower bandwidth is typically allocated to LP ns due to their DSL backhaul connectivity with limited hput. Figures 22a-b shows that when using 50% of HP itters it is possible to increase icantly the rate distribution, raising the e per-client data rate from 2.4Mbps in Figure 22a to 6181P524XPCT [CORRECTED SPECIFICATION] 38Mbps in Figure 22b.

Next, we d algorithms to control power transmission of LP stations such that higher power is allowed at any given time, thereby sing the throughput over the downlink channel of DIDO systems in Figure 22b. We observe that the FCC limits on the power density is d based on average over time as ܵ ൌ σ೙సభ ௌ೙ ௧ ೙ (14) ்ಾುಶ where ܶெ௉ா ൌ σே௡ୀଵ ݐ௡ is the MPE averaging time, ݐ௡ is the period of time of exposure to radiation with power density ܵ௡. For olled” exposure the average time is 6 minutes, whereas for “uncontrolled” exposure it is increased up to 30 minutes. Then, any power source is allowed to transmit at larger power levels than the MPE limits, as long as the e power y in (14) satisfies the FCC limit over 30 minute average for “uncontrolled” exposure.

Based on this analysis, we define adaptive power control methods to increase instantaneous per-antenna transmit power, while maintaining average power per DIDO antenna below MPE limits. We consider DIDO systems with more transmit antennas than active clients. This is a reasonable assumption given that DIDO antennas can be conceived as inexpensive wireless devices (similar to WiFi access points) and can be placed anywhere there is DSL, cable modem, optical fiber, or other Internet connectivity.

The framework of DIDO systems with adaptive per-antenna power control is depicted in Figure 23. The amplitude of the digital signal coming out of the lexer 234 is dynamically adjusted with power scaling s S1,…,S M, before being sent to the DAC units 235. The power scaling factors are computed by the power control unit 232 based on the CQI 233.

In one embodiment, Ng DIDO antenna groups are defined. Every group contains at least as many DIDO antennas as the number of active s (K). At any given time, only one group has Na>K active DIDO antennas transmitting to the clients at larger power level (So) than MPE limit (ܯܲܧതതതതതത). One method iterates across all antenna groups according to Round-Robin scheduling policy depicted in Figure 24 . In another embodiment, different scheduling ques (i.e., proportional-fair scheduling [8]) are ed for cluster selection to ze error rate or throughput 6181P524XPCT [CORRECTED SPECIFICATION] performance.

Assuming Round-Robin power allocation, from (14) we derive the average transmit power for every DIDO antenna as ܵ ൌ ܵ௢ ௧೚ ൑ ܯܲܧതതതതതത (15) ்ಾುಶ where to is the period of time over which the antenna group is active and TMPE =30min is the average time defined by the FCC guidelines [2]. The ratio in (15) is the duty factor (DF) of the groups, defined such that the average transmit power from every DIDO antenna satisfies the MPE limit (ܯܲܧതതതതതത). The duty factor depends on the number of active clients, the number of groups and active antennas per-group, according to the following definition ܨܦ ؜ ௄ ൌ ௧೚ . (16) ே೒ேೌ ்ಾುಶ The SNR gain (in dB) obtained in DIDO systems with power control and antenna grouping is expressed as a function of the duty factor as ܩௗ஻ ൌ ͳͲ Ž‘‰ଵ଴ ቀ ଵ ቁ. (17) We observe the gain in (17) is ed at the expense of GdB additional transmit power across all DIDO antennas.

In general, the total transmit power from all Na of all Ng groups is defined as ܲത ൌ σ σேೌ ௜ୀଵ ܲ௜௝ (18) where the Pij is the average per-antenna transmit power given by ܲ௜௝ ൌ ଵ ׬்ಾುಶ ܵ௜௝ ሺݐሻ ݀ݐ ൑ ܯܲܧതതതതതതത (19) ்ಾುಶ ଴ and Sij (t) is the power spectral density for the ith it antenna within the jth group.

In one ment, the power al density in (19) is designed for every antenna to optimize error rate or throughput performance.

To gain some intuition on the performance of the proposed method, er 400 DIDO distributed antennas in a given coverage area and 400 clients subscribing to a wireless Internet service offered over DIDO systems. It is ly that every Internet connection will be fully ed all the time. Let us assume that % of the clients will be actively using the wireless Internet connection at any given time. Then, 400 DIDO as can be divided in Ng=10 groups of Na=40 antennas each, every group serving K=40 active clients at any given time with duty factor 6181P524XPCT [CORRECTED SPECIFICATION] DF=0.1. The SNR gain resulting from this transmission scheme is GdB =10log 10 =10dB, provided by 10dB additional transmit power from all DIDO antennas. We observe, however, that the average per-antenna transmit power is constant and is within the MPE limit.

Figure 25 compares the (uncoded) SER performance of the above power control with antenna grouping against conventional ode selection in U.S. Patent No. 381. All schemes use BD ing with four clients, each client equipped with single antenna. The SNR refers to the ratio of per-transmitantenna power over noise power (i.e., per-antenna transmit SNR). The curve denoted with DIDO 4x4 assumes four transmit antenna and BD precoding. The curve with s s the SER performance with two extra transmit antennas and BD with eigenmode selection, yielding 10dB SNR gain (at 1% SER target) over tional BD ing. Power control with antenna grouping and DF=1/10 yields 10dB gain at the same SER target as well. We observe that eigenmode selection s the slope of the SER curve due to diversity gain, whereas our power control method shifts the SER curve to the left (maintaining the same slope) due to increased average transmit power. For comparison, the SER with larger duty factor DF=1/50 is shown to provide additional 7dB gain ed to DF=1/10.

Note that our power control may have lower complexity than conventional eigenmode selection methods. In fact, the antenna ID of every group can be pre-computed and shared among DIDO as and clients via lookup tables, such that only K channel estimates are required at any given time. For eigenmode selection, (K+2 ) channel estimates are computed and additional computational sing is required to select the eigenmode that minimizes the SER at any given time for all clients.

Next, we describe another method involving DIDO antenna grouping to reduce CSI feedback overhead in some special scenarios. Figure 26a shows one scenario where clients (dots) are spread randomly in one area covered by multiple DIDO distributed antennas (crosses). The average power over every transmitreceive wireless link can be computed as ۯ ൌ ሼȁ ۶ȁଶሽ. (20) where H is the channel estimation matrix available at the DIDO BTS.

The matrices A in s 26a-c are obtained numerically by averaging the channel matrices over 1000 instances. Two alternative scenarios are 6181P524XPCT [CORRECTED SPECIFICATION] depicted in Figure 26b and Figure 26c, respectively, where clients are grouped er around a subset of DIDO antennas and receive negligible power from DIDO antennas located far away. For example, Figure 26b shows two groups of antennas yielding block diagonal matrix A. One extreme io is when every client is very close to only one transmitter and the transmitters are far away from one another, such that the power from all other DIDO antennas is negligible. In this case, the DIDO link degenerates in multiple SISO links and A is a diagonal matrix as in Figure 26c .

In all three scenarios above, the BD precoding dynamically adjusts the precoding weights to account for different power levels over the wireless links between DIDO antennas and clients. It is convenient, however, to identify multiple groups within the DIDO cluster and operate DIDO precoding only within each group.

Our proposed grouping method yields the following advantages: x Computational gain: DIDO ing is ed only within every group in the cluster. For example, if BD precoding is used, singular value decomposition (SVD) has complexity O(n3), where n is the minimum ion of the channel matrix H. If H can be reduced to a block diagonal matrix, the SVD is ed for every block with reduced xity. In fact, if the channel matrix is divided into two block matrices with dimensions n1 and n2 such that n=n1+n 2, the complexity of the SVD is only O(n13)+O(n 3)<O(n 3). In 2 the extreme case, if H is diagonal matrix, the DIDO link reduce to multiple SISO links and no SVD calculation is required. x Reduced CSI feedback overhead: When DIDO antennas and clients are divided into groups, in one embodiment, the CSI is computed from the clients to the antennas only within the same group. In TDD systems, assuming channel reciprocity, antenna grouping reduces the number of l estimates to compute the channel matrix H. In FDD s where the CSI is fed back over the wireless link, antenna grouping further yields reduction of CSI feedback overhead over the wireless links between DIDO antennas and clients.

Multiple Access Techniques for the DIDO Uplink l In one embodiment of the invention, different multiple access techniques are defined for the DIDO uplink channel. These techniques can be used 6181P524XPCT [CORRECTED SPECIFICATION] to feedback the CSI or transmit data streams from the clients to the DIDO as over the uplink. ter, we refer to feedback CSI and data streams as uplink streams . x Multiple-input multiple-output (MIMO): the uplink streams are transmitted from the client to the DIDO antennas via open-loop MIMO multiplexing schemes. This method assumes all clients are time/frequency synchronized. In one embodiment, synchronization among s is achieved via training from the nk 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 the clocks of different clients that may affect the performance of MIMO uplink scheme. After the clients send uplink streams via MIMO multiplexing schemes, the receive DIDO antennas may use near (i.e., maximum likelihood, ML) or linear (i.e., zeros-forcing, minimum mean squared error) receivers to cancel nnel interference and demodulate the uplink streams individually. x Time division multiple access (TDMA): Different clients are assigned to different time slots. Every client sends its uplink stream when its time slot is available. x ncy division multiple access (FDMA): Different clients are ed to different carrier frequencies. In multicarrier (OFDM) systems, subsets of tones are assigned to different clients that it the uplink streams simultaneously, thereby reducing latency. x Code division multiple access (CDMA): Every client is assigned to a different -random sequence and orthogonality across clients is achieved in the code domain.

In one embodiment of the invention, the clients are wireless devices that it at much lower power than the DIDO antennas. In this case, the DIDO BTS defines client sub-groups based on the uplink SNR information, such that interference across sub-groups is minimized. Within every sub-group, the above multiple access techniques are employed to create orthogonal channels in time, frequency, space or code domains thereby avoiding uplink interference across different clients.

] In r embodiment, the uplink multiple access techniques 6181P524XPCT [CORRECTED SPECIFICATION] described above are used in combination with antenna grouping methods presented in the previous section to define different client groups within the DIDO cluster.

System and Method for Link tion in DIDO Multicarrier Systems Link adaptation methods for DIDO systems exploiting time, ncy and space selectivity of wireless channels were defined in U.S. Patent No. 7,636,381. Described below are embodiments of the invention for link adaptation in multicarrier (OFDM) DIDO systems that exploit time/frequency selectivity of wireless channels.

We simulate Rayleigh fading ls according to the exponentially decaying power delay profile (PDP) or Saleh-Valenzuela model in [9]. For simplicity, we assume single-cluster channel with multipath PDP defined as ܲ௡ ൌ ݁ ିఉ௡ (21) where n=0,…, L-1, is the index of the channel tap, L is the number of channel taps and ߚ ൌ ͳȀߪ஽ௌ is the PDP exponent that is an indicator of the channel coherence bandwidth, inverse proportional to the channel delay spread (ߪ஽ௌ ). Low values of ߚ yield ncy-flat channels, whereas high values of ߚ produce frequency selective channels. The PDP in (21) is normalized such that the total average power for all L channel taps is unitary ܲ௡ഥ ൌ ௉೙ (22) σಽషభ ೔సబ ௉೔ Figure 27 depicts the amplitude of low frequency selective channels ing ߚ ൌ ͳ) over delay domain or instantaneous PDP(upper plot) and frequency domain (lower plot) for DIDO 2x2 systems. The first ipt indicates the client, the second ipt the transmit antenna. High ncy selective channels (with ߚ ൌ ͲǤͳ) are shown in Figure 28.

Next, we study the performance of DIDO precoding in frequency selective channels. We compute the DIDO precoding weights via BD, assuming the signal model in (1) that ies the condition in (2). We reformulate the DIDO receive signal model in (5), with the condition in (2), as ܚ௞ ൌ ۶ୣ௞ ܛ௞ ൅ܖ ௞. (23) where ۶ୣ௞ ൌ ۶௞܅௞ is the effective channel matrix for user k. For DIDO 2x2, with a single a per client, the effective channel matrix reduces to one value with a frequency se shown in Figure 29 and for channels characterized 6181P524XPCT [CORRECTED ICATION] by high frequency selectivity (e.g., with ߚ ൌ ͲǤͳ) in Figure 28. The continuous line in Figure 29 refers to client 1, whereas the line with dots refers to client 2. Based on the l quality metric in Figure 29 we define time/frequency domain link adaptation (LA) methods that dynamically adjust MCSs, depending on the changing channel conditions.

We begin by evaluating the performance of different MCSs in AWGN and gh fading SISO channels. For simplicity, we assume no FEC coding, but the following LA methods can be extended to systems that include FEC.

Figure 30 shows the SER for different QAM schemes (i.e., 4-QAM, 16- QAM, 64-QAM). Without loss of generality, we assume target SER of 1% for d systems. The SNR thresholds to meet that target SER in AWGN channels are 8dB, 15.5dB and 22dB for the three modulation schemes, respectively. In Rayleigh fading channels, it is well known the SER performance of the above modulation schemes is worse than AWGN [13] and the SNR thresholds are: 18.6dB, 27.3dB and 34.1dB, respectively. We observe that DIDO precoding transforms the multi-user downlink channel into a set of parallel SISO links. Hence, the same SNR thresholds as in Figure 30 for SISO s hold for DIDO systems on a -byclient basis. Moreover, if taneous LA is carried out, the olds in AWGN channels are used.

The key idea of the proposed LA method for DIDO systems is to use low MCS orders when the channel undergoes deep fades in the time domain or frequency domain (depicted in Figure 28) to provide link-robustness. Contrarily, when the channel is characterized by large gain, the LA method es to higher MCS orders to increase spectral efficiency. One contribution of the t application compared to U.S. Patent No. 7,636,381 is to use the effective channel matrix in (23) and in Figure 29 as a metric to enable adaptation.

The general framework of the LA methods is depicted in Figure 31 and defined as follows: x CSI estimation: At 3171 the DIDO BTS computes the CSI from all users.

Users may be equipped with single or multiple receive antennas. x DIDO precoding: At 3172, the BTS computes the DIDO precoding weights for all users. In one embodiment, BD is used to compute these weights. The precoding weights are calculated on a tone-by-tone basis. 6181P524XPCT CTED SPECIFICATION] x Link-quality metric calculation: At 3173 the BTS computes the frequency-domain link y metrics. In OFDM systems, the metrics are calculated from the CSI and DIDO precoding weights for every 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 effective channel in (23). We define this method as LA2 (based on tone-by-tone performance to exploit frequency diversity). If every client has single antenna, the frequency-domain effective channel is depicted in Figure 29. If the clients have le receive antennas, the link-quality metric is defined as the Frobenius norm of the effective channel matrix for every tone. Alternatively, multiple link-quality metrics are defined for every client as the singular values of the effective channel matrix in (23). x Bit-loading algorithm: At 3174, based on the link-quality metrics, the BTS determines the MCSs for different clients and different OFDM tones. For LA1 method, the same MCS is used for all clients and all OFDM tones based on the SNR olds for Rayleigh fading ls in Figure 30. For LA2, different MCSs are assigned to different OFDM tones to exploit channel ncy ity. x Precoded data transmission: At 3175, the BTS transmits precoded data streams from the DIDO distributed antennas to the clients using the MCSs derived from the bit-loading algorithm. One header is attached to the precoded data to communicate the MCSs for different tones to the clients. For example, if eight MCSs are ble and the OFDM symbols are defined with N=64 tone, log2(8)*N=192 bits are ed to communicate the current MCS to every client. Assuming 4-QAM (2 bits/symbol spectral efficiency) is used to map those bits into symbols, only 192/2/N=1.5 OFDM symbols are required to map the MCS information. In another embodiment, multiple subcarriers (or OFDM tones) are grouped into subbands and the same MCS is assigned to all tones in the same subband to reduce the overhead due to control ation. Moreover, the MCS are adjusted based on temporal variations of the channel gain (proportional to the coherence time). In fixed-wireless channel cterized by low Doppler ) the MCS are recalculated every fraction of the channel coherence time, thereby ng the ad required for control information.

Figure 32 shows the SER performance of the LA methods described 6181P524XPCT [CORRECTED SPECIFICATION] above. For comparison, the SER performance in Rayleigh fading channels is plotted for each of the three QAM schemes used. The LA2 method adapts the MCSs to the fluctuation of the effective channel in the frequency domain, thereby providing 1.8bps/Hz gain in spectral efficiency for low SNR (i.e., SNR=20dB) and 15dB gain in SNR (for SNR>35dB) compared to LA1.

System and Method for DIDO Precoding Interpolation in Multicarrier Systems The computational complexity of DIDO s is mostly localized at the centralized processor or BTS. The most computationally expensive operation is the calculation of the ing weights for all s from their CSI. When BD precoding is employed, the BTS has to carry out as many singular value decomposition (SVD) operations as the number of clients in the system. One way to reduce complexity is through parallelized processing, where the SVD is computed on a separate processor for every client.

In multicarrier DIDO systems, each rier undergoes flat-fading channel and the SVD is carried out for every client over every subcarrier. Clearly the complexity of the system increases linearly with the number of subcarriers. For example, in OFDM systems with 1MHz signal bandwidth, the cyclic prefix (L0) must have at least eight channel taps (i.e., duration of 8 microseconds) to avoid intersymbol interference in outdoor urban macrocell environments with large delay spread [3]. The size (NFFT ) of the fast Fourier transform (FFT) used to generate the OFDM s is typically set to multiple of L0 to reduce loss of data rate. If NFFT =64, the effective spectral efficiency of the system is d by a factor NFFT /( NFFT +L 0)=89%. Larger values of NFFT yield higher spectral efficiency at the expense of higher computational complexity at the DIDO precoder.

One way to reduce ational xity at the DIDO precoder is to carry out the SVD operation over a subset of tones (that we call pilot tones) and derive the precoding weights for the remaining tones via interpolation. Weight interpolation is one source of error that results in inter-client erence. In one embodiment, optimal weight interpolation techniques are employed to reduce interclient interference, ng ed error rate performance and lower computational xity in multicarrier systems. In DIDO s with M transmit antennas, U clients and N receive antennas per clients, the condition for the 6181P524XPCT [CORRECTED SPECIFICATION] precoding weights of the kth client (܅௞) that guarantees zero interference to the other clients u is derived from (2) as ۶௨܅௞ ൌ ૙ே୶ே Ǣ ׊ ݑ ൌ ͳǡǥǡܷǢ ™‹–Š ݑ ് ݇ (24) where ۶௨ are the channel matrices corresponding to the other DIDO s in the system.

In one embodiment of the invention, the objective function of the weight interpolation method is defined as ˆሺી ௞ሻ ൌ σ୙୳ୀଵ ฮ۶ ෡ ௨܅௞ሺી ௞ሻฮ ୊ (25) where ી௞ is the set of parameters to be optimized for user k, ܅෡௞ሺી ௞ሻ is the weight interpolation matrix and ԡȉԡ୊ denotes the Frobenius norm of a matrix. The optimization problem is formulated as ી௞ǡ୭୮୲ ൌ ƒ”‰‹ી ˆሺી ௞ሻ (26) where ȣ௞ is the le set of the optimization m and ી௞ǡ୭୮୲ is the optimal solution.

The objective function in (25) is defined for one OFDM tone. In another embodiment of the invention, the objective function is defined as linear combination of the Frobenius norm in (25) of the matrices for all the OFDM tones to be interpolated. In r embodiment, the OFDM spectrum is divided into subsets of tones and the optimal solution is given by ી௞ǡ୭୮୲ ൌ ƒ”‰‹ી ƒš ௡୅ ˆሺǡી ௞ሻ (27) where n is the OFDM tone index and A is the subset of tones.

The weight olation matrix ܅௞ሺી ௞ሻ in (25) is expressed as a function of a set of parameters ી௞. Once the optimal set is determined according to (26) or (27), the optimal weight matrix is computed. In one embodiment of the invention, the weight interpolation matrix of given OFDM tone n is defined as linear combination of the weight matrices of the pilot tones. One e of weight interpolation function for rming systems with single client was defined in [11].

In DIDO multi-client s we write the weight interpolation matrix as ܅෡௞ሺ݈ܰ ଴ ൅݊ǡߠ ௞ሻ ൌ ሺͳെܿ ௡ሻ ȉ܅ ሺ݈ሻ ൅ ܿ ௡‡௝ఏ ೖ ȉ܅ ሺ݈ ൅ͳሻ (28) where Ͳ ൑ ݈ ൑ ሺܮ଴ -1), L0 is the number of pilot tones and ܿ௡ ൌ ሺ݊ െͳሻȀܰ଴ , with ܰ଴ ൌ ܰிி் Ȁܮ ଴. The weight matrix in (28) is then normalized such that ฮ܅ ෡௞ฮ ൌ ξܰܯ 6181P524XPCT [CORRECTED SPECIFICATION] to guarantee unitary power ission from every antenna. If N=1 (single receive antenna per client), the matrix in (28) becomes a vector that is normalized with respect to its norm. In one embodiment of the invention, the pilot tones are chosen uniformly within the range of the OFDM tones. In another embodiment, the pilot tones are adaptively chosen based on the CSI to minimize the interpolation error.

We observe that one key difference of the system and method in [11] against the one proposed in this patent application is the ive function. In particular, the systems in [11] assumes multiple transmit antennas and single , so the d method is ed to maximize the product of the precoding weight by the channel to maximize the receive SNR for the client. This method, however, does not work in multi-client scenarios, since it yields inter-client interference due to interpolation error. By contrast, our method is designed to minimize inter-client interference thereby improving error rate performance to all clients.

Figure 33 shows the entries of the matrix in (28) as a function of the OFDM tone index for DIDO 2x2 systems with ܰிி் ൌ ͸Ͷ and ܮ଴ ൌ ͺ. The l PDP is generated according to the model in (21) with ߚ ൌ ͳ and the channel consists of only eight channel taps. We observe that L0 must be chosen to be larger than the number of channel taps. The solid lines in Figure 33 represent the ideal functions, whereas the dotted lines are the interpolated ones. The interpolated weights match the ideal ones for the pilot tones, according to the definition in (28). The weights computed over the remaining tones only imate the ideal case due to estimation error.

One way to implement the weight interpolation method is via exhaustive search over the feasible set ȣ௞ in (26). To reduce the complexity of the search, we quantize the feasible set into P values uniformly in the range [0,ʹߨ ].

Figure 34 shows the SER versus SNR for ܮ଴ ൌ ͺ, M=Nt=2 transmit antennas and variable number of P. As the number of quantization levels ses, the SER performance es. We observe the case P=10 approaches the performance of P=100 for much lower computational complexity, due to d number of searches.

Figure 35 shows the SER mance of the interpolation method for different DIDO orders and ܮ଴ ൌ ͳ͸. We assume the number of clients is the same as 6181P524XPCT [CORRECTED SPECIFICATION] the number of transmit antennas and every client is equipped with single antenna.

As the number of clients increases the SER performance degrades due to increase inter-client erence produced by weight interpolation errors.

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

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Sig. Proc., vol. 52, pp. 461–471, Feb. 2004. 6181P524XPCT [CORRECTED SPECIFICATION] II. DISCLOSURE FROM RELATED APPLICATION SERIAL NO. 12/917,257 Described below are wireless radio ncy (RF) communication systems and methods employing a plurality of distributed transmitting as operating cooperatively to create wireless links to given users, while suppressing erence to other users. Coordination across different transmitting antennas is enabled via user-clustering . The user cluster is a subset of itting antennas whose signal can be reliably detected by given user (i.e., received signal strength above noise or interference level). Every user in the system defines its own usercluter.

The waveforms sent by the transmitting antennas within the same user-cluster coherently combine to create RF energy at the target user’s location and points of zero RF interference at the location of any other user reachable by those antennas. er a system with M transmit antennas within one user-cluster and K users reachable by those M antennas, with ܭ ൑ . We assume the transmitters are aware of the CSI (۶ א ׋௄୶ெ) between the M transmit antennas and K users. For city, every user is assumed to be equipped with a single a, but the same method can be extended to le receive antennas per user.

Consider the channel matrix H obtained by combining the channel vectors (ܐ௞ א ׋ଵ୶ெ ) from the M transmit antennas to the K users as ڭ ۍ ې ۶ ൌ ܐێ௞ ڭ ێ ۑ ܐۏ௄ے The precoding weights (ܟ௞ א ׋ெ୶ଵ ) that create RF energy to user k and zero RF energy to all other K-1 users are computed to satisfy the following condition ۶෩௞ܟ௞ ൌ ૙௄୶ଵ where ۶෩௞ is the effective channel matrix of user k obtained by removing the k-th row of matrix H and Ͳ௄୶ଵ is the vector with all zero entries In one ment, the wireless system is a DIDO system and user ring is employed to create a wireless communication link to the target user, while pre-cancelling interference to any other user reachable by the antennas lying within the user-cluster. In U.S. Application Serial No. 12/630,627, a DIDO system is described which includes: 6181P524XPCT [CORRECTED SPECIFICATION] x DIDO clients: user terminals ed with one or multiple antennas; x DIDO distributed as: transceiver stations operating atively to transmit precoded data streams to multiple users, y suppressing inter-user interference; x DIDO base transceiver stations (BTS): centralized processor generating precoded rms to the DIDO distributed as; x DIDO base station network (BSN): wired backhaul connecting the BTS to the DIDO buted antennas or to other BTSs.

The DIDO distributed antennas are grouped into different subsets depending on their spatial distribution relative to the location of the BTSs or DIDO clients. We define three types of clusters, as depicted in Figure 36: x Super-cluster 3640: is the set of DIDO distributed antennas connected to one or multiple BTSs such that the round-trip latency between all BTSs and the respective users is within the constraint of the DIDO ing loop; x DIDO-cluster 3641: is the set of DIDO distributed antennas connected to the same BTS. When the super-cluster contains only one BTS, its definition coincides with the DIDO-cluster; x User-cluster 3642: is the set of DIDO distributed antennas that cooperatively it precoded data to given user.

For example, the BTSs are local hubs connected to other BTSs and to the DIDO distributed antennas via the BSN. The BSN can be sed of various network technologies including, but not limited to, digital subscriber lines (DSL), ADSL, VDSL [6], cable modems, fiber rings, T1 lines, hybrid fiber coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi). All BTSs within the same super-cluster share information about DIDO ing via the BSN such that the round-trip latency is within the DIDO precoding loop.

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

We proposed similar method in [5], where points of zero RF energy were created to remove erence in the overlapping regions between DIDO 6181P524XPCT CTED SPECIFICATION] clusters. Extra antennas were required to transmit signal to the s within the DIDO cluster while suppressing inter-cluster interference. One embodiment of a method proposed in the present application does not attempt to remove inter-DIDO- cluster interference; rather it assumes the cluster is bound to the client (i.e., usercluster ) and guarantees that no interference (or negligible interference) is generated to any other client in that 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 pathloss. Users close or within the luster receive interference-free signal due to precoding. Moreover, additional transmit antennas can be added to the user-cluster (as shown in Figure 37) such that the condition ܭ ൑ ܯ is ied.

One embodiment of a method employing user clustering consists of the following steps: a. uality measurements: the link quality between every DIDO distributed antenna and every user is reported to the BTS. The link-quality metric consists of signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR).

In one ment, the DIDO distributed antennas transmit training signals and the users estimate the received signal quality based on that ng. The training signals are designed to be orthogonal in time, ncy or code domains such that the users can distinguish across different transmitters. Alternatively, the DIDO antennas transmit narrowband signals (i.e., single tone) at one particular frequency (i.e., a beacon channel) and the users estimate the link-quality based on that beacon .

One threshold is defined as the minimum signal ude (or power) above the noise level to demodulate data sfully as shown in Figure 38a. Any link-quality metric value below this threshold is assumed to be zero. The link-quality metric is quantized over a finite number of bits and fed back to the transmitter.

In a different embodiment, the training signals or beacons are sent from the users and the link quality is estimated at the DIDO transmit antennas (as in Figure 38b), assuming reciprocity between uplink (UL) and downlink (DL) pathloss. Note that pathloss ocity is a realistic assumption in time division duplexing (TDD) 6181P524XPCT [CORRECTED SPECIFICATION] systems (with UL and DL channels at the same frequency) and frequency division duplexing (FDD) systems when the UL and DL frequency bands are reatively close.

Information about the link-quality metrics is shared across different BTSs through the BSN as depicted in Figure 37 such that all BTSs are aware of the link-quality between every antenna/user couple across different DIDO clusters. b. Definition of user-clusters: the link-quality metrics of all wireless links in the DIDO clusters are the entries to the link-quality matrix shared across all BTSs via the BSN. One example of link-quality matrix for the scenario in Figure 37 is depicted in Figure 39.

The link-quality matrix is used to define the user clusters. For example, Figure 39 shows the selection of the user cluster for user U8. The subset of transmitters with ro link-quality metrics (i.e., active transmitters) to user U8 is first identified.

These transmitters populate the user-cluster for the user U8. Then the sub-matrix containing non-zero entries from the transmitters within the luster to the other users is selected. Note that since the link-quality metrics are only used to select the user cluster, they can be quantized with only two bits (i.e., to identify the state above or below the thresholds in Figure 38) thereby reducing feedback overhead.

Another example is depicted in Figure 40 for user U1. In this case the number of active transmitters is lower than the number of users in the trix, thereby violating the condition ܭ ൑ . Therefore, one or more columns are added to the sub-matrix to satisfy that condition. If the number of transmitters s the number of users, the extra antennas can be used for diversity s (i.e., antenna or eigenmode selection).

Yet another example is shown in Figure 41 for user U4. We observe that the trix can be obtained as combination of two sub-matrices. c. CSI report to the BTSs: Once the user clusters are selected, the CSI from all transmitters within the user-cluster to every user reached by those transmitters is made available to all BTSs. The CSI information is shared across all BTSs via the BSN. In TDD systems, UL/DL l ocity can be exploited to derive the CSI from training over the UL channel. In FDD systems, feedback channels from all users to the BTSs are required. To reduce the amount of ck, only the CSI corresponding to the ro entries of the link-quality matrix are fed back. 6181P524XPCT [CORRECTED SPECIFICATION] d. DIDO precoding: Finally, DIDO precoding is applied to every CSI s ubmatrix corresponding to different user clusters (as described, for example, in the related U.S. Patent Applications).

In one embodiment, singular value decomposition (SVD) of the effective channel matrix ۶෩ܓ is computed and the precoding weight ܟ௞ for user k is defined as the right sigular vector corresponding to the null subspace of ۶෩௞. Alternatively, if M>K and the SVD decomposes the effective channel matrix as ۶෩௞ ൌ ு , the DIDO precoding weight for user k is given by ܟ௞ ൌ ܃୭ ሺ܃ ு ȉ ܐ ்ሻ ୭ ௞ where ܃୭ is the matrix with columns being the singular vectors of the null subspace of ۶෩௞.

From basic linear algebra considerations, we observe that the right singular vector in the null subspace of the matrix ۶෩ is equal to the eigenvetor of C corresponding to the zero alue ۱ ൌ ۶෩ு ۶෩ ൌ ሺ܄઱܃ ுሻு ሺ܄઱܃ ுሻ ൌ ܃ ઱ଶ ܃ ு where the effective channel matrix is decomposed as ۶෩ ൌ ܄઱܃ு, according to the SVD. Then, one ative to computing the SVD of ۶෩ܓ is to calculate the eigenvalue osition of C. There are several methods to compute eigenvalue decomposition such as the power method. Since we are only interested to the eigenvector corresponding to the null subspace of C, we use the inverse power method described by the iteration ሺ۱ െ ߣ۷ሻି૚ ܝ ܝ௜ାଵ ൌ ௜ ԡሺ۱ െ ߣ۷ሻି૚ ܝ ௜ԡ where the vector ሺܝ ௜) at the first iteration is a random vector.

Given that the alue (ߣ) of the null subspace is known (i.e., zero) the inverse power method es only one iteration to converge, thereby ng computational complexity. Then, we write the precoding weight vector as ܟ ൌ ۱ି૚ ܝ ଵ where ܝ ଵ is the vector with real entries equal to 1 (i.e., the precoding weight vector is the sum of the columns of ۱ି૚ ).

The DIDO precoding ation requires one matrix inversion. There are several numerical ons to reduce the complexity of matrix inversions such as the Strassen’s algorithm [1] or the Coppersmith-Winograd’s algorithm [2,3]. Since C is 24XPCT [CORRECTED SPECIFICATION] Hermitian matrix by definition, an alternative solution is to decompose C in its real and imaginary components and compute matrix inversion of a real matrix, according to the method in [4, Section 11.4].

Another e of the proposed method and system is its reconfigurability. As the client moves across different DIDO clusters as in Figure 42, the user-cluster follows its moves. In other words, the subset of transmit antennas is constantly updated as the client changes its position and the effective channel matrix (and corresponding precoding weights) are recomputed.

The method proposed herein works within the super-cluster in Figure 36 , since the links between the BTSs via the BSN must be low-latency. To suppress interference in the overlapping regions of different super-clusters, it is possible to use our method in [5] that uses extra antennas to create points of zero RF energy in the ering regions between DIDO clusters.

] It should be noted that the terms “user” and “client” are used interchangeably herein.

References [1] S. Robinson, “Toward an Optimal Algorithm for Matrix Multiplication”, SIAM News, Volume 38, Number 9, November 2005. [2] D. Coppersmith and S. ad, “Matrix Multiplication via Arithmetic Progression”, J. Symb. Comp. vol.9, p.251-280, 1990. ] [3] H. Cohn, R. Kleinberg, B. y, C. Umans, “Group-theoretic Algorithms for Matrix Multiplication”, p. 379-388, Nov. 2005. [4] W.H. Press, S.A. Teukolsky, W. T. Vetterling, B.P. Flannery “NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING”, Cambridge University Press, 1992. [5] A. Forenza and S.G.Perlman, “INTERFERENCE MANAGEMENT , HANDOFF , POWER CONTROL AND LINK ADAPTATION IN DISTRIBUTED -I NPUT DISTRIBUTED - OUTPUT (DIDO) COMMUNICATION SYSTEMS ”, Patent Application Serial No. ,988, filed June 16, 2010. [6] ik Eriksson and Björn mmar, “VDSL2: Next important broadband technology”, Ericsson Review No. 1, 2006. 6181P524XPCT [CORRECTED SPECIFICATION] III. SYSTEMS AND METHODS TO EXPLOIT AREAS OF COHERENCE IN WIRELESS SYSTEMS The capacity of multiple antenna systems (MAS) in practical propagation environments is a function of the spatial diversity available over the wireless link. Spatial diversity is determined by the distribution of scattering objects in the wireless channel as well as the geometry of transmit and receive antenna arrays.

One popular model for MAS channels is the so called clustered l model, that defines groups of rers as clusters located around the transmitters and receivers. In general, the more clusters and the larger their angular spread, the higher l ity and capacity achievable over wireless links.

Clustered l models have been ted through practical measurements [1- 2] and variations of those models have been adopted by different indoor (i.e., IEEE 802.11n Technical Group [3] for WLAN) and r (3GPP Technical Specification Group for 3G cellular systems [4]) ss rds.

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

A unified model describing the effects of antenna array design as well as the characteristics of the propagation channel on the spatial diversity (or degrees of freedom) of wireless links was ted in [29]. The received signal model in [29] is given by ܡሺܙሻ ൌ න ۱ሺܙǡܘ ሻܠሺܘሻ݀ܘ ൅ ܢሺܙሻ where ܠሺܘሻ א ଷ is the polarized vector describing the transmit signal, ܘǡא ܙ ଷ are the polarized vector positions describing the transmit and receive arrays, respectively, and ۱ሺȉǡȉ ሻ א ଷ୶ଷ is the matrix bing the system response between transmit and receive vector positions given by ۱ሺܙǡܘ ሻ ൌ ඵܚۯሺܙǡܕ ෝሻ۶ሺܕෝǡܖෝሻܜۯሺܖෝǡܘ ሻ݀ܖෝ݀ܕ ෝ where ܜۯሺȉǡȉ ሻǡۯ ଷ୶ଷ ܚሺȉǡȉ ሻ א are the transmit and receive array responses respectively and ෝሻ א ଷ୶ଷ is the channel response matrix with entries being the complex 6181P524XPCT [CORRECTED SPECIFICATION] gains between it ion ܖෝ and receive ion ܕෝ. In DIDO systems, user devices may have single or multiple antennas. For the sake of simplicity, we assume single antenna receivers with ideal pic patterns and e the system response matrix as ۱ሺܙǡܘ ሻ ൌ න۶ሺܙǡܖෝሻۯሺܖෝǡܘ ሻ݀ܖෝ where only the transmit antenna pattern ۯሺܖෝǡܘ ሻ is considered.

From the Maxwell equations and the far-field term of the Green on, the array response can be approximated as [29] ŒɄ‡ ୨ଶ஠ୢ ౥ ۯሺܖෝǡܘ ሻ ൌ ሺ۷ െ ܖෝܖෝுሻ ƒ ሺܖෝǡܘ ሻ ʹɉ ଶ† ୭ with ܘԖ , P is the space that defines the antenna array and where ƒሺܖෝǡܘ ሻ ൌ ‡š’ሺെŒʹɎ ܖෝுܘሻ with ሺܖෝǡܘ ሻԖ π ൈ Ǥ For unpolarized antennas, studying the array response is equivalent to study the integral kernel above. Hereafter, we show closed for expressions of the integral kernels for different types of arrays.

Unpolarized Linear Arrays For unpolarized linear arrays of length L (normalized by the wavelength) and antenna elements oriented along the z-axis and centered at the origin, the integral kernel is given by [29] ƒሺ…‘•ߠǡ݌ ୸ሻ ൌ ‡š’ሺെŒʹɎ ݌ ୸ …‘•ߠ ሻ Ǥ Expanding the above equation into a series of shifted dyads, we obtain that the sinc function have tion of 1/L and the dimension of the limited and approximately wavevector-limited subspace (i.e., degrees of freedom) is ୊ ൌ ȁπ஘ȁ where π஘ ൌ ሼ…‘•ߠ ǣߠ߳ȣሽ. We observe that for broadside arrays ȁπ஘ȁ ൌ ȁȣȁ whereas for endfire ȁπ஘ȁ ൎ ȁȣȁଶȀʹǤ Unpolarized Spherical Arrays The integral kernel for a cal array of radius R (normalized by the wavelength) is given by [29] ƒሺܖෝǡܘ ሻ ൌ ‡š’ሼെŒʹɎ ሾ•‹ߠ •‹ߠ ᇱ …‘• ሺ߶ െ ߶ᇱሻ ൅ …‘•ߠ …‘•ߠᇱ ሿ ሽǤ Decomposing the above function with sum of spherical Bessel functions of the first kind we obtain the resolution of spherical arrays is 1/( Ɏ ଶ) and 24XPCT CTED SPECIFICATION] the degrees of freedom are given by ୊ ൌ ܣȁπȁ ൌ Ɏ ଶȁπȁ where A is the area of the spherical array and ȁπȁ ؿ ሾͲǡɎ ሻ ൈ ሾͲǡʹɎ ሻ.

Areas of Coherence in Wireless Channels The relation between the resolution of spherical arrays and their area A is depicted in Figure 43. The sphere in the middle is the spherical array of area A.

The projection of the channel clusters on the unit sphere s ent scattering regions of size proportional to the angular spread of the rs. The area of size 1/ A within each cluster, which we call “area of coherence”, denotes the tion of the basis functions of the radiated field of the array and s the resolution of the array in the wavevector domain.

Comparing Figure 43 with Figure 44, we observe that the size of the area of coherence decreases as the inverse of the size of the array. In fact, larger arrays can focus energy into smaller areas, yielding larger number of degrees of freedom DF. Note that to total number of degrees of freedom depends also on the angular spread of the cluster, as shown in the definition above.

Figure 45 s another example where the array size covers even larger area than Figure 44, yielding additional degrees of freedom. In DIDO systems, the array re can be approximated by the total area covered by all DIDO transmitters (assuming antennas are spaced fractions of wavelength apart).

Then Figure 45 shows that DIDO systems can achieve increasing numbers of degrees of freedom by distributing antennas in space, thereby reducing the size of the areas of coherence. Note that these figures are generated assuming ideal spherical arrays. In practical scenarios, DIDO antennas spread random across wide areas and the resulting shape of the areas of coherence may not be as regular as in the figures.

Figure 46 shows that, as the array size increases, more clusters are included within the wireless channel as radio waves are scatterered by increasing number of objects between DIDO transmitters. Hence, it is possible to excite an increasing number of basis functions (that span the radiated field), yielding additional degrees of freedom, in agreement with the definition above.

The multi-user (MU) multiple antenna systems (MAS) described in this patent application exploit the area of nce of wireless channels to create 24XPCT [CORRECTED ICATION] multiple simultaneous independent non-interfering data streams to different users.

For given channel conditions and user distribution, the basis functions of the radiated field are ed to create independent and simultaneous wireless links to different users in such a way that every user ences interference-free links. As the MUMAS is aware of the channel between every transmitter and every user, the ing transmission is adjusted based on that ation to create separate areas of coherence to 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 invention, the MU-MAS employs non-linear precoding, such as block diagonalization (BD) as in our previous patent ations 0009] or zero-forcing beamforming (ZF-BF) [34].

To enable precoding, the MU-MAS requires knowledge of the channel state ation (CSI). The CSI is made available to the MU-MAS via a feedback channel or estimated over 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 limited feedback techniques [35-37].

In one embodiment, the MU-MAS uses d feedback techniques to reduce the CSI overhead of the control l. Codebook design is critical in limited feedback ques. One embodiment defines the codebook from the basis functions that span the radiated field of the transmit array.

As the users move in space or the propagation environment changes over time due to mobile objects (such as people or cars), the areas of coherence change their locations and shape. This is due to the well known Doppler effect in wireless communications. The MU-MAS described in this patent application adjusts the precoding to adapt the areas of coherence constantly for every user as the environment changes due to Doppler effects. This adaptation of the areas of coherence is such to create simultaneous non-interfering channels to different users.

Another embodiment of the invention adaptively selects a subset of as of the MU-MAS system to create areas of coherence of different sizes. For example, if the users are sparsely distributed in space (i.e., rural area or times of the day with low usage of wireless resources), only a small subset of antennas is selected and the size of the area of coherence are large relative to the array size as in Figure 43. Alternatively, in densely populated areas (i.e., urban areas or time of 6181P524XPCT [CORRECTED SPECIFICATION] the day with peak usage of ss services) more antennas are selected to create small areas of coherence for users in direct vicinity of each other.

In one embodiment of the invention, the MU-MAS is a DIDO system as described in previous patent ations [0003-0009]. The DIDO system uses linear or non-linear precoding and/or limited ck techniques to create area of coherence to different users.

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

Figure 47 shows the degrees of freedom of MIMO systems in practical indoor and outdoor propagation scenarios. For example, ering linear arrays with ten antennas spaced one wavelength apart, the maximum degrees of freedom (or number of spatial channels) available over the wireless link is d to about 3 for outdoor scenarios and 7 for . Of course, indoor channels provide more degrees of freedom due to the larger angular spread.

Next we compute the s of freedom in DIDO systems. We consider the case where the antennas distributed over 3D space, such as downtown urban scenarios where DIDO access points may be distributed on different floors of adjacent building. As such, we model the DIDO transmit antennas (all connected to each other via fiber or DSL backbone) as a spherical array. Also, we assume the clusters are uniformly distributed across the solid angle.

] Figure 48 shows the degrees of freedom in DIDO systems as a function of the array diameter. We observe that for a diameter equal to ten wavelengths, about 1000 degrees of freedom are ble in the DIDO system. In theory, it is possible to create up to 1000 non-interfering channels to the users. The increased spatial diversity due to distributed antennas in space is the key to the multiplexing gain provided by DIDO over conventional MIMO systems.

As a comparison, we show the degrees of freedom achievable in 6181P524XPCT [CORRECTED SPECIFICATION] suburban environments with DIDO systems. We assume the clusters are distributed within the elevation angles [ȽǡɎ െ Ƚ], and define the solid angle for the rs as ȁπȁ ൌ ͶɎ…‘•Ƚ. For example, in suburban scenarios with two-story buildings, the elevation angle of the scatterers can be Ƚ ൌ ͸Ͳ୭. In that case, the number of degrees of freedom as a function of the wavelength is shown in Figure 48.

IV. SYSTEM AND METHODS FOR PLANNED EVOLUTION AND OBSOLESCENCE OF MULTIUSER SPECTRUM The growing demand for peed wireless services and the increasing number of cellular telephone subscribers has produced a radical technology tion in the wireless industry over the past three decades from initial analog voice services (AMPS [1-2]) to standards that support digital voice (GSM [3- 4], IS-95 CDMA [5]), data traffic (EDGE [6], EV-DO [7]) and Internet browsing (WiFi [8-9], WiMAX ], 3G [12-13], 4G [14-15]). This wireless technology growth throughout the years has been enabled by two major efforts: i) The federal ications commission (FCC) [16] has been allocating new spectrum to support new emerging standards. For example, in the first generation AMPS systems the number of channels allocated by the FCC grew from the initial 333 in 1983 to 416 in the late 1980s to support the increasing number of cellular clients. More recently, the commercialization of technologies like Wi-Fi, Bluetooth and ZigBee has been possible with the use of the unlicensed ISM band allocated by the FCC back in 1985 [17]. ii) The wireless industry has been producing new technologies that utilize the limited ble spectrum more efficiently to support higher data rate links and increased numbers of subscribers. One big revolution in the wireless world was the migration from the analog AMPS s to digital D-AMPS and GSM in the 1990s, that d much higher call volume for a given frequency band due to improved spectral efficiency. Another radical shift was produced in the early 2000s by spatial processing techniques such as le-input multiple-output (MIMO), yielding 4x improvement in data rate over previous ss ks and adopted by different standards (i.e., IEEE n for Wi-Fi, IEEE 802.16 for WiMAX, 3GPP for 4G-LTE).

Despite efforts to provide solutions for peed wireless connectivity, the wireless industry is facing new challenges: to offer high-definition 6181P524XPCT [CORRECTED SPECIFICATION] (HD) video streaming to satisfy the growing demand for services like gaming and to provide wireless ge everywhere (including rural areas, where building the wireline backbone is costly and impractical). Currently, the most advanced wireless standard systems (i.e., 4G-LTE) cannot provide data rate requirements and y constraints to support HD streaming services, particularly when the network is overloaded with a high volume of concurrent links. Once again, the main drawbacks have been the limited spectrum availability and lack of spectrally efficient logies that can truly enhance data rate and provide complete coverage.

A new technology has emerged in recent years called distributed-input distributed-output (DIDO) [18-21] and described in our previous patent ations [0002-0009]. DIDO technology promises orders of magnitude increase in spectral efficiency, making HD wireless streaming services possible in overloaded networks.

At the same time, the US government has been sing the issue of spectrum scarcity by launching a plan that will free 500MHz of spectrum over the next 10 years. This plan was released on June 28th , 2010 with the goal of allowing new emerging wireless technologies to operate in the new frequency bands and providing high-speed wireless coverage in urban and rural areas [22]. As part of this plan, on September 23rd , 2010 the FCC opened up about 200MHz of the VHF and UHF spectrum for unlicensed use called “white spaces” [23]. One restriction to operate in those ncy bands is that harmful interference must not be created with existing ss microphone devices operating in the same band. As such, on July 22nd , 2011 the IEEE 802.22 working group finalized the standard for a new wireless system employing cognitive radio technology (or spectrum sensing) with the key feature of cally monitoring the spectrum and operating in the available bands, thereby ng harmful interference with coexisting wireless devices [24].

Only ly has there been debates to allocate part of the white spaces to licensed use and open it up to spectrum auction [25].

The coexistence of unlicensed devices within the same frequency bands and spectrum contention for unlicensed versus licensed use have been two major issues for FCC spectrum allocation plans throughout the years. For example, in white spaces, coexistence between ss microphones and wireless communications s has been d via cognitive radio technology. Cognitive radio, however, can provide only a on of the spectral efficiency of other logies using spatial processing like DIDO. Similarly, the performance of Wi-Fi 6181P524XPCT [CORRECTED SPECIFICATION] systems have been degrading significantly over the past decade due to sing number of access points and the use of Bluetooth/ZigBee devices that operate in the same unlicensed ISM band and te uncontrolled interference. One shortcoming of the unlicensed um is unregulated use of RF devices that will continue to e the spectrum for years to come. RF pollution also prevents the unlicensed spectrum from being used for future licensed operations, thereby limiting important market opportunities for wireless broadband commercial services and um auctions.

We propose a new system and methods that allow dynamic allocation of the wireless spectrum to enable coexistence and evolution of different es and standards. One embodiment of our method dynamically assigns entitlements to RF transceivers to operate in certain parts of the spectrum and enables obsolescence of the same RF devices to provide: i) Spectrum reconfigurability to enable new types of wireless operations (i.e., licensed vs. unlicensed) and/or meet new RF power emission . This feature allows spectrum auctions whenever is necessary, without need to plan in advance for use of licensed versus unlicensed spectrum. It also allows it power levels to be adjusted to meet new power emission levels enforced by the FCC. ii) Coexistence of different technologies operating in the same band (i.e., white spaces and wireless microphones, WiFi and Bluetooth/ZigBee) such that the band can be dynamically reallocated as new technologies are created, while ng interference with existing technologies. iii) Seamless evolution of wireless infrastructure as systems migrate to more advanced technologies that can offer higher spectral efficiency, better coverage and improved performance to support new types of services demanding higher QoS (i.e., HD video streaming).

Hereafter, we describe a system and method for d evolution and obsolescence of a ser spectrum. One embodiment of the system ts of one or multiple centralized processors (CP) 4901-4904 and one or multiple distributed nodes (DN) 4911-4913 that communicate via wireline or wireless connections as ed in Figure 49. For example, in the t of 4G-LTE networks [26], the centralized processor is the access core gateway (ACGW) connected to several Node B transceivers. In the context of Wi-Fi, the centralized processor is the internet service provider (ISP) and the distributed nodes are Wi-Fi 6181P524XPCT [CORRECTED SPECIFICATION] access points connected to the ISP via modems or direct connection to cable or DSL. In another embodiment of the invention, the system is a distributed-input distributed-output (DIDO) system [0002-0009] with one lized sor (or BTS) and distributed nodes being the DIDO access points (or DIDO distributed antennas connected to the BTS via the BSN).

The DNs 913 communicate with the CPs 4901-4904. The information exchanged from the DNs to the CP is used to dynamically adjust the configuration of the nodes to the evolving design of the network architecture. In one embodiment, the DNs 4911-4913 share their identification number with the CP. The CP store the identification numbers of all DNs connected through the network into lookup tables or shared database. Those lookup tables or database can be shared with other CPs and that ation is synchronized such that all CPs have always access to the most up to date ation about all DNs on the network.

For e, the FCC may decide to te a certain portion of the spectrum to unlicensed use and the proposed system may be designed to operate within that spectrum. Due to scarcity of spectrum, the FCC may subsequently need to te part of that spectrum to licensed use for commercial carriers (i.e., AT&T, Verizon, or Sprint), defense, or public safety. In conventional wireless systems, this tence would not be possible, since existing wireless devices operating in the unlicensed band would create harmful interference to the licensed RF transceivers.

In our proposed system, the distributed nodes exchange control information with the CPs 4901-4903 to adapt their RF transmission to the evolving band plan. In one embodiment, the DNs 913 were originally designed to operate over different frequency bands within the available spectrum. As the FCC allocates one or multiple portions of that spectrum to licensed ion, the CPs exchange control information with the unlicensed DNs and reconfigure them to shut down the frequency bands for licensed use, such that the unlicensed DNs do not interfere with the licensed DNs. This scenario is depicted in Figure 50 where the unlicensed nodes (e.g., 5002) are ted with solid circles and the licensed nodes with empty circles (e.g., 5001). In another embodiment, the whole um can be allocated to the new licensed service and the control information is used by the CPs to shut down all unlicensed DNs to avoid interference with the licensed DNs. This scenario is shown in Figure 51 where the obsolete unlicensed nodes are covered with a cross.

By way of another example, it may be necessary to restrict power 6181P524XPCT [CORRECTED SPECIFICATION] emissions for certain devices operating at given frequency band to meet the FCC exposure limits [27]. For instance, the wireless system may originally be designed for fixed wireless links with the DNs 4911-4913 ted to outdoor rooftop transceiver antennas. Subsequently, the same system may be updated to support DNs with indoor le antennas to offer better indoor coverage. The FCC re limits of portable devices are more restrictive than rooftop transmitters, due to possibly closer ity to the human body. In this case, the old DNs designed for outdoor applications can be re-used for indoor applications as long as the it power setting is adjusted. In one embodiment of the invention the DNs are designed with predefined sets of transmit power levels and the CPs 4901-4903 send control information to the DNs 4911-4913 to select new power levels as the system is upgraded, thereby meeting the FCC re limits. In another embodiment, the DNs are ctured with only one power emission setting and those DNs exceeding the new power emission levels are shut down remotely by the CP.

In one embodiment, the CPs 4901-4903 monitor periodically all DNs 4911-4913 in the network to define their ement to operate as RF eivers according to a certain standard. Those DNs that are not up to date can be marked as obsolete and removed from the network. For example, the DNs that operate within the current power limit and frequency band are kept active in the network, and all the others are shut down. Note that the DN parameters controlled by the CP are not limited to power emission and frequency band; it can be any parameter that defines the wireless link between the DN and the client devices.

In another embodiment of the invention, the DNs 4911-4913 can be reconfigured to enable the coexistence of different standard systems within the same spectrum. For example, the power emission, frequency band or other configuration ters of n DNs operating in the t of WLAN can be ed to accommodate the adoption of new DNs designed for WPAN applications, while avoiding harmful interference.

As new wireless standards are developed to enhance data rate and coverage in the wireless network, the DNs 4911-4913 can be updated to support those standards. In one embodiment, the DNs are software defined radios (SDR) equipped with programmable computational lity such as such as FPGA, DSP, CPU, GPU and/or GPGPU that run algorithms for baseband signal processing. If the standard is upgraded, new baseband algorithms can be remotely uploaded from the 24XPCT [CORRECTED SPECIFICATION] CP to the DNs to reflect the new standard. For example, in one embodiment the first standard is CDMA-based and subsequently it is replaced by OFDM technology to t different types of systems. rly, the sample rate, power and other parameters can be updated remotely to the DNs. This SDR feature of the DNs allows for continuous upgrades of 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 multiple CPs, buted nodes and a network interconnecting the CPs to the DNs. Figure 52 shows one e of cloud wireless system where the nodes identified with solid circles (e.g., 5203) communicate to CP 5206, the nodes identified with empty circles communicate to CP 5205 and the CPs 5205-5206 communicate between each other all through the network 5201. In one embodiment of the invention, the cloud wireless system is a DIDO system and the DNs are connected to the CP and exchange information to reconfigure periodically or instantly system parameters, and dynamically adjust to the changing conditions of the ss architecture. In the DIDO , the CP is the DIDO BTS, the distributed nodes are the DIDO distributed antennas, the network is the BSN and multiple BTSs are interconnected with each other via the DIDO centralized processor as described in our previous patent applications [0002-0009].

All DNs 5202-5203 within the cloud wireless system can be grouped in different sets. These sets of DNs can simultaneously create non-interfering wireless links to the multitude of client devices, while each set supporting a ent 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, turbo . Similarly, every client may be served with different multiple access techniques and/or different modulation/coding schemes. Based on the active s in the system and the standard they adopt for their wireless links, the CPs 5205-5206 dynamically select the subset of DNs that can support those standards and that are within range of the client devices. nces [1] Wikipedia, “Advanced Mobile Phone System” http://en.wikipedia.org/wiki/Advanced_Mobile_Phone_System [2] AT&T, “1946: First Mobile Telephone Call” http://www.corp.att.com/attlabs/reputation/timeline/46mobile.html 6181P524XPCT [CORRECTED SPECIFICATION] [3] GSMA, “GSM technology” http://www.gsmworld.com/technology/index.htm [4] ETSI, “Mobile technologies GSM” http://www.etsi.org/WebSite/Technologies/gsm.aspx [5] Wikipedia, “IS-95” http://en.wikipedia.org/wiki/IS-95 [6] Ericsson, “The ion of EDGE” http://www.ericsson.com/res/docs/whitepapers/evolution_to_edge.pdf [7] Q. Bi (2004-03). "A Forward Link Performance Study of the 1xEVDO Rel. 0 System Using Field Measurements and Simulations" (PDF). Lucent Technologies. http://www.cdg.org/resources/white_papers/files/Lucent%201xEVDO %20Rev%20O%20Mar%2004.pdf [8] Wi-Fi alliance, /www.wi-fi.org/ [9] Wi-Fi alliance, “Wi-Fi certified makes it Wi-Fi” http://www.wi-fi.org/files/WFA_Certification_Overview_WP_en.pdf

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

[11] C. Eklund, R. B. Marks, K. L. Stanwood and S. Wang, “IEEE rd 802.16: A Technical Overview of the WirelessMAN™Air Interface for and ss Access” http://ieee802.org/16/docs/02/C80216-02_05.pdf

[12] 3GPP, “UMTS”, /www.3gpp.org/article/umts

[13] H. Ekström, A. Furuskär, J. Karlsson, M. Meyer, S. Parkvall, J.

Torsner, and M. Wahlqvist “Technical Solutions for the 3G Long-Term Evolution”, IEEE Communications Magazine, 45, Mar. 2006

[14] 3GPP, “LTE”, http://www.3gpp.org/LTE ] [15] Motorola, “Long Term Evolution (LTE): A Technical Overview”, http://business.motorola.com/experiencelte/pdf/LTETechnicalOverview.pdf

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

[17] ITU, “ISM band” http://www.itu.int/ITUR /terrestrial/faq/index.html#g013

[18] S. Perlman and A. Forenza ibuted-input distributed-output 6181P524XPCT [CORRECTED SPECIFICATION] (DIDO) ss technology: a new approach to multiuser wireless”, Aug. 2011 http://www.rearden.com/DIDO/DIDO_White_Paper_110727.pdf

[19] Bloomberg Businessweek, “Steve Perlman’s Wireless Fix”, July 27, 2011 http://www.businessweek.com/magazine/the-edison-of-silicon-valley-07272011.html

[20] Wired, “Has OnLive’s Steve Perlman Discovered Holy Grail of Wireless?”, June 30, 2011 http://www.wired.com/epicenter/2011/06/perlman-holy-grail-wireless/

[21] The Wall Street Journal “Silicon Valley Inventor’s Radical e of Wireless”, July 28, 2011 http://blogs.wsj.com/digits/2011/07/28/silicon-valley-inventors-radical-rewrite-ofwireless

[22] The White House, dential Memorandum: Unleashing the Wireless Broadband Revolution”, June 28, 2010 http://www.whitehouse.gov/the-press-office/presidential-memorandum-unleashingwireless-broadband-revolution

[23] FCC, “Open commission g”, Sept. 23rd , 2010 http://reboot.fcc.gov/open-meetings/2010/september

[24] IEEE 802.22, “IEEE 802.22 Working Group on Wireless Regional Area ks”, http://www.ieee802.org/22/ ] [25] “A bill”,112th congress, 1st session, July 12, 2011 http://republicans.energycommerce.house.gov/Media/file/Hearings/Telecom/071511/ DiscussionDraft.pdf

[26] H. Ekström, A. Furuskär, J. Karlsson, M. Meyer, S. Parkvall, J.

Torsner, and M. Wahlqvist “Technical Solutions for the 3G Long-Term Evolution”, IEEE ications Magazine, pp.38-45, Mar. 2006

[27] FCC, “Evaluating compliance with FCC guidelines for human exposure to radiofrequency electromagnetic fields,” OET in 65, Edition 97-01, Aug. 1997 Embodiments of the invention may include various steps as set forth above. The steps may be embodied in machine-executable instructions which cause a general-purpose or special-purpose processor to perform certain steps. For example, the various components within the Base Stations/APs and Client Devices described above may be implemented as re executed on a general purpose or 24XPCT [CORRECTED SPECIFICATION] special purpose processor. To avoid obscuring the ent aspects of the invention, various well known personal computer components such as computer memory, hard drive, input devices, etc., have been left out of the figures.

Alternatively, in one embodiment, the various functional modules illustrated herein and the associated steps 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 , Modulation and Signal Processing Logic 903 described above may be implemented on a mmable digital signal processor (“DSP”) (or group of DSPs) such as a DSP using a Texas ments’ TMS320x architecture (e.g., a TMS320C6000, TMS320C5000, . . . etc). The DSP in this embodiment may be ed within an add-on card to a personal computer such as, for example, a PCI card. Of course, a variety of 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 machinereadable medium for storing the machine-executable instructions. The machinereadable medium may include, but is not limited to, flash memory, optical disks, CDROMs , DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of machine-readable media suitable for g electronic instructions. For example, the present invention may be downloaded as a computer program which may be transferred from a remote computer (e.g., a ) 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 k tion).

Throughout the foregoing description, for the purposes of explanation, numerous specific details were set forth in order to provide a thorough understanding of the present system and method. It will be apparent, r, to one skilled in the art that the system and method may be ced without some of these specific details. Accordingly, the scope and spirit of the present ion should be judged in terms of the claims which follow.

Moreover, hout the foregoing description, numerous publications were cited to provide a more thorough understanding of the present invention. All of 6181P524XPCT [CORRECTED SPECIFICATION] these cited references are incorporated into the present application by reference.

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Claims (18)

1. A le user (MU)-multiple antenna system (MAS) comprising a plurality of distributed transceivers and a plurality of users, wherein: the transceivers are equipped with antennas and are distributed throughout a cell, the users are placed in between any of the antennas of the distributed transceivers, and the distributed transceivers atively create volumes of coherent signals in wireless channels to generate multiple terfering data streams with the plurality of users, wherein each volume is ed around only one user and is used to transfer the data stream only to that user.

2. The system as in claim 1 wherein precoding is used to create separate s of coherent signals to the plurality of users.

3. The system as in claim 2 wherein near precoding or linear precoding is employed to create the non-interfering data streams to the plurality of users.

4. The system as in claim 3 wherein the non-linear precoding comprises dirty-paper coding (DPC) or Tomlinson-Harashima ing and the linear precoding comprises block diagonalization (BD) or zero-forcing beamforming (ZF-BF).

5. The system as in claim 2 wherein precoding is computed from the channel state information (CSI) between the users and the MU-MAS.

6. The system as in claim 2 wherein precoding employs limited feedback techniques.

7. The system as in claim 2 wherein the precoding is continuously updated to create non-interfering volumes of coherent signals to the users as the wireless channel changes due to Doppler effect.

8. The system as in claim 2 n the size of the volumes of coherent signals is dynamically adjusted depending on the distribution of users.

9. The system as in claim 1 wherein the system is a distributed input, distributed output (DIDO) system.

10. A method implemented within a multiple user ultiple antenna system (MAS) comprising a plurality of distributed transceivers and a plurality of users, wherein: the transceivers are ed with antennas and are distributed throughout a cell, the users are placed in between any of the antennas a subset of the distributed transceivers, and the method comprising: creating volumes of coherent signals in ss to generate multiple non-interfering data s with the plurality of users, wherein each volume is confined around only one user and is used to transfer the data stream only to that user.

11. The method as in claim 10 further comprising: precoding data streams prior to transmission to create separate volumes of coherent signals to the plurality of users.

12. The method as in claim 11 wherein near precoding or linear precoding is employed to create the non-interfering data streams to the plurality of users.

13. The method as in claim 12 wherein the non-linear precoding comprises dirtypaper coding (DPC) or son-Harashima precoding and the linear precoding comprises block diagonalization (BD) or orcing beamforming (ZF-BF).

14. The method as in claim 11 wherein precoding is computed from the channel state information (CSI) between the users and the MU-MAS.

15. The method as in claim 11 n precoding employs limited feedback techniques.

16. The method as in claim 13 further comprising: continuously updating precoding to create non-interfering volumes of coherent signals to the users as the wireless channel changes due to Doppler effect.

17. The method as in claim 11 further sing: dynamically adjusting size of volumes of coherent signals depending on the bution of users.

18. The method as in claim 10 wherein the MU-MAS system is a distributed input, distributed output (DIDO) system.