JP2017225138A - System and method for dealing with doppler effect in distributed input distributed output radio system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a system and a method for correcting harmful effect of Doppler to a performance of a distributed input distributed output (DIDO) system.SOLUTION: A system and a method for correcting harmful effect of the Doppler to a performance of a DIDO system are disclosed. According to an embodiment of the system, a different selection algorithm for adaptively adjusting active BTS while matching to a different UE is used on the basis of tracking of a channel state to be changing. According to an another embodiment, a future CSI or DIDO pre-coding weight is estimated by using a channel prediction, and thereby removing an error due to an old CSI.SELECTED DRAWING: Figure 53

Description

(Cross-reference of related applications)
This application is a continuation-in-part of the following co-pending US patent applications:
US patent application Ser. No. 12 / 917,257 (filed Nov. 1, 2010, entitled “Systems And Methods To Coordinated Transmissions In Distributed Systems Via User Cluster 10, 10/2008, 8/2008, 10/2008, 8/2016, 12/98. Application, name “Interference Management, Handoff, Power Control And Link Adaptation In Distributed-Input Distributed-Output (DIDO) Communication Systems”, No. 16 / 20th Method F r Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements, currently issued US Patent No. 8,170,081 (issued May 1, 2012), US Patent Application No. 12 / 802,974 (June 2010) 16th filing, name “System And Method For Managination Inter-Cluster Handoff Of Clients Witch Traverse Multiple DIDO Clusters”, No. 12/802, 989 (Fed. Of A Client Between Different Distributed-Input-Distributed-Output (DIDO) Networks Based On Detected the Client, the 12th 802 in 958, filed on June 16, 2010, named “System and the Adon A Distributed-Input-Distributed-Output (DIDO) Network "), No. 12 / 802,975 (filed on June 16, 2010, name" System And Method For Link adaptation In DIDOM "). / 802,938 ( Application on June 16, 2010, “System And Method For DIDO Precoding Interpolation In Multicarrier Systems”, No. 12 / 630,627 (Application on December 3, 2009, Name “System and MetDrMetDr. No. 12 / 143,503 (filed Jun. 20, 2008, entitled “System and Method For Distributed Input-Distributed Output Wireless Communications”), currently issued US Patent No. 8,160,121 (2009). Issued on April 17)), US Patent Application No. 1 No. 894,394 (filed Aug. 20, 2007, name “System and Method for Distributed Input Distributed Output Wireless Communications”), currently issued US Patent No. 7,599,420 (October 6, 2009) U.S. Patent Application No. 11 / 894,362 (August 20, 2007, name "System and method for Distributed Input-Distributed Wireless Communications"), currently issued U.S. Patent No. 7,633,994 (December 2009). (Issued on the 15th)), U.S. Patent Application No. 11 / 894,540 (filed on August 20, 2007, entitled "System and Methodo") For Distributed Input-Distributed Output Wireless Communications, currently issued US Patent No. 7,636,381 (issued December 22, 2009)), US Patent Application No. 11 / 256,478 (filed October 21, 2005) , “System and Method for Spatial-Multiplexed Tropostic Scatter Communications”, currently US Patent No. 7,711,030 (issued May 4, 2010), US Patent Application No. 10 / 817,731 (2004) Filed April 2, 1980, entitled “System and Method for Enhancing Near Vertical Incident Skyw” ave (“NVIS”) Communication Usage Space-Time Coding ”, currently issued US Pat. No. 7,885,354 (issued February 28, 2011).

  Prior art multi-user radio systems can include only a single base station or several base stations.

  A single WiFi base station (eg, 2.4 GHz 802) attached to a broadband wired Internet connection in an area where there is no other WiFi access point (eg, a WiFi access point attached to a rural home DSL) .11b, g, or n protocol) is an example of a relatively single multi-user radio system that is a single base station shared by one or more users in its transmission range It is. When a user is in the same room as a wireless access point, the user typically receives a high-speed link with little disruption of communication (eg, from 2.4 GHz interference, similar to a microwave oven). There may be packet loss not from the spectrum shared with other WiFi devices). If the user is at a medium distance, or if some obstacle is in the path between the user and the WiFi access point, the user will probably receive a medium speed link. If the user is approaching the end of the WiFi access point range, the user will probably receive a slow link and the channel change will result in periodic dropouts if the signal SNR falls below the usable level. It may be easy to receive. Finally, if the user is out of range of the WiFi base station, the user has no link at all.

  When multiple users access the WiFi base station simultaneously, the available data yield is shared among the multiple users. Different users typically have different yield requirements for a WiFi base station at a given time, but some when the total yield requirement exceeds the available yield from the WiFi base station to the user. Or all users will receive less data yield than they want. In extreme situations where WiFi access points are shared among a very large number of users, the yield to each user can be slow, and worse, the data yield to each user is all In some cases, short bursts separated by a long period without data yield may arrive, and during that time, other users are being dealt with. Similar to media streaming, this “discontinuous” data delivery may detract from certain applications.

  Even if more WiFi base stations are added in a situation where there are many users, the effect is only to a certain point. In the US 2.4 GHz ISM band, there are three non-interfering channels that can be used for WiFi, and three WiFi base stations in the same coverage area are each configured to use different non-interfering channels In addition, the total yield of the coverage area among multiple users will increase up to three times. However, if it is larger than that, the total yield will not increase even if WiFi base stations are added in the same coverage area. This is because the use of spectrum “in turn” makes use of time division multiple access (TDMA) and begins to share the same available spectrum between WiFi base stations. This situation is often seen in coverage areas with high population density, such as in apartment buildings. For example, users in large apartments with WiFi adapters serve other users in the same coverage area, even if the user's access point is in the same room as the client device accessing the base station There is plenty of risk of receiving very poor yields due to dozens of other interfering WiFi networks (eg, other apartments). The link quality is likely to be good in that situation, but the user will be subject to interference from neighboring WiFi adapters operating in the same frequency band, reducing the effective yield to the user.

There are some limitations in current multi-user wireless systems that include unlicensed and licensed spectrum such as WiFi. These include coverage area, downlink (DL) data rate, and uplink (UL) data rate. An important goal of next generation wireless systems (such as WiMaX and LTE) is to improve coverage area, DL data rate, and UL data rate through multiple input multiple output (MIMO) technology. In MIMO, multiple antennas are used on the transmit and receive sides of a radio link to improve link quality (resulting in increased coverage) or data rate (by creating multiple non-interfering spatial channels for every user). Use on the side. If sufficient data rates are available to all users (Note: the terms “user” and “client” are used interchangeably herein), multiple users according to the multi-user MIMO (MU-MIMO) method It would be desirable to utilize channel space diversity to create a non-interfering channel (rather than a single user). For example, see the following references:
G. Caire and S.M. Shamai, “On the achable throughput of a multitenna Gaussian broadcast channel”, IEEE Trans. Info. Th. , Vol. 49, pp. 1691-1706, July 2003.

  P. Viswanath and D.W. 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.M. Jindal, and A.J. 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.H. Cioffi, “Sum capacity of Gaussian vector broadcast channels”, IEEE Trans. Info. Th. , Vol. 50, pp. 1875-1892, Sep. 2004.

  M.M. Costa, “Writing on Dirty Paper”, IEEE Transactions on Information Theory, vol. 29, pp. 439-441, May 1983.

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

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

  M.M. Sharif and B.M. 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, with a MIMO 4 × 4 system (ie, 4 transmit antennas and 4 receive antennas), 10 MHz bandwidth, 16-QAM modulation, and rate 3/4 (obtains 3 bps / Hz spectral efficiency) and forward For false signal correction (FEC) coding), the ideal peak data rate achievable at the physical layer for any user is 4 × 30 Mbps = 120 Mbps, which means high resolution video content (only 10 Mbps) Is much higher than the peak data rate required to deliver. For an MU-MIMO system with 4 transmit antennas, 4 users, and a single antenna per user in an ideal scenario (ie independent codistribution, iid channel), downlink data transfer The speed can be shared across four users and channel space diversity can be used to create four parallel 30 Mbps data links to the user.

  For example, 3GPP, “Multiple Input Multiple Output in UTRA”, 3GPP TR 25.876 V7.0.0, Mar. 2007, 3GPP, “Base Physical channels and modulation”, TS 36.211, V8.7.0, May 2009, and 3GPP, “Multiplexing and channel coding”, TS 36.212, V8.7.0, May 200. As explained, different MU-MIMO schemes have been proposed as part of the LTE standard. However, in these systems, in DL, the data transfer rate can be improved up to twice as much with four transmission antennas. Practical examples of MU-MIMO methods in standard and proprietary cellular systems by companies such as ArrayComm (eg, ArrayComm, “Field-proven results”, http://www.arraycom.com/server.php? In the case of page = prof), the DL data rate is increased up to 3 times (with four transmission antennas) through space division multiple access (SDMA). An important limitation of the MU-MIMO scheme in mobile phone networks is the lack of spatial diversity at the transmission side. Spatial diversity is a function of antenna spacing and multipath angular spread in the radio link. In cellular systems using the MU-MIMO method, the transmit antennas at the base station are typically clustered together and the antenna support structure (whether physically higher or lower). (Referred to as “towers”) for limited land and buildings on top, and for restrictions on where the tower can be located, only one or two wavelengths apart. Furthermore, the multipath angular spread is low. This is because the portable radio tower is typically provided at a position (10 meters or higher) that is considerably higher than the obstacle so that the coverage can be expanded.

  Other practical issues with cellular system deployment include location over-cost and limited availability with respect to cellular antenna location (eg local government restrictions on antenna placement, land and building costs, physical obstacles, etc.). As well as the cost and / or availability of network connectivity (referred to herein as “backhaul”) with the transmitter. In addition, cellular systems are often difficult to reach clients at the back of buildings due to losses from walls, ceilings, floors, fixtures, and other obstructions.

  In fact, the overall concept of a cell structure for wide area network radios is that a fairly robust arrangement of mobile towers, frequency shifts between adjacent cells, and transmitters (base stations or users using the same frequency) Assuming frequent sectorization to avoid interference between any of the above. As a result, a given sector of a given cell ends up sharing a block of UL and DL spectra among all of the users in the cell sector, and the cell sector is then between these users primarily in the time domain. Shared. For example, cellular systems based on time division multiple access (TDMA) and code division multiple access (CDMA) both share spectrum between users in the time domain. Overlapping such cellular systems with sectorization can probably provide a space area advantage of 2-3 times. Next, by superimposing such a cellular system with a MU-MIMO system as described above, it may possibly provide a further 2 to 3 times space-time domain advantage. However, even with this limited advantage given a fixed time, given that the cells and sectors of a cellular system are typically in a fixed location, often specified by where the tower can be installed The user density (or data transfer rate requirement) of this is difficult to use if it does not fit well in the tower / sector arrangement. Mobile smartphone users are often affected today, who may be talking on the phone or downloading a web page without any trouble and then driving to a new location ( You will suddenly witness the voice quality after moving (or walking), or the web page slows down or even loses connectivity completely. However, as the day changes, the user may experience the exact opposite at each location. Assuming that the environmental conditions are the same, the user is probably experiencing that the user density (or data rate requirement) varies greatly. However, the total available spectrum to be shared between users at a given location (and thereby the total data transfer rate using the prior art) is mainly fixed.

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

Thus, in summary, prior art cellular systems may lose 3x spectrum utilization due to cellularization, and possibly 3x through sectorization, and perhaps 3x more through MU-MIMO methods. Can be improved, resulting in a net spectral utilization of 3 ^* 3/3 = 3 times. The bandwidth is then divided among the users, typically in the time domain, based on which sector of which cell the user falls into at a given time. There is even another inefficiency that arises because a given user's data rate requirement is typically independent of the user's location, but the available data rate is the link between the user and the base station. Varies based on quality. For example, users far from cellular base stations typically have less available data rates than users near the base station. As data rates are typically shared among all users within a given cellular sector, this results in all users being distant users with poor link quality (eg, at the edge of a cell). Affected by high data transfer rate requirements. This is because such users still require the same amount of data transfer rate and consume more of the shared spectrum to get the same amount of data transfer rate. .

  What is used by WiFi (eg, 802.11b, g, and n) because the simultaneous transmission by the base station in the user's range results in interference, and therefore the system utilizes collision prevention and shared protocols, and Other proposed spectrum sharing systems, such as those proposed by the White Space Federation, are very inefficient in spectrum sharing. These spectrum sharing protocols are in the time domain and therefore collectively no matter how efficient each base station's own spectrum utilization is when there are many interfering base stations and users. Base stations are limited to time domain sharing of spectrum between each other. Other prior art spectrum sharing systems also rely on similar methods to mitigate interference between base stations (cellular base stations with tower antennas or small base stations (WiFi access points (AP) Like)). These methods include limiting transmit power from the base station to limit the range of interference, beamforming (through synthesis or physical means) to narrow the interference area, time domain multiplexing of the spectrum, and / or Or there is a MU-MIMO method with multiple clustered antennas on the user device, base station, or both. And in the case of advanced mobile phone networks already existing or planned today, many of these technologies are often used at once.

  However, it is clear that all of these technologies are receivable, even in advanced cellular systems, which is evident by the fact that spectrum utilization can only be increased about three times compared to a single user utilizing spectrum. It is of little use to increase the total data transfer rate between shared users towards a given area of the range. In particular, as a given coverage area expands from the user's perspective, it becomes increasingly difficult to increase the available data transfer rate within a given amount of spectrum in order to keep pace with the user's growth. For example, to increase the total data transfer rate within a given area for a cellular system, cells are typically subdivided into smaller cells (often referred to as nanocells or femtocells). Such small cells have a “dead band” that provides minimal coverage, and further limits on where the tower can be set to avoid interference between nearby cells using the same frequency, And the requirement that the towers should be arranged in a fairly structured pattern can be very expensive. In essence, the coverage area must be carefully planned, the available locations where towers or base stations are to be identified, and then given these constraints, the designer of the cellular system can: We must make it in time for the best possible. And, of course, if user data rate requirements increase over time, the cellular system designer will try to remap the coverage area, find the location of the tower or base station, and once again the situation Should be addressed. Also, in very many cases, there is simply no good solution, resulting in a dead band or inadequate total data rate capacity in the coverage area. In other words, the robust physical deployment requirements of cellular systems that avoid interference between towers or base stations that use the same frequency create significant problems and constraints in cellular system design, which often are User data rate and coverage requirements cannot be met.

  So-called prior art “cooperative” radio systems and “cognitive” radio systems have no or potential use of other spectra so that they can minimize interference with each other and / or wait until the channel is free. Trying to increase spectrum utilization within a given area by using intelligent algorithms in the air so that they can be “listened” to the target. Systems have been proposed that are specifically used in unlicensed spectrum to increase the spectral utilization of such spectra.

  Mobile ad hoc network (MANET) (see http://en.wikipedia.org/wiki/Mobile_ad_hoc_network) is an example of a collaborative self-configuring network aimed at providing peer-to-peer communication and without mobile phone infrastructure It can be used to establish communication between radios, and with sufficiently low power communication, it can potentially mitigate interference between simultaneous transmissions that are out of range of each other. A very large number of routing protocols have been proposed and implemented for MANET systems (for a list of dozens of routing protocols in a broad class, see http://en.wikipedia.org/wiki/List_of_ad- hoc_routing_protocols), a common theme for routing protocols, is to route transmissions such that they minimize transmitter interference in the spectrum that is available towards the goal of a given efficiency or reliability paradigm (e.g. , Repeat) all technologies.

  All prior art multi-user wireless systems seek to improve spectrum utilization within a given coverage area by utilizing techniques that allow simultaneous spectrum utilization between a base station and multiple users. In particular, in all of these cases, techniques utilized for simultaneous spectrum utilization between the base station and multiple users result in simultaneous spectral usage by multiple users by mitigating inter-waveform interference for multiple users. . For example, in the case of three base stations, each using a different frequency to transmit to one of the three, the interference is mitigated because the three transmissions are at three different frequencies. In the case of sectorization from the base station to three different users, beamforming mitigates the interference at 180 ° intervals each to the base station to prevent the three transmissions from overlapping by the user. .

  When such techniques are augmented with MU-MIMO and each base station has four antennas, for example, this can be reduced by creating four non-interfering spatial channels for users in a given coverage area. May increase link yield by a factor of four. However, some technology should still be used to mitigate interference between multiple simultaneous transmissions for multiple users in different coverage areas.

  As noted above, such prior art (eg, cellularization, sectorization) is typically not only problematic in increasing the cost and / or deployment flexibility of multi-user radio systems, but is also typical. There are physical and practical limitations on the total yield within a given coverage area. For example, a cellular system may not have enough locations available to increase the number of base stations and make it smaller. Also, in the MU-MIMO system, when considering the spacing of clustered antennas at each base station location, yield profits are asymptotically as the number of antennas added to the base station increases due to limited space diversity. Decrease.

  Furthermore, yields are unpredictable (due to sudden changes in frequency) in the case of multi-user wireless systems where user location and density cannot be predicted. This is inconvenient for the user and makes some applications (eg, delivery of services that require predictable yield) impractical or of poor quality. Thus, the prior art multi-user wireless systems are still unsatisfactory in terms of their ability to provide predictable and / or high quality services to users.

  Despite the tremendous sophistication and complexity developed over time for prior art multi-user radio systems, transmissions are delivered to different base stations (or ad hoc transceivers) and different base stations And / or there is a common theme that RF waveform transmissions from different ad hoc transceivers are configured and / or controlled to avoid interfering with each other at a particular user's receiver.

  Or, in other words, if a user accidentally received a transmission from one or more base stations or ad hoc transceivers simultaneously, the SNR and / or bandwidth of the signal to the user due to interference from multiple simultaneous transmissions A reduction in width will occur and as a result, if sufficiently severe, all or some loss of potential data (or analog information) that would have been received by the user if not severe enough Will occur.

  Thus, a multi-user radio system avoids such interference to users from multiple base stations or ad hoc transceivers that simultaneously transmit on the same frequency using one or more spectrum sharing techniques or another spectrum sharing technique. Or it needs to be mitigated. Includes control of base station physical location (eg, cellularization), base station and / or ad hoc transceiver power output limitations (eg, transmission range limitations), beamforming / sectorization, and time domain multiplexing There are numerous prior art techniques to avoid such interference. In short, all of these spectrum sharing systems have reduced data yields for users affected by interference obtained when multiple base stations and / or ad hoc transceivers simultaneously transmitting on the same frequency are received by the same user, or Attempts to address the limitations of multi-user radio systems being destroyed. Multi-user radio when most or all of the users in the multi-user radio system are subject to interference from multiple base stations and / or ad hoc transceivers (eg, in the event of malfunction of a component of the multi-user radio system) Situations can arise where the total yield of the system is drastically reduced or fails.

  Prior art multi-user wireless systems add complexity and introduce limitations to the wireless network, and thus often a given user experience (eg, available bandwidth, latency, predictability, A situation occurs where reliability is affected by the use of spectrum by other users in the area. For increasing demands on total bandwidth in the radio spectrum shared by multiple users and applications that may depend on the reliability, predictability, and short latency of multi-user wireless networks for a given user In view of further growth, it is clear that there are many limitations in the prior art multi-user radio technology. Indeed, because of the limited availability of spectrum suitable for certain types of radio communications (eg, at wavelengths that are efficient when passing through building walls), prior art radio technologies are reliable. May be insufficient to meet the growing demand for bandwidth that is high, predictable, and has low latency.

Prior art relating to the present invention describes a beamforming system and method for null steering in a multi-user scenario. Beamforming inherently maximizes the received signal-to-noise ratio (SNR) by dynamically adjusting the phase and / or amplitude of signals supplied to the antennas of the array (ie, beamforming weights), and thus It is considered that the energy is concentrated toward the user direction. In multi-user scenarios, beamforming can be used to suppress interference sources and maximize the signal to interference noise ratio (SINR). For example, when beamforming is used in a radio link receiver, the weight is calculated to produce a null in the direction of the interference source. When beamforming is used at the transmitter in a multi-user downlink scenario, the weights are calculated to pre-cancel inter-user interference and maximize SINR for all users. Alternative techniques for multi-user systems, such as BD precoding, calculate precoding weights to maximize yield in the downlink broadcast channel. The co-pending application, incorporated herein by reference, describes the techniques described above (see co-pending application for specific citations).
The invention will be better understood by reading the following description of the best mode for carrying out the invention in conjunction with the drawings.

Fig. 4 shows a main DIDO cluster surrounded by neighboring DIDO clusters in one embodiment of the present invention. 2 illustrates a frequency division multiple access (FDMA) method used in one embodiment of the present invention. 2 illustrates a time division multiple access (TDMA) method used in one embodiment of the present invention. Fig. 4 illustrates different types of interference zones addressed in an embodiment of the invention. Figure 3 shows a framework used in one embodiment of the present invention. FIG. 6 shows a graph showing SER as a function of SNR assuming SIR = 10 dB for a target client in the interference zone. Figure 5 shows a graph showing SER derived from two IDCI precoding methods. FIG. 6 illustrates an exemplary scenario where a target client moves from a primary DIDO cluster to an interfering cluster. Fig. 4 shows the signal to interference noise ratio (SINR) as a function of distance (D). Figure 3 shows the code error rate (SER) performance of three scenarios of 4-QAM modulation in a flat fading narrowband channel. Fig. 4 illustrates a method of IDCI precoding according to an embodiment of the present invention. FIG. 6 illustrates SINR variation as a function of client distance from the center of the main DIDO cluster in one embodiment. FIG. Fig. 4 illustrates an embodiment in which the SER is derived for 4-QAM modulation. Fig. 4 illustrates an embodiment of the present invention in which a finite state machine executes a handoff algorithm. FIG. 4 illustrates one embodiment of a handoff strategy when shadowing is present. FIG. FIG. 93 shows a hysteresis loop mechanism when switching between any two states. 1 illustrates one embodiment of a DIDO system with power control. Figure 5 shows SNR vs. SER assuming 4 DIDO transmit antennas and 4 clients in different scenarios. FIG. 6 shows MPE power density as a function of distance from an RF radiation source for different values of transmit power according to an embodiment of the present invention. 2 shows different distributions of low power and high power DIDO distributed antennas. 2 shows different distributions of low power and high power DIDO distributed antennas. Two power distributions respectively corresponding to the configurations of FIGS. 20a and 20b are shown. Two power distributions respectively corresponding to the configurations of FIGS. 20a and 20b are shown. 99a and 99b show the velocity distribution for the scenario shown respectively. 99a and 99b show the velocity distribution for the scenario shown respectively. 1 illustrates one embodiment of a DIDO system with power control. FIG. 6 illustrates an embodiment of a method for iterating across all antenna groups according to a round robin scheduling policy for transmitting data. A comparison of uncoded SER performance of power control with antenna grouping for conventional eigenmode selection in US Pat. No. 7,636,381 is shown. FIG. 6 illustrates a scenario where BD precoding dynamically adjusts precoding weights to adapt to different power levels across the radio link between a DIDO antenna and a client. FIG. 6 illustrates a scenario where BD precoding dynamically adjusts precoding weights to adapt to different power levels across the radio link between a DIDO antenna and a client. FIG. 6 illustrates a scenario where BD precoding dynamically adjusts precoding weights to adapt to different power levels across the radio link between a DIDO antenna and a client. The amplitude of the low frequency selection channel (assuming β = 1) over the delay domain or instantaneous PDP (upper plot) and frequency domain (lower plot) for the DIDO 2 × 2 system. FIG. 4 illustrates one embodiment of a channel matrix frequency response for DIDO 2 × 2 with one antenna per client. FIG. 6 illustrates one embodiment of a channel matrix frequency response for DIDO 2 × 2 with one antenna per client for a channel characterized by high frequency selectivity (eg, with β = 0.1). Fig. 3 shows exemplary SERs for different QAM schemes (i.e. 4-QAM, 16-QAM, 64-QAM). 3 illustrates an embodiment of a method for performing a link adaptation (LA) method. FIG. 5 illustrates SER performance of one embodiment of a link adaptation (LA) method. FIG. FIG. 6 shows the matrix input in equation (28) as a function of OFDM tone index for a DIDO 2 × 2 system with N _FFT = 64 and L ₀ = 8. FIG. 6 shows SER vs. SNR for L ₀ = 8, M = N _t = 2 transmit antennas, and a variable number of Ps. FIG. 6 shows the SER performance of one embodiment of the interpolation method for different DIDO orders and L ₀ = 16. 1 illustrates one embodiment of a system using a super cluster, a DIDO cluster, and a user cluster. 1 illustrates a system having a user cluster according to an embodiment of the present invention. Fig. 5 illustrates a link quality metric threshold used in an embodiment of the present invention. Fig. 5 illustrates a link quality metric threshold used in an embodiment of the present invention. 2 shows an example of a link quality matrix that establishes a user cluster. 2 shows an example of a link quality matrix that establishes a user cluster. 2 shows an example of a link quality matrix that establishes a user cluster. Fig. 4 illustrates an embodiment where a client traverses different DIDO clusters. In one embodiment of the present invention, the relationship between the resolution of a spherical array and these areas A is shown. In one embodiment of the present invention, the relationship between the resolution of a spherical array and these areas A is shown. In one embodiment of the present invention, the relationship between the resolution of a spherical array and these areas A is shown. In one embodiment of the present invention, the relationship between the resolution of a spherical array and these areas A is shown. The degree of freedom of the MIMO system in practical indoor and outdoor propagation scenarios is shown. Fig. 4 shows the degrees of freedom in a DIDO system as a function of array diameter. 1 illustrates one embodiment including a plurality of centralized processors (CPs) and distributed nodes (DNs) communicating over wired or wireless connections. Fig. 4 illustrates an embodiment where a CP exchanges control information with an unauthorized DN and reconfigures them to stop the frequency band for authorized use. FIG. 6 illustrates an embodiment where all spectrum is allocated to a new service and the CP uses control information to stop all unauthorized DNs and avoid interference with authorized DNs. 1 illustrates one embodiment of a cloud wireless system that includes a plurality of CPs, distributed nodes, and a network that interconnects the CPs to a DN. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. 1 illustrates an embodiment of a multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or propagation environment changes. Shows multiple BTSs, some with good SNR, some with low Doppler for the UE. FIG. 6 illustrates one embodiment of a matrix including SNR and Doppler values recorded by a CP for multiple BTS-UE links. FIG. Fig. 5 shows channel gain (or CSI) at different times according to an embodiment of the invention.

  One solution that overcomes many of the limitations of the prior art described above is an embodiment of a distributed input distributed output (DIDO) technique. DIDO technology is described in the following patents and patent applications, all of which are assigned to the assignee of this patent and incorporated by reference. This application is a continuation-in-part (CIP) of these patent applications. These patents and applications may be collectively referred to herein as “related patents and applications”.

  US Patent Application No. 13 / 232,996 (filed September 14, 2011, name “Systems and Methods To Exploit Area of Coherence in Wireless Systems”).

  No. 13 / 233,006 (filed on Sep. 14, 2011, name “Systems and Methods for Planned Evolution and Obsolescence of Multiuser Spectrum”).

  No. 12 / 917,257 (filed on Nov. 1, 2010, name “Systems And Methods To Coordinated Transmissions In Distributed Wireless Systems Via User Clustering”).

  No. 12 / 802,988 (filed Jun. 16, 2010, name "Interference Management, Handoff, Power Control And Link Adaptation-In-Distributed-Output Distributed-Output (DIDO) Coimulation)".

  No. 12 / 802,976 (filed on June 16, 2010, name “System And Method For Adjusting DIDO Interference Cancellation Based On Signal Strength Measurements”).

  No. 12 / 802,974 (filed Jun. 16, 2010, name “System And Method For Managing Inter-Cluster Handoff Of Clients Whitch Multiples DIDO Clusters”).

  No. 12 / 802,989 (filed on June 16, 2010, name “System And Method For Managing Handoff Of A Client Between Differed-Input-DuttedDuttedDuttedDuttedDuttedDuttedDuttedDoutedDuttedDuttedDuttedDuttedDuttedDuttedDuttedDuttedDuttedDuttedDuttedDuttedDoutedD .

  No. 12 / 802,958 (filed Jun. 16, 2010, name “System And Method For Power Control And Antenna Grouping In A Distributed-Input-Distributed-OutputN”).

  No. 12 / 802,975 (filed on June 16, 2010, name “System And Method For Link Adaptation In DIDO Multi-carrier System”).

  No. 12 / 802,938 (filed on June 16, 2010, name “System And Method For DIDO Precoding Interpolation In Multicarrier Systems”).

  No. 12 / 630,627 (filed on Dec. 2, 2009, name “System and Method For Distributed Antenna Wireless Communications”).

  US Pat. No. 7,599,420 (filed Aug. 20, 2007, issued Oct. 6, 2009, name “System and Method for Distributed Input Distributed Output Communication Communication”).

  No. 7,633,994 (filed Aug. 20, 2007, issued Dec. 15, 2009, name “System and Method for Distributed Input Distributed Output Wireless Communication”).

  No. 7,636,381 (filed Aug. 20, 2007, issued Dec. 22, 2009, name “System and Method for Distributed Input Distributed Output Wireless Communication”).

  US patent application Ser. No. 12 / 143,503 (filed Jun. 20, 2008, entitled “System and Method For Distributed Input-Distributed Output Wireless Communications”).

  No. 11 / 256,478 (filed Oct. 21, 2005, name “System and Method for Spatial-Multiplexed Tropological Scatter Communications”).

  US Pat. No. 7,418,053 (filed July 30, 2004, issued August 26, 2008, name “System and Method for Distributed Input Distributed Output Wireless Communication”).

  US patent application Ser. No. 10 / 817,731 (filed Apr. 2, 2004, entitled “System and Method for Enhancing Near Vertical Indication Skywave (“ NVIS ”) Communication Usage Space-Time Coding).

  In order to reduce the size and complexity of this patent application, some disclosures of related patents and applications are not explicitly described below. See the related patents and applications for a complete detailed description of the disclosure.

  Section I below (disclosure from related application No. 12 / 802,988) utilizes a prior art reference assigned to the applicant of this application and a unique set of endnotes pointing to the prior application. Please note that. The citation at the end of the book appears at the end of section I (just before the section II heading). A citation in Section II will be identified by a number that matches the numerical notation used in Section I for that citation, even though it identifies references that differ in their numerical notation (described at the end of Section II). May have a notation. Thus, a reference identified by a particular number can be identified within a section where a numerical notation is used.

I. Disclosure from Related Application No. 12 / 802,988 Method for Eliminating Intercluster Interference A radio frequency (RF) communication system and method that uses multiple distributed transmit antennas to create locations in a space with zero RF energy is described below. When using M transmit antennas, up to (M-1) points of zero RF energy can be created at a given location. In one embodiment of the invention, the zero RF energy point is a wireless device and the transmit antenna is aware of channel state information (CSI) between the transmitter and the receiver. In one embodiment, the CSI is calculated at the receiver and fed back to the transmitter. In another embodiment, CSI is calculated at the transmitter through training from the receiver assuming channel correlation is utilized. The transmitter can use CSI to determine interference signals that are transmitted simultaneously. In one embodiment, block diagonalization (BD) precoding is used at the transmit antenna to generate zero RF energy points.

  The systems and methods described herein differ from the conventional receive / transmit beamforming methods described above. In practice, receive beamforming calculates weights (through null steering) to suppress interference at the receiver side, while some embodiments of the invention described herein result in a “zero” Weights are applied at the transmitter side to create an interference pattern that results in one or more locations in space having “RF energy”. Unlike conventional transmit beamforming or BD precoding, designed to maximize signal quality (or SINR) or downlink yield, respectively, for any user, the systems and methods described herein are Under circumstances and / or minimize signal quality from a given transmitter, a zero RF energy point is created at the client device (sometimes referred to herein as a “user”). Further, in the context of a distributed input distributed output (DIDO) system (as described in our related patents and applications), multiple zero RF energy points and An extension of the degrees of freedom that can be used to create the maximum SINR (ie, an increase in channel space diversity) is obtained. For example, with M transmit antennas, up to (M-1) points of RF energy can be created. In contrast, practical beamforming or BD multi-user systems are typically simultaneous, which can be addressed over the radio link so that any number of transmit antennas M can be obtained at the transmit side. Designed with a compact antenna that limits the number of users.

  Consider a system with K <M and M transmit antennas and K users. The transmitter is between M transmit antennas and K users.

Is recognized. For simplicity, it is assumed that every user is equipped with a single antenna, but the same method can be extended to multiple receive antennas per user. Precoding weights to create zero RF energy at the location of K users

Is calculated to satisfy the following condition.
Hw = 0 ^{K × 1}
Where 0 ^{K × 1} is a vector with all zero inputs and H is a channel vector from M transmit antennas to K users.

By combining

As a channel matrix.

  In one embodiment, the singular value decomposition (SVD) of the channel matrix H is calculated and the precoding weight w is defined as the right singular vector corresponding to the null subspace of H (identified by 0 single values).

  The transmit antenna is configured to transmit RF energy while creating K zero RF energy points at the location of K users, as shown in the following equation for the signal received by the k th user: Use the weight vector defined in.

Where

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

  In one embodiment, the singular value decomposition (SVD) of the channel matrix H is calculated and the precoding weight W is defined as the right singular vector corresponding to the null subspace of H (identified by 0 single values).

In another embodiment, the wireless system is a DIDO system, where zero RF energy points are created to pre-cancele interference to clients between different DIDO coverage areas. In US patent application Ser. No. 12 / 630,627, a DIDO system is described that includes:
・ DIDO client ・ DIDO distributed antenna ・ DIDO base transceiver station (BTS)
・ DIDO Base Station Network (BSN)

  Every BTS is connected through a BSN to a plurality of distributed antennas that serve a given coverage area called a DIDO cluster. This patent application describes a system and method for eliminating interference between adjacent DIDO clusters. As shown in FIG. 1, it is assumed that the primary DIDO cluster serves clients that are affected by interference from neighboring clusters (ie, user devices served by the multi-user DIDO system, ie, target clients).

In one embodiment, neighboring clusters operate at different frequencies according to a frequency division multiple access (FDMA) method similar to conventional cellular systems. For example, with a frequency reuse factor of 3, the same carrier frequency is repeated for every three DIDO clusters as shown in FIG. In FIG. 2, the different carrier frequencies are identified as F ₁ , F ₂ , and F ₃ . Although this embodiment can be used for some examples, this solution results in a reduction in frequency utilization efficiency. This is because the available spectrum is divided into multiple subbands, and only a subset of DIDO clusters operate in the same subband. In addition, complex cell designs require different DIDO clusters to be associated with different frequencies, thus preventing interference. Similar to prior art cellular systems, such cell designs require predetermined antenna placement and transmit power limitations to avoid interference between clusters using the same frequency.

In another embodiment, neighboring clusters operate in different time slots according to the time division multiple access (TDMA) method, but in the same frequency band. For example, as shown in FIG. 3, DIDO transmission is only allowed in time slots T ₁ , T ₂ , and T ₃ for a given cluster, as shown. Time slots are equally assigned to different clusters so that different clusters are scheduled according to a round robin policy. When different clusters are characterized by different data transfer rate requirements (ie clusters in crowded urban environments versus clusters in rural areas with low number of clients per coverage area), different priorities are used for data transfer Larger speed requirements are assigned to different clusters as more time slots are assigned. Although TDMA as described above can be used in an embodiment of the present invention, the TDMA approach may require time synchronization across different clusters and may result in reduced frequency utilization efficiency. This is because interference clusters cannot use the same frequency at the same time.

  In one embodiment, all neighboring clusters transmit simultaneously in the same frequency band and use spatial processing across the clusters to avoid interference. In this embodiment, the multi-cluster DIDO system uses (i) conventional DIDO pre-encoding within the main cluster to send simultaneous incoherent data streams to multiple clients within the same frequency band (eg, US (As described in related patents and application specifications including Patent Nos. 7,599,420, 7,633,994, 7,636,381, and U.S. Patent Application No. 12 / 143,503)), (Ii) DIDO precoding with interference cancellation in neighboring clusters to avoid interference for clients in interference zone 8010 in FIG. 4 by creating a point of zero radio frequency (RF) energy at the target client location. Is used. If the target client is in the interference zone 410, it receives the sum of the RFs that contain the data stream from the primary cluster 411 and zero RF energy from the interference clusters 412 to 413 that would simply be the RF that contains the data stream from the primary cluster. receive. Thus, neighboring clusters can utilize the same frequency at the same time without the target clients in the interference zone experiencing interference.

In a practical system, the performance of DIDO pre-coding is the channel estimation error or Doppler effect (old channel state information is generated with DIDO distributed antennas), intermodulation distortion (IMD) in multi-carrier DIDO system, time or frequency offset. May be affected by different factors such as As a result of these effects, it may be impractical to provide zero RF energy points. However, as long as the RF energy at the target client from the interfering cluster is negligible compared to the RF energy from the main cluster, the associated performance at the target client is not affected by the interference. For example, using forward error signal correction (FEC) coding to yield a target bit error rate (BER) of 10 ⁻⁶ , the client has a 20 dB signal-to-noise ratio (to demodulate the 4-QAM satellite constellation ( Assume that SNR) is required. If the RF energy at the target client received from the interfering cluster is 20 dB below the RF energy received from the main cluster, the interference is insignificant and the client can safely retrieve data within a given BER target. It can be demodulated. Thus, as used herein, the term “zero RF energy” does not necessarily mean that the RF energy from the interfering RF signal is zero. Rather, RF energy means that it is sufficiently low relative to the RF energy of the desired RF signal so that the desired RF signal can be received at the receiver. Furthermore, while a predetermined desired threshold of interference RF energy for the desired RF energy is described, the basic principles of the present invention are not limited to the predetermined threshold.

  There are different types of interference zones 8010 as shown in FIG. For example, the “type A” area (indicated by the letter “A” in FIG. 80) is affected by interference from only one neighboring cluster, while the “type B” area (indicated by the letter “B”). Corresponds to interference from two or more neighboring clusters.

  FIG. 5 illustrates the framework used in one embodiment of the present invention. The dots indicate DIDO distributed antennas, the cross symbol points to the DIDO client, and the arrows indicate the direction of RF energy propagation. The DIDO antenna in the main cluster transmits a pre-encoded data signal to the client MC 501 in that cluster. Similarly, DIDO antennas in an interference cluster serve client ICs 502 in that cluster through conventional DIDO precoding. A green cross symbol 503 indicates the target client TC 503 in the interference zone. The DIDO antenna in the main cluster 511 transmits a pre-encoded data signal to the target client (black arrow) through conventional DIDO pre-encoding. The DIDO antenna in interference cluster 512 uses precoding to create zero RF energy towards the target client 503 (green arrow).

  The received signal at the target client k in any interference zone 410A (FIG. 4B) is given by:

Where k = 1. . . K and K are the number of clients in interference zones 8010A and B, U is the number of clients in the primary DIDO cluster, C is the number of interference DIDO clusters 412 to 413, and I _c is in interference cluster c Is the number of clients. Further assume that there are M transmit DIDO antennas and N receive antennas at the client device,

Is a vector containing the received data stream at client k,

Is a vector of outgoing data streams to client k in the main DIDO cluster,

Is a vector of transmission data streams to client u in the main DIDO cluster,

Is a vector of transmission data streams to client i in the c th interfering DIDO cluster,

Is a vector of additive white Gaussian noise (AWGN) at the N receive antennas of client k,

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

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

Is a matrix of DIDO pre-encoding weights for client k in the main DIDO cluster,

Is a matrix of DIDO pre-encoding weights for client u in the main DIDO cluster,

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

  To simplify the notation and avoid loss of generality, all clients are equipped with N receive antennas, and there are M DIDO distributed antennas in every DIDO cluster,

Assume that If M is greater than the total number of receive antennas in the cluster, an additional transmit antenna can be used to pre-empt interference to target clients in the interference zone, or US Pat. No. 7,599,420, ibid. Link robustness to clients in the same cluster through diversity schemes described in related patents and applications, including 7,633,994, 7,636,381, and US patent application Ser. No. 12 / 143,503 Used to improve sex.

  DIDO pre-encoding weights are calculated to pre-cancel inter-client interference within the same DIDO cluster. For example, U.S. Patent Nos. 7,599,420, 7,633,994, 7,636,381, and U.S. Patent Application No. 12 / 143,503, and [7] Using block diagonalization (BD) precoding as described in related patents and applications, inter-client interference can be eliminated so that the following conditions are met in the main cluster.

  The pre-encoding weight matrix in the neighborhood DIDO cluster is designed so that the following conditions are met:

To calculate the precoding matrix W _{c, i} , the downlink channels from M transmit antennas to the l _c clients in the interference cluster as well as to the client k in the interference zone are estimated, and the precoding matrix is Calculated by DIDO BTS in the interference cluster. When the BD method is used to calculate the precoding matrix in the interference cluster, the following effective channel matrix is configured to calculate the weight to the i th client in the neighborhood cluster.

Where

Is the channel matrix for the interference cluster c

The column corresponding to the i th client is removed.

  By substituting the conditions (2) and (3) into (1), the received data stream is acquired for the target client k, and intra-cluster and inter-cluster interference is removed.

The pre-encoded weights W _{c, i} in (1) calculated in the neighboring clusters are pre-encoded for all clients in those clusters while pre-compensating for interference to target clients in the interference zone. Designed to send a stream. The target client receives pre-encoded data only from its main cluster. In different embodiments, the same data stream is sent from the primary cluster and neighboring clusters to the target client to obtain diversity gain. In this case, the signal model in (5) is expressed as follows.

Where W _{c, k} is the DIDO precoding matrix from the DIDO transmitter in the c th cluster to the target client k in the interference zone. Method (6) requires time synchronization across neighboring clusters, which may be complex to achieve in large systems, but if the diversity gain advantage still justifies the cost of implementation. Note that this is quite achievable.

  We start by assessing the performance of the proposed method in terms of code error rate (SER) as a function of signal-to-noise ratio (SNR). Without loss of generality, we define the following signal model assuming a single antenna per client and reformulation (1).

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

FIG. 6 shows the SER as a function of SNR assuming SIR = 10 dB for the target client in the interference zone. Without loss of generality, SER was measured for 4-QAM and 16-QAM without forward error correction (FEC) coding. Fix target SER at 1% for uncoded systems. This target corresponds to different values of SNR based on the modulation order (ie, SNR = 20 dB for 4-QAM and SNR = 28 dB for 16-QAM). When using FEC coding for coding gain, a lower SER target can be met for the same value of SNR. Consider a scenario of two clusters (one main cluster and one interfering cluster) with two DIDO antennas and two clients (equipped with each single antenna) per cluster. One of the clients in the main cluster is in the interference zone. Assuming a flat fading narrowband channel, the following results can be extended to a frequency selective multi-carrier (OFDM) system where each subcarrier undergoes flat fading. Two scenarios: (i) Pre-encoding weight scenario: one scenario where the pre-encoding weight W _{c, i} has DIDO intercluster interference (IDCI) calculated without corresponding to the target client in the interference zone And (ii) Consider the other scenario where the IDCI is removed by calculating the weight W _{c, i} to remove the IDCI to the target client. In the presence of IDCI, it is recognized that the SER is high and greater than the predetermined target. IDCI precoding with neighboring clusters removes interference to the target client and reaches the SER target so that SNR> 20 dB is obtained.

The result of FIG. 6 assumes IDCI precoding as in the case of (5). Yet another diversity gain is obtained when IDCI precoding in neighboring clusters is used to pre-encode the data stream to the target client in the interference zone as in (6). FIG. 7 illustrates two techniques: (i) “Method 1” using IDCI precoding in (5), and (ii) a neighboring cluster also sends a precoded data stream to the target client ( 6) comparing the SER derived from “Method 2” using IDCI precoding. Method 2 is ˜3 dB compared to conventional IDCI precoding due to yet another array gain obtained by the DIDO antenna in the neighboring cluster used to transmit the precoded data stream to the target client. Gain is obtained. More generally, the array gain of Method 2 over Method 1 is proportional to 10 ^* log10 (C + 1), where C is the number of neighboring clusters and the coefficient “1” refers to the main cluster. .

  Next, the performance of the previous method as a function of the location of the target client with respect to the interference zone is evaluated. Consider one simple scenario where the target client 8401 moves from the primary DIDO cluster 802 to the interference cluster 803 as shown in FIG. Assume that BD precoding is used to remove intra-cluster interference so that all DIDO antennas 812 in main cluster 802 satisfy condition (2). Assume a single interfering DIDO cluster, a single receive antenna at client device 801, and equal propagation loss from all DIDO antennas in the main or interfering cluster to the client (ie, circle around the client DIDO antenna provided). We use one simplified propagation loss model [11] with a propagation loss index of 4 (as in a typical urban environment).

  Subsequent analysis is based on the following simplified signal model that extends (7) to accommodate propagation losses.

Where the signal to interference ratio (SIR) is derived as SIR = ((1-D) / D) ⁴ . In modeling the IDCI, three scenarios, i) the ideal case without IDCI, ii) IDCI pre-cancelled through BD precoding in the interference cluster to satisfy condition (3), and iii) Consider IDCI and no prior removal by neighboring clusters.

FIG. 9 shows the signal to interference noise ratio (SINR) as a function of D (ie, when the target client moves from the main cluster 802 in the direction of the DIDO antenna 813 in the interference cluster 8403). The SINR is derived as noise power using the signal power and the signal model within the interference ratio plus (8). Assume D _o = 0.1 and SNR = 50 dB towards D = D _o . In the absence of IDCI, the radio link performance is affected only by noise and SINR is reduced due to propagation loss. Interference from DIDO antennas in neighboring clusters when IDCI is present (ie, without IDCI precoding) contributes to reducing SINR.

FIG. 10 shows the code error rate (SER) performance in three scenarios of 4QAM modulation in a flat fading narrowband channel. These SER results correspond to the SINR of FIG. Assume a 1% SER threshold for an uncoded system (ie, no FEC) corresponding to the SINR threshold SINR _T = 20 dB in FIG. The SINR threshold depends on the modulation order used for data transmission. Typically, higher SINR _T is characterized by higher modulation order to provide the same target error rate. With FEC, a lower target SER can be provided for the same SINR value due to coding gain. In the case of IDCI without precoding, the target SER is achieved only within the range D <0.25. With IDCI pre-coding with neighboring clusters, the range that satisfies the target SER is extended to D <0.6. Above that range, the SINR increases due to propagation loss and the target SER is not satisfied.

One embodiment of the IDCI pre-encoding method is shown in FIG. 11 and consists of the following steps.
SIR estimation 1101: The client estimates the signal power from the main DIDO cluster (ie based on the received pre-encoded data) and the noise plus interference signal power from neighboring DIDO clusters. In a single carrier DIDO system, the frame structure can be designed with a short silence period. For example, a silence period can be defined between channel estimation training and pre-encoded data transmission during channel state information (CSI) feedback. In one embodiment, noise plus interference signal power from neighboring clusters is measured during silence periods from DIDO antennas in the main cluster. Practical DIDO multi-carrier (OFDM) systems prevent null tones from being attenuated at the direct current (DC) and band edges that are typically offset for filtering at the transmit and receive sides. Used for. In another embodiment using a multi-carrier system, the noise plus interference signal power is estimated from null tones. A correction factor can be used to correct for transmit / receive filter attenuation at the edge of the band. Once the signal-to-noise plus interference power (P _S ) from the main cluster and the noise plus interference power from neighboring clusters (P _IN ) are estimated, the client calculates SINR as follows:

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

  It will be appreciated that the metrics in (9) cannot distinguish between noise and interference power levels. For example, clients that are affected by shadowing in an interference-free environment (ie, behind obstacles that attenuate signal power from all DIDO distributed antennas in the main cluster) are not affected even by intercluster interference. Even a low SINR can be estimated.

  A more reliable metric of the proposed method is the SIR calculated as follows:

In the equation, P _N is noise power. Practical multicarrier OFDM system, assuming that all DIDO antennas of the main cluster and vicinity cluster use the same set of null tones, the noise power P _N (10) is estimated from the null tones. As described above, the noise plus interference power (P _IN ) is estimated from the silence period. Finally, signal to noise plus interference power (P _S ) S) is derived from the data tones. From these estimates, the client calculates the SIR at (10).

Channel estimation in neighboring clusters 1102 to 1103: If the estimated SIR in (10) is smaller than a predetermined threshold (SIR _T ) determined at 8702 in FIG. Start listening. SIR _T It is noted that depending on the modulation and FEC encoding scheme used for data transmission (MCS). Different SIR targets are defined by the client's MCS. When DIDO distributed antennas in different clusters are time synchronized (ie, locked to the same pulse / second PPS time reference), the client delivers its channel estimate to DIDO antennas in neighboring clusters at 8703 Use training sequences. The training sequence for channel estimation in neighboring clusters is designed to be orthogonal to the training from the main cluster. Alternatively, a quadrature sequence (with good cross-correlation properties) is used for time synchronization in different DIDO clusters when DIDO antennas in different clusters are not time synchronized. Channel estimation is performed at 1103 with the client locked to the neighborhood cluster time / frequency reference.

  IDCI pre-encoding 1104: When channel estimates become available at DIDO BTSs in neighboring clusters, IDCI pre-encoding is calculated to satisfy condition (3). DIDO antennas in neighboring clusters transmit pre-encoded data streams only to clients in the cluster while pre-compensating for interference to clients in interference zone 410 of FIG. If the client is in the Type B interference zone 410 of FIG. 4, it will be appreciated that interference to the client is generated by multiple clusters, and IDCI precoding is performed by all neighboring clusters simultaneously.

Handoff methods From now on, different handoff methods for clients moving across DIDO clusters populated by distributed antennas located in different areas offering different types of services (ie low mobility services or high mobility services). explain.

a. Handoff between adjacent DIDO clusters In one embodiment, the IDCI-precoder that removes the intercluster interference described above is used as a baseline for handoff methods in DIDO systems. Traditional handoffs in cellular systems are considered towards clients that switch seamlessly across cells served by different base stations. In DIDO systems, handoff allows clients to move between clusters without losing connectivity.

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

  FIG. 12 shows SINR variation as a function of client distance from the center of cluster C1. Consider a target SINR = 20 dB for 4-QAM modulation without FEC coding. The line identified by the circle represents the SINR of the target client served by the DIDO antenna in C1 when both C1 and C2 use DIDO precoding without interference cancellation. SINR as a function of D decreases due to propagation loss and interference from neighboring clusters. When IDCI precoding is performed on neighboring clusters, the SINR reduction is only due to propagation loss (as shown in the line with triangles). This is because the interference is completely eliminated. Symmetric behavior is experienced when a client is served from a neighboring cluster. One embodiment of a handoff strategy is defined such that when the client moves from C1 to C2, the algorithm switches between different DIDO schemes that maintain SINR above a given target.

  From the plot of FIG. 12, the SER of the 4-QAM modulation of FIG. 13 is derived. By switching between different precoding strategies, it can be seen that the SER was maintained within a given target.

  One embodiment of a handoff strategy is as follows.

  C1-DIDO and C2-DIDO precoding: When the client is in C1 away from the interference zone, both clusters C1 and C2 operate independently with conventional DIDO precoding.

C1-DIDO and C2-IDCI precoding: SIR or SINR degrades when the client moves in the direction of the interference zone. When the target SINR _T1 is reached, the target client begins to estimate the channel from all DIDO antennas in C2 and supplies CSI to C2's BTS. The BTS in C2 computes IDCI precoding and sends it to all clients in C2 while preventing interference to the target client. As long as the target client is in the interference zone, it continues to supply CSI to both C1 and C2.

  C1-IDCI and C2-DIDO precoding: When the client moves in the C2 direction, the SIR or SINR continues to decrease until it reaches the target again. At this point, the client decides to switch to a neighboring cluster. In this case, C1 begins to use CSI from the target client to create zero interference in that direction with IDCI precoding, while neighboring clusters are CSI toward traditional DIDO precoding. Is used. In one embodiment, when the SIR estimate approaches the target, clusters C1 and C2 may alternatively use DIDO precoding and IDCI precoding to allow the client to estimate the SIR in both cases. Try both encoding schemes. The client then selects the best scheme that maximizes the predetermined error rate characteristic metric. When the method is applied, handoff strategy intersections occur at the intersections of curves having triangles and diamonds in FIG. One embodiment uses the modified IDCI pre-encoding method described in (6), and neighboring clusters also transmit the pre-encoded data stream to the target client so that array gain is obtained. This approach simplifies the handoff strategy because the client does not need to estimate the SINR for both strategies at the intersection.

C1-DIDO and C2-DIDO precoding: When a client leaves the interference zone in the direction of C2, the primary cluster C1 stops pre-cancelling the interference in the direction of its target client through IDCI precoding. Switch back to conventional DIDO pre-encoding for all clients remaining in C1. This final intersection in the handoff strategy is useful to avoid unnecessary CSI feedback from the target client to C1, thus reducing overhead on the feedback channel. In one embodiment, a second target SINR _T2 is defined. When the SINR (or SIR) exceeds this target, the strategy switches to C1-DIDO and C2-DIDO. In one embodiment, cluster C1 continues to alternate between DIDO precoding and IDCI precoding to allow clients to estimate SINR. The client then selects a method for C1 that is closer to the target SINR _T1 from above.

  The above method is used to calculate SINR or SIR estimates for different schemes in real time and to select the optimal scheme. In one embodiment, the handoff algorithm is designed based on the finite state machine shown in FIG. The client keeps track of the current state and switches to the next state when SINR or SIR is below or above the predetermined threshold shown in FIG. As described above, in state 1201, both clusters C1 and C2 operate independently with conventional DIDO precoding, the client is served by cluster C1, and in state 1202, the client is in cluster C1. And the BTS in C2 computes IDCI precoding, cluster C1 operates using conventional DIDO precoding, and in state 1203, the client is served by cluster C2. The BTS in C1 computes IDCI precoding, cluster C2 operates using conventional DIDO precoding, and in state 1204 the client is served by cluster C2 and cluster C1 And C2 are independent of conventional DIDO precodes In to operate.

  If there is a shadowing effect, the signal quality (SIR) may vary around the threshold as shown in FIG. 15, and repetitive switching between the continuous states of FIG. 14 occurs. Changing the state iteratively is an undesirable effect because it results in significant overhead for the control channel between the client and the BTS that allows switching between transmission schemes. FIG. 15 illustrates one embodiment of a handoff strategy when shadowing is present. In one embodiment, the shadowing factor is simulated according to a lognormal distribution with variance 3 [3]. In the following, we define several ways to prevent repetitive switching effects during DIDO handoff.

One embodiment of the present invention uses a hysteresis loop to address state switching effects. For example, when the switching between the state of "C1-DIDO, C2-IDCI" in FIG. 14 9302 as "C1-IDCI, C2-DIDO" 9303 (or versa vice versa), the range A ₁ threshold SINR _T1 Can be adjusted within. The method avoids repetitive switching between states when the signal quality varies around SINR _T1 . For example, FIG. 16 shows a hysteresis loop mechanism when switching between any two states of FIG. To switch to A from state B, SIR _{is, (SIR T1 + A 1/} 2) than is Nakere should increase, in order switched again from A to B is, SIR is, (SIR _T1 -A ₁ / Must fall below 2).

In different embodiments, the threshold SINR _T2 is adjusted in FIG. 14 to avoid repetitive switching between the first and second (or third and fourth) states of the finite state machine. For example, the range of the value A ₂ can be defined such that the threshold SINR _T2 is selected in that range based on channel conditions and shadowing effects.

In one embodiment, the SINR threshold is dynamically adjusted within the range [SINR _T2 , SINR _T2 + A ₂ ] based on the estimated shadowing variance across the radio link. As the client moves from its current cluster to a neighboring cluster, the variance of the lognormal distribution can be estimated from the variance of the received signal strength (or RSSI).

  The previous method assumes that the client triggers the handoff strategy. In one embodiment, handoff decisions are left to the DIDO BTS assuming communication across multiple BTSs is enabled.

  For the sake of brevity, the above method is derived assuming no FEC coding and 4-QAM. More generally, SINR or SIR thresholds are derived for different modulation and coding schemes (MCS), and the handoff strategy is to optimize the downlink data rate for each client in the interference zone. And in combination with link adaptation (see, for example, US Pat. No. 7,636,381).

b. Handoff between low and high Doppler DIDO networks The DIDO system uses a closed loop transmission scheme and pre-encodes the data stream across the downlink channel. The closed loop scheme is inherently suppressed by latency across the feedback channel. In a practical DIDO system, the computer time can be reduced by a transceiver with high processing power, and most of the waiting time is delivered from the BTS to CSI and baseband pre-coded data to the distributed antenna. It is estimated that it will be introduced by DIDO BSN. BSN is a variety of network technologies including but not limited to digital subscriber line (DSL), cable modem, fiber ring, T1 line, hybrid optical coax (HFC) network, and / or fixed radio (eg, WiFi). Can be configured. Dedicated fibers typically have very high bandwidth and low latency (potentially less than 1 millisecond in the local area), but are not as popular as DSL and cable modems. Today, DSL and cable modem connections are very popular, although the last mile latency typically exists between 10-25 ms in the United States.

  The maximum latency over the BSN determines the maximum Doppler shift that can be tolerated over the DIDO radio link without the performance degradation of DIDO precoding. For example, in [1], a network with a carrier frequency of 400 MHz and a latency of about 10 milliseconds (ie, DSL) can tolerate client speeds (running speeds) up to 3.58 m / s (8 mph). While networks with 1 millisecond latency (ie fiber ring) can support speeds up to 31.3 m / s (70 mph) (ie freeway traffic). Has been issued.

  Two or more DIDO sub-networks are defined based on the maximum Doppler shift allowed over the BSN. For example, a BSN with a high latency DSL connection between a DIDO BTS and a distributed antenna can only deliver low mobility or fixed wireless services (ie, low Doppler networks), while latency is low. A BSN with low latency over a short fiber ring can tolerate high mobility (ie, a high Doppler network). It is recognized that most broadband users are not moving when using broadband, and that most users are not likely to be near an area through which many high-speed objects pass (eg, next to a highway). It is done. This is because such a location is typically a less desirable location for living or running an office. However, there are broadband users who seem to be using broadband at high speed (eg in a car traveling on a highway) or near a high-speed object (eg in a store located near a highway) . To address these two different user Doppler scenarios, in one embodiment, a low Doppler DIDO network typically has a relatively low power spread over a wide area (ie, 1 W to 100 W, for indoor or rooftop deployments). While typically composed of a large number of DIDO antennas, a high Doppler network is typically composed of a small number of DIDO antennas with high power transmission (ie, 100 W, for rooftop or tower placement). Low Doppler DIDO networks typically serve many low Doppler users, typically using low cost, high latency broadband connections such as DSL and cable modems, typically at low connection costs. Can be provided. High Doppler DIDO networks typically serve a small number of high Doppler users and can do it typically at high connection costs using expensive low latency broadband connections such as fiber. .

  Different multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA) or code division multiple access (CDMA) to avoid interference across different types of DIDO networks (eg, low and high Doppler) Can be used.

  We propose a method for assigning clients to different types of DIDO networks and enabling handoffs between them. Network selection is based on the mobility type of each client. The client speed (v) is proportional to the maximum Doppler shift according to equation [6] below.

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

  In one embodiment, every client's Doppler shift is computed through a blind estimation technique. For example, the Doppler shift can be estimated by sending RF energy to the client and analyzing the reflected signal similar to a Doppler radar system.

  In another embodiment, one or more DIDO antennas send training signals to the client. Based on those training signals, the client estimates the Doppler shift using techniques such as counting the number of zero crossings of the channel gain or performing spectral analysis. It will be appreciated that for a constant velocity v and client trajectory, the angular velocity v sin θ in (11) may depend on the client's relative distance from any DIDO antenna. For example, a DIDO antenna near a moving client can provide a greater angular velocity and Doppler shift than a far antenna. In one embodiment, the Doppler velocity is estimated from multiple DIDO antennas at different distances from the client, and an average, weighted average or standard deviation is used as an indicator of client mobility. The DIDO BTS determines whether clients should be assigned to the low Doppler or high Doppler network based on the estimated Doppler indication.

  The Doppler indicator is periodically monitored for all clients and sent back to the BTS. When one or more clients change the Doppler speed (ie, a client on the bus vs. a walking or sitting client), they move to different DIDO networks that can tolerate a level of mobility. Will be reallocated.

  Although a slow client Doppler can be affected by being near a high speed object (eg, near a highway), the Doppler typically does not move a client Doppler that itself is moving. Far below. Thus, in one embodiment, the client's speed is estimated (eg, by using means such as monitoring the client position using GPS) and if the speed is low, the client is assigned to a low Doppler network. If the speed is high, the client is assigned to a high Doppler network.

Method of Power Control and Antenna Grouping A block diagram of a DIDO system with power control is shown in FIG. One or more data streams (s _k ) of every client (1... U) are multiplied by the weights generated by the DIDO precoding unit. The pre-encoded data stream is multiplied by a power scaling factor calculated by the power controller based on input channel quality information (CQI). The CQI is fed back from the client to the DIDO BTS or derived from the uplink channel assuming uplink-downlink channel correlation. Next, U pre-coded streams of different clients are combined and multiplexed into M data streams (t _m ), one for each of the M transmit antennas. Finally, the stream t _m is sent to a digital / analog converter (DAC) unit, a radio frequency (RF) unit, a power amplifier (PA) unit, and finally an antenna.

The power control unit measures CQI for all clients. In one embodiment, the CQI is average SNR or RSSI. CQI varies for different clients based on propagation loss or shadowing. The power control method adjusts the transmission power scaling factor P _k for different clients and multiplies by the pre-encoded data stream generated for different clients. Note that one or more data streams can be generated for any client based on the number of client receive antennas.

  In order to evaluate the performance of the proposed method, the following signal model is defined based on (5) including propagation loss and power control parameters.

Where k = 1. . . U, U is the number of clients, SNR = P _o / N _o , P _o is the average transmission power, N _o is the noise power, and α _k is the propagation loss / shadowing factor. To model propagation loss / shadowing, the following simplified model is used.

Where a = 4 is the propagation loss index, and it is assumed that the propagation loss increases with the client index (ie, the client is at a position where the distance from the DIDO antenna increases).

FIG. 18 shows the SER vs. SNR assuming 4 DIDO transmit antennas and 4 clients in different scenarios. In the ideal case, assume that all clients have the same propagation loss (ie, a = 0) and that P _k = 1 is obtained for all clients. The plot with squares refers to the case where the clients have different propagation loss factors and no power control. The curve with points is derived from the same scenario (with propagation loss) and the power control factor is chosen so that P _k = 1 / α _k . In the power control method, more power is allocated to the data stream that is for clients that experience higher propagation loss / shadowing, and therefore there is no power control (for this given scenario). An SNR gain of 9 dB is obtained as compared with FIG.

  The Federal Communications Commission (FCC) (and other international regulatory agencies) have defined constraints on the maximum power that can be transmitted from a wireless device to limit human exposure to electromagnetic (EM) radiation. There are two types of limits [2]: i) “occupation / control environment lower limit” that allows people to fully recognize radio frequency (RF) sources through fences, warnings, or labels; and ii) control over exposure. There is no “general public / unmanaged” limit.

  Different emission levels are defined for different types of wireless devices. In general, DIDO distributed antennas used for indoor / outdoor applications are [2], ie “usually used with radiating structures that are maintained 20 cm or more from the body of the user or nearby person. Relates to the FCC category of “mobile” devices, defined as “transmitting devices designed to be used outside fixed locations”.

The EM emission of a “mobile” device is measured in terms of maximum allowable exposure (MPE) expressed in mW / cm ² . FIG. 19 shows MPE power density as a function of distance from the RF radiation source for different values of transmit power at a carrier frequency of 700 MHz. Typically, the maximum allowable transmit power to meet the FCC “unmanaged” limit for devices that operate above 20 cm from the human body is 1 W.

  Less restrictive power emission constraints are defined for transmitters installed on the rooftop or building away from the “general public”. For these “rooftop transmitters”, the FCC defines a 1000 W looser emission limit measured in terms of effective radiated power (ERP).

Based on the above FCC constraints, in one embodiment, two types of DIDO distributed antennas are defined for a practical system.
Low power (LP) transmitter: 1W and 5 Mbps consumer grade wideband (eg, DSL, cable modem, fiber to the home (FTTH)) maximum transmit power, anywhere at any height (ie indoors, Or backhaul connectivity located outdoors.
High power (HP) transmitter: 100 W and commercial grade broadband (eg fiber optic ring) backhaul transmission power (effectively “unlimited” data rate compared to yield available over DIDO radio links) ) Rooftop or building-mounted antenna at a height of approximately 10 meters.

  LP clients with DSL or cable modem connectivity are good candidates for low Doppler DIDO networks (as described above in the previous section) because most of the clients are fixed or have low mobility. Please keep in mind. Commercially available HP transmitters with fiber connectivity can tolerate higher client mobility and can be used for high Doppler DIDO networks.

Consider a practical case of DIDO antenna placement in downtown Palo Alto, CA so that a practical intuition on the performance of a DIDO system with different types of LP / HP transmitters can be gained. Figure 20a shows a Palo Alto, random distribution of N _LP = 100 low power DIDO distributed antennas CA. In FIG. 20b, 50 LP antennas are replaced with N _HP = 50 high power transmitters.

  Based on the DIDO antenna distribution of FIGS. 20a-20b, a coverage map is derived in Palo Alto for systems using DIDO technology. FIGS. 21a and 21b show two power distributions corresponding to the configurations of FIGS. 20a and 20b, respectively. The power distribution (expressed in dBm) is derived assuming a propagation loss / shadowing model for an urban environment defined by the 3GPP standard [3] at a carrier frequency of 700 MHz. It can be seen that using 50% of the HP transmitter improves the coverage of the selected area.

  22a to 22b show the velocity distributions of the above two scenarios. Yields (expressed in Mbps) are derived based on power thresholds for different modulation and coding schemes defined in 3GPP Long Term Evolution (LTE) standards in [4, 5]. The bandwidth for fullness is fixed at 10 MHz with a carrier frequency of 700 MHz. Two different frequency allocation schemes are considered: i) 5 MHz spectrum allocated only to LP base stations, ii) 9 MHz for HP transmitters and 1 MHz for LP transmitters. Note that typically LP fields are allocated lower bandwidth due to DSL backhaul connectivity with limited yield. FIGS. 22a-22b can significantly increase the speed distribution by increasing the average data transfer rate per client from 2.4 Mbps in FIG. 22a to 38 Mbps in FIG. 22b when using 50% of the HP transmitter. It shows what you can do.

  Next, an algorithm was defined that controls the power transmission of the LP base station such that higher power is possible at any given time, thus increasing yield over the downlink channel of the DIDO system of FIG. 22b. It will be appreciated that the FCC limit for power density is defined as [2] based on an average over time.

Where

Is the MPE averaging time and t _n is the exposure period to radiation at power density S _n .
For “managed” exposures, the average time is 6 minutes, while for “unmanaged” exposures it is increased to 30 minutes. Any power supply is then allowed to transmit at a power level that is greater than the MPE limit as long as the average power density in (14) meets the FCC limit for the 30-minute average value of “unmanaged” exposure.

  Based on this analysis, an adaptive power control method is defined that increases the instantaneous transmit power per antenna while maintaining the average power per DIDO antenna below the MPE limit. Consider a DIDO system with more transmit antennas than active clients. This takes into account that the DIDO antenna can be thought of as an inexpensive wireless device (similar to a WiFi access point) and can be located anywhere with DSL, cable modem, fiber optic or other internet connectivity This is an appropriate assumption.

A DIDO system framework with power control per adaptive antenna is shown in FIG. The amplitude of the digital signal appearing from the multiplexer 234 is sent to the power scaling factor S ₁ . . . Dynamically adjusted with S _M. The power scaling factor is calculated by the power control unit 232 based on the CQI 233.

In one embodiment, N _g DIDO antenna groups are defined. Every group includes at least as many DIDO antennas as the number of active clients (K). Only one group can have MPE limits at any given time

With N _a > K active DIDO antennas transmitting to the client at a higher power level (S _o ). One method is repeated across all antenna groups according to the round robin scheduling policy shown in FIG. In another embodiment, different scheduling methods (ie proportional fair scheduling [8]) are used for cluster selection to optimize error rate or yield performance.

  Assuming round robin power allocation, from (14) we derive the average transmit power for all DIDO antennas as follows:

_Where t _o is the period during which the antenna group is active, and T _MPE = 30 min is the average time defined by the FCC guidelines [2]. The ratio of (15) indicates that the average transmission power from any DIDO antenna is the MPE limit

The load time ratio (DF) of the group defined to satisfy The load time ratio depends on the number of active clients, the number of groups, and the number of active antennas per group according to the following definition.

  The SNR gain (in dB) obtained in a DIDO system with power control and antenna grouping is expressed as a function of load time ratio as follows:

It will be appreciated that the gain of (17) is achieved at the expense of yet another transmit power of G _dB across all DIDO antennas.

Generally, the total transmit power from all N _a of all N _g groups are defined as follows.

_Where P _ij is the average transmit power per antenna given by:

S _ij (t) is the power spectral density of the i th transmit antenna in the j th group. In one embodiment, the power spectral density of (19) is designed such that every antenna optimizes error rate or yield performance.

Consider 400 DIDO distributed antennas in a given coverage area and 400 clients that register with the wireless Internet service provided by the DIDO system so that some intuition regarding the performance of the proposed method is obtained. It is unlikely that all Internet connections will be used all the time. Assume that 10% of clients actively use a wireless Internet connection at any given time. Next, the 400 DIDO antennas can be divided into N _g = 10 groups of N _a = 40 antennas each, each group being K at any given time with a load time ratio DF = 0.1. = Serving 40 active clients. The SNR gain resulting from this transmission scheme is G _dB = ₁₀ log ₁₀ (1 / DF) = 10 _dB supplied by an additional transmit power of 10 dB from all DIDO antennas. However, it can be seen that the average transmit power per antenna is constant and within the MPE limits.

  FIG. 25 compares the (uncoded) SER performance of the above power control with antenna grouping with the conventional eigenmode selection of US Pat. No. 7,636,381. All schemes use BD precoding with 4 clients, each equipped with a single antenna. SNR refers to the ratio of power per transmit antenna to noise power (ie, transmit SNR per antenna). The curve shown in DIDO 4x4 assumes 4 transmit antennas and BD precoding. The curve with squares shows the SER performance with two extra transmit antennas and BD with eigenmode selection, yielding 10 dB SNR gain (with 1% SER target) over traditional BD precoding. . Similarly, a gain of 10 dB can be obtained with the same SER by antenna grouping and power control with DF = 1/10. It can be seen that eigenmode selection changes the slope of the SER curve due to diversity gain, while this power control method shifts the SER curve to the left (while maintaining the same slope) due to an increase in average transmit power. . For comparison, it is found that for a SER at a larger load time ratio DF = 1/50, another 7 dB gain is obtained compared to DF = 1/10.

  Note that our power control may be less complex than conventional eigenmode selection methods. In practice, every group of antenna IDs is pre-calculated and shared between DIDO antennas and clients through a lookup table so that only K channel estimates are needed at any given time. Can do. For eigenmode selection, (K + 2) channel estimates are calculated, requiring further computer processing to select the eigenmode that minimizes the SER at any given time for all clients. .

  Next, another method with DIDO antenna grouping is described to reduce CSI feedback overhead in some special scenarios. FIG. 26a shows one scenario where a client (dot) is randomly spread in one area that is the target of multiple DIDO distributed antennas (crosshairs). The average power across all transmit-receive radio links can be calculated as follows:

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

  The matrix A of FIGS. 26a-26c is obtained numerically by averaging the channel matrix over 1000 cases. Two alternative scenarios are shown in FIGS. 26b and 26c, respectively, where the clients are grouped together around a subset of the DIDO antennas and receive insignificant power from a remotely located DIDO antenna. For example, FIG. 26b shows two groups of antennas from which a block diagonal matrix A is obtained. One extreme scenario is when every client is very close to only one transmitter and the transmitters are far away from each other so that power from all other DIDO antennas is negligible. Is. In this case, the DIDO link degrades in multiple SISO links, and A is a diagonal matrix as in FIG. 26c.

In all three scenarios above, BD precoding dynamically adjusts the precoding weights to adapt to different power levels across the radio link between the DIDO antenna and the client. However, it is advantageous to identify multiple groups in the DIDO cluster and operate DIDO precoding only within each group. This proposed grouping method has the following advantages.
• Computational gain: DIDO precoding is computed only within every group in the cluster. For example, when BD precoding is used, singular value decomposition (SVD) has complexity O (n ³ ), where n is the smallest dimension of the channel matrix H. If H can be reduced to a block diagonal matrix, the SVD is computed for every block with reduced complexity. When the channel matrix is divided into two block matrices of dimensions n ₁ and n ₂ such that n = n ₁ + n ₂ , the complexity of SVD is O (n ₁ ³ ) + O (n ₂ ³ ) <O It is only (n ³ ). In the extreme case, when H is a diagonal matrix, the DIDO link is reduced to multiple SISO links and no SVD calculation is required.
Reduced CSI feedback overhead: When DIDO antennas and clients are divided into groups in one embodiment, CSI is calculated from clients to antennas only within the same group. The TDD system reduces the number of channel estimates for which the channel matrix H should be calculated by antenna grouping by assuming channel correlation. In FDD systems where CSI is fed back over the radio link, antenna grouping further provides a reduction in CSI feedback overhead across the radio link between the DIDO antenna and the client.

Multiple Access Technologies for DIDO Uplink Channel In one embodiment of the present invention, different multiple access technologies are defined for DIDO uplink channels. These techniques can be used to feed back CSI or send a data stream from the client to the DIDO antenna on the uplink. Hereinafter, the feedback CSI and the data stream are referred to as an uplink stream.
Multiple-input multiple-output (MIMO): The uplink stream is transmitted from the client to the DIDO antenna through an open-loop MIMO multiplexing scheme. The method assumes that all clients are time / frequency synchronized. In one embodiment, synchronization between clients is achieved through training from the downlink and it is assumed that all DIDO antennas are locked to the same time / frequency reference clock. Note that variations in delay spread at different clients can cause jitter between the clocks of different clients that can affect the performance of the MIMO uplink scheme. After the client sends an uplink stream through a MIMO multiplexing scheme, the receiving DIDO antenna can be non-linear (ie, maximum likelihood, ML) or linear to remove co-channel interference and demodulate the uplink stream individually. A receiver can be used (ie, zero forcing, minimum mean square error).
Time division multiple access (TDMA): Different clients are assigned to different time slots. Every client sends an uplink stream when a time slot is available.
Frequency division multiple access (FDMA): Different clients are assigned to different carrier frequencies. In a multi-carrier (OFDM) system, a subset of tones is assigned to different clients transmitting uplink streams at the same time, thus reducing latency.
Code Division Multiple Access (CDMA): Every client is assigned a different pseudo-random number, and orthogonality across clients is achieved in the code domain.

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

  In another embodiment, the uplink multiple access technique described above is used in combination with the antenna grouping method shown in the previous section to define different clients within a DIDO cluster.

Link Adaptation System and Method in DIDO Multi-Carrier System A link adaptation method for a DIDO system that utilizes the time, frequency, and spatial selectivity of the radio channel is defined in US Pat. No. 7,636,381. An embodiment of the present invention of link adaptation in a multi-carrier (OFDM) DIDO system that utilizes time / frequency selectivity of a radio channel is described below.

  In [9], a Rayleigh fading channel simulation is provided according to the exponentially decaying power delay profile (PDP) or Saleh-Valenzuela model. For simplicity, assume a single cluster channel with a multipath PDP defined as follows:

Where n = 0. . . (L-1) is an index of channel taps, L is the number of channel taps, and β = 1 / σ _DS is an index of the channel interference zone bandwidth that is inversely proportional to the channel delay spread (σ _DS ). It is a certain PDP index. A low value of β results in a frequency non-selective channel, while a high value of β results in a frequency selective channel. The PDP of (21) is normalized so that the total average power for all L channel taps is single.

  FIG. 27 shows the amplitude of the low frequency selection channel (assuming β = 1) over the delay domain or instantaneous PDP (upper plot) and frequency domain (lower plot) for the DIDO 2 × 2 system. The first subscript indicates the client, and the second subscript indicates the transmit antenna. The high frequency selection channel (with β = 0.1) is shown in FIG.

  Next, we study the performance of DIDO precoding in frequency selective channels. Assuming that the signal model in (1) satisfies the condition of (2), the DIDO pre-encoding weight through BD is calculated. Under the conditions of (2), the DIDO received signal model of (5) is reformulated as follows.

_Where H _ek = H _k W _k is the effective channel matrix for user k. For DIDO 2 × 2, with one antenna per client, the effective channel matrix is for channels characterized by the high frequency selectivity (eg, β = 0.1) of FIGS. Reduce to one value with the frequency response shown. The solid line in FIG. 29 indicates the client 1, while the line having a point indicates the client 2. Based on the channel quality metric of FIG. 29, a time / frequency domain link adaptation (LA) method that dynamically adjusts the MCS according to the channel condition change condition is defined.

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

  FIG. 30 shows the SER for different QAM schemes (ie, 4-QAM, 16-QAM, 64-QAM). Without loss of generality, assume a 1% target SER for an uncoded system. The SNR thresholds that should meet its target SER in the AWGN channel are 8 dB, 15.5 dB, and 22 dB, respectively, for the three modulation schemes. In a Rayleigh fading channel, the SER performance of the above modulation scheme is known to be worse than AWGN [13], and the SNR thresholds are 18.6 dB, 27.3 dB, and 34.1 dB, respectively. . It will be appreciated that DIDO precoding converts a multi-user downlink channel into a set of parallel SISO links. Therefore, the same SNR threshold as in FIG. 30 for the SISO system is applied to the DIDO system on a per client basis. Furthermore, the threshold in the AWGN channel is used when instantaneous LA is performed.

  An important idea of the proposed LA method for DIDO systems is to use a lower MCS order when the channel is subjected to a time-domain or frequency-domain deep fade (shown in Figure 28) so that link robustness is obtained. is there. On the other hand, when the channel is characterized by a large gain, the LA method switches to a higher MCS order to increase spectral efficiency. One contribution of this application compared to US Pat. No. 7,636,381 is to use the effective channel matrix in (23) and FIG. 29 as a metric that allows adaptation.

A general framework of the LA method is shown in FIG. 31 and is defined as follows.
CSI estimation: At 3171, DIDO BTS calculates CSI from all users. A user can be equipped with a single or multiple receive antennas.
DIDO precoding: At 3172, the BTS calculates DIDO precoding weights for all users. In one embodiment, the BD is used to calculate these weights. The pre-encoding weight is calculated for each tone.
Link quality metric calculation: At 3173, the BTS calculates the frequency domain link quality metric. In an OFDM system, the metric is calculated from CSI and DIDO precoding weights are calculated for every tone. In one embodiment of the invention, the link quality metric is the average SNR across all OFDM tones. We define the method as LA1 (based on average SNR performance). In another embodiment, the link quality metric is the frequency response of the effective channel of (23). We define this method as LA2 (to take advantage of frequency diversity based on per-tone performance). If every client has a single antenna, the frequency domain effective channel is shown in FIG. If the client has multiple 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 singular values of the effective channel matrix of (23).
Bit loading algorithm: At 3174, based on the link quality metric, the BTS determines the MCS for different clients and different OFDM tones. In the LA1 method, the same MCS is used for all clients and all OFDM tones based on the SNR threshold of the Rayleigh fading channel of FIG. In LA2, different MCSs are assigned to different OFDM tones to take advantage of channel frequency diversity.
Pre-encoded data transmission: At 3175, the BTS uses the MCS derived from the bit loading algorithm to transmit the pre-encoded data stream from the DIDO distributed antenna to the client. One header is attached to the pre-encoded data to tell the client the MCS for different tones. For example, if 8 MCSs are available and the OFDM code is defined with N = 64 tones, log ₂ (8) ^* N = 192 bits will tell any client the current MCS. Needed. Assuming 4-QAM (2 bits / code spectral efficiency) is used to map those bits to the code, only the 192/2 / N = 1.5 OFDM code is used to map the MCS information. Needed. In another embodiment, multiple subcarriers (or OFDM tones) are grouped into subbands and the same MCS is assigned to all tones in the same subband to reduce overhead due to control information. Further, the MCS is adjusted based on the temporal variation of the channel gain (proportional to the coherence time). In a fixed radio channel (characterized by low Doppler effect), the MCS is recalculated for each small portion of the channel coherence time, thus reducing the overhead required for control information.

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

Systems and methods of DIDO pre-coded interpolation in multi-carrier systems The computational complexity of DIDO systems is mostly localized to centralized processors or BTSs. The most computationally expensive task is the calculation of precoding weights for all clients from the CSI. When BD precoding is used, the BTS needs to perform as many singular value decomposition (SVD) operations as there are clients in the system. A way to reduce complexity is by parallel processing where the SVD is computed on a separate processor for every client.

In a multi-carrier DIDO system, each subcarrier undergoes flat fading and SVD is performed for every client across every subcarrier. The complexity of the system obviously increases linearly with the number of subcarriers. For example, in an OFDM system with 1 MHz signal bandwidth, the cyclic prefix (L ₀ ) is at least 8 channel taps (ie, to avoid intersymbol interference in an outdoor urban macrocell environment with large delay spread [3]. , 8 microsecond duration). The Fast Fourier Transform (FFT) size (N _FFT ) used to generate the OFDM code is typically set to a multiple of L ₀ to reduce data rate loss. When N _FFT = 64, the effective spectral efficiency of the system is limited by the factor N _FFT / (N _FFT + L ₀ ) = 89%. Increasing the value of N _FFT results in higher spectral efficiency at the cost of higher computational complexity in the DIDO precoder.

A way to reduce computational complexity in a DIDO precoder is to perform SVD work over a subset of tones (called pilot tones) and derive precoding weights for the remaining tones through interpolation. is there. Weight interpolation is one error factor that results in inter-client interference. In one embodiment, optimal weight interpolation is used to reduce inter-client interference, improving error rate performance and reducing computational complexity in multi-carrier systems. In a DIDO system with M transmit antennas, U clients, and N receive antennas per client, the precoding weight of the k th client (W _k ) that guarantees zero interference to other clients u. The condition is derived from (2) as follows.

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

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

Where θ _k is a set of parameters that should be optimized for user k,

Is a weight interpolation matrix,
Indicates the Frobenius norm of the matrix. The optimization problem is formulated as follows:

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

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

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

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

Where 0 ≦ l ≦ (LO−1), L ₀ is the number of pilot tones, c _n = (n−1) / N ₀ , and N ₀ = N _FFT / L ₀ . The weight matrix of (28) is then used to guarantee a single power transmission from every antenna

Normalized to be If N = 1 (single receive antenna per client), the matrix of (28) is a vector that is normalized with respect to its norm. In one embodiment of the invention, the pilot tones are selected uniformly over the range of OFDM tones. In another embodiment, pilot tones are adaptively selected based on CSI to minimize interpolation errors.

  The present inventors have recognized that the important difference between the system and method of [11] with respect to the system and method proposed in this patent application is the objective function. Specifically, the system of [11] assumes multiple transmit antennas and a single client, so the related method is to pre-encode weight by channel to maximize the received SNR for the client. Designed to maximize the product of However, this method does not work in multi-client scenarios due to inter-client interference due to interpolation errors. In contrast, the method of the present application is designed to minimize inter-client interference, thus improving error rate performance for all clients.

FIG. 33 shows the matrix input in equation (28) as a function of OFDM tone index for a DIDO 2 × 2 system with N _FFT = 64 and L ₀ = 8. The channel PDP is generated according to the model (21) with β = 1, and the channel is composed of only eight channel taps. It will be appreciated that L ₀ should be selected to be greater than the number of channel taps. The solid line in FIG. 33 represents an ideal function. On the other hand, the dotted line is an interpolated function. The interpolated weight matches the ideal weight of the pilot tone according to the definition in (28). The weights calculated over the remaining tones approximate only in the ideal case due to estimation errors.

One way to perform weight interpolation is through an exhaustive search over the feasible set θ _k of (26). In order to reduce the complexity of the search, the feasible set is quantized to P values uniformly in the range [0, 2π]. FIG. 34 shows the SNR vs. SER for the transmit antenna and the variable P for L ₀ = 8 and M = N _t = 2. As the number of quantization levels increases, the SER performance improves. It can be seen that case P = 10 approaches the performance of P = 100 because the computational complexity is much less due to the reduced number of searches.

FIG. 35 shows the SER performance of the interpolation method with different DIDO orders and L ₀ = 16. Assume that the number of clients is the same as the number of transmit antennas and that every client is equipped with a single antenna. As the number of clients increases, the SER performance decreases due to the increased inter-client interference generated by weight interpolation errors.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  [18] R.M. J. et al. Masak, “Beamforming / null-steering adaptive array”, US Patent N. 4,771,289, Sep. 1988.

  [20] K.K. -B. Yu, et. al. , "Adaptive digital beamforming architecture and algorithm for mulling, mainlobe and multiple sirable, pre-raveled jammers 1997.

  [21] H.I. Boche, et al. , "Analysis of differential precoding / decoding strategies for multi-user beamforming", IEEE Vehic. Tech. Conf. , Vol. 1, Apr. 2003.

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

  [23] H.I. Boche, et al. “A general duty theory for uplink and downlink beamformingc”, vol. 1, pp. 87-91, Dec. 2002.

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

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

II. Disclosure from Related Application No. 12 / 917,257 Radio High Frequency Using Multiple Distributed Transmit Antennas Working Together to Create a Radio Link with a Predetermined User While Suppressing Interference with Other Users The (RF) communication system and method are described below. Coordination across different transmit antennas is possible through user clustering. A user cluster is a subset of transmit antennas whose signals can be reliably detected by a given user (ie, the received signal strength is greater than the noise or interference level). Every user in the system defines his own user cluster. Waveforms sent by transmit antennas in the same user cluster create RF energy at the target user's location and coherent to create zero RF interference points at any other user's location reachable by those antennas. Combined with

  Consider a system with one user cluster reachable by those M antennas with K ≦ M and M transmit antennas in K users. The transmitter is between M transmit antennas and K users.

Is recognized. For simplicity, it is assumed that every user is equipped with a single antenna, but the same method can be extended to multiple receive antennas per user. Channel vector from M transmit antennas to K users

The channel matrix H obtained by combining

Think of it as

  Pre-encoding weight to create RF energy for user k, zero RF energy for all other K-1 users

Is calculated to satisfy the following condition.

Where

Is the effective channel matrix for user k obtained by removing the k th column of matrix H, where 0 ^{k × 1} is a vector with all zero inputs.

In one embodiment, the wireless system is a DIDO system and user clustering is a wireless communication link with the target user while pre-compensating for interference to any other user that is reachable by an antenna in the user cluster. Used to create In US patent application Ser. No. 12 / 630,627, a DIDO system is described that includes:
DIDO client: A user terminal equipped with one or more antennas.
DIDO distributed antenna: A transceiver base station that works in concert to transmit a pre-encoded data stream to multiple users, thus suppressing inter-user interference.
DIDO Base Transceiver Station (BTS): A centralized processor that generates pre-coded waveforms on DIDO distributed antennas.
DIDO Base Station Network (BSN): A wired backhaul that connects the BTS to a DIDO distributed antenna or other BTS.
DIDO distributed antennas are grouped into different subsets depending on the spatial distribution to the location of the BTS or DIDO client. As shown in FIG. 36, three types of clusters are defined.
Supercluster 3640: A set of DIDO distributed antennas connected to one or more BTSs so that round trip latency between all BTSs and their respective users is within the constraints of the DIDO precoding loop .
DIDO cluster 3641: a set of DIDO distributed antennas connected to the same BTS. When a super cluster contains only one BTS, its definition is consistent with a DIDO cluster.
User cluster 3642: a set of DIDO distributed antennas that cooperate to transmit pre-encoded data to a given user.

  For example, a BTS is a local hub connected to other BTS and DIDO distributed antennas through a BSN. BSNs include, but are not limited to, digital subscriber lines (DSL), ADSL, VDSL [6], cable modems, fiber rings, T1 lines, hybrid optical coaxial (HFC) networks, and / or fixed radio (eg, WiFi). It can be configured by various network technologies that are not limited. All BTSs in the same supercluster share information about DIDO precoding through the BSN so that round trip latency is in the DIDO precoding loop.

  In FIG. 37, dots indicate DIDO distributed antennas, cross symbols indicate users, and broken lines indicate user clusters of users U1 and U8, respectively. The method described below is designed to create a communication link with the target user U1 while creating a zero RF energy point for any other user (U2-U8) inside or outside the user cluster. The

  We have proposed in [5] a similar method created to eliminate the interference of zones where zero RF energy points overlap between DIDO clusters. An additional antenna was required to transmit signals to clients in the DIDO cluster while suppressing intercluster interference. In the embodiment of the method proposed in this application, it does not attempt to remove the DIDO intercluster interference, but rather the cluster is associated with the client (ie, user cluster) and the interference is any other client within its neighbors. Assume that it is guaranteed that no interference is generated (or interference is negligible).

  One idea related to the proposed method is that due to the large propagation loss, users far enough from the user cluster are not affected by the radiation from the transmit antenna. Users close to or within the user cluster receive a signal free of interference for precoding. Furthermore, additional transmit antennas can be added to the user cluster (as shown in FIG. 37) so that the condition K ≦ M is satisfied.

One embodiment of a method using user clustering consists of the following steps:
a. Link quality measurement: Report the link quality between every DIDO distributed antenna and every user to the BTS. The link quality metric consists of a signal to noise ratio (SNR) or a signal to interference noise ratio (SINR).

  In one embodiment, the DIDO distributed antenna transmits a training signal and the user estimates the received signal quality based on the training. The training signal is designed to be orthogonal in the time domain, frequency domain, or code domain so that the user can differentiate across different transmitters. Alternatively, the DIDO antenna transmits a narrowband signal (ie a single tone) on one predetermined frequency (ie beacon channel) and the user estimates the link quality based on the beacon signal. One threshold is defined as the minimum signal amplitude (or power) greater than the noise level that normally demodulates the data as shown in FIG. 38a. Any link quality metric value less than 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 different embodiments, a training signal or beacon is sent from the user and the link quality assumes an interrelationship between uplink (UL) and downlink (DL) propagation losses (FIG. 38b). As is the case with the DIDO transmit antenna. Propagation loss interrelationships include time division duplex (TDD) and frequency division duplex (FDD) systems (with UL and DL channels at the same frequency) when the UL and DL frequency bands are relatively close. It should be noted that this is a realistic assumption.

  Information about link quality metrics is shared across different BTSs through the BSN so that all BTSs are aware of every antenna / user link quality across different DIDO clusters, as shown in FIG.

  b. User Cluster Definition: The link quality metric for all radio links in the DIDO cluster is an input to the link quality matrix shared across all BTSs through the BSN. An example of the link quality matrix for the scenario of FIG. 37 is shown in FIG.

The link quality matrix is used to define user clusters. For example, FIG. 39 illustrates selection of a user cluster for user U8. A subset of transmitters with a non-zero link quality metric (ie active transmitter) for user U8 is first identified. These transmitters populate user U8's user cluster.
Next, a submatrix containing non-zero inputs from transmitters in the user cluster to other users is selected. Since the link quality metric is only used to select user clusters, it can be quantized with only 2 bits (ie, to identify states above or below the threshold in FIG. 38), and therefore The feedback overhead is reduced.

  Another example is shown in FIG. 40 for user U1. In this case, the number of active transmitters is lower than the number of users in the submatrix, and therefore violates the condition K ≦ M. Thus, one or more columns are added to the submatrix to satisfy the condition. If the number of transmitters is greater than the number of users, additional antennas can be used for diversity schemes (ie, antenna or eigenmode selection).

  Yet another example is shown in FIG. 41 for user U4. It will be appreciated that the submatrix can be obtained as a combination of two submatrixes.

  c. CSI reporting to BTS: When a user cluster is selected, CSI from all transmitters in the user cluster to any users that those transmitters reach is available to all BTSs. CSI information is shared across all BTSs through the BSN. The TDD system can utilize UL / DL channel correlation and derive CSI from training on the UL channel. The FDD system requires a feedback channel from all users to the BTS. To reduce the amount of feedback, only the CSI corresponding to the non-zero input of the link quality matrix is fed back.

  d. DIDO precoding: Finally, DIDO precoding is applied to any CSI that corresponds to a different user (eg, as described in the related US patent application).

  In one embodiment, the effective channel matrix

The singular value decomposition (SVD) of the user k and the precoding weight w _k of the user _k is

Is defined as the right singular vector corresponding to the null subspace. Alternatively, M> K and SVD is

If we decompose the effective channel matrix as follows, the DIDO pre-encoding weight for user k is given by

_Where U _o is

Is a matrix having columns that are singular vectors of the null subspace.

  From basic linear algebra considerations, the matrix

It can be seen that the right singular vector in the null subspace is equal to the C eigenvector corresponding to the zero eigenvalue.

Where the effective channel matrix is according to SVD:

As disassembled. next,

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

Where the vector (u _i ) in the first iteration is a random vector.

  Considering that the eigenvalue (λ) of the null subspace is known (ie, zero), the inverse power method requires only one iteration to converge, thus reducing the computational complexity. Next, the pre-encoding weight vector is written as follows:

Where u _i is a vector with a true input equal to 1 (ie, the precoding weight vector is the sum of the columns of C ⁻¹ ).

  The matrix inversion required for DIDO precoding calculation is one time. There are several numerical solutions that reduce the complexity of matrix inversion, such as Strassen's algorithm [1] or Coppersmith-Winograd's algorithm [2, 3]. Since C is essentially a Hermitian matrix, an alternative solution is to compute the matrix inversion of the real matrix by decomposing C in the real and imaginary parts according to the method of [4, Section 11.4].

  Another feature of the proposed method and system is reconfigurability. As the client crosses different DIDO clusters in FIG. 42, the user cluster follows its movement. In other words, the subset of transmit antennas is constantly updated as the client changes position, and the effective channel matrix (and corresponding precoding weights) are recalculated.

  The method proposed here works within the supercluster in FIG. 36 because the link between BTSs through the BSN must have low latency. To suppress the interference of overlapping zones of different superclusters, the method of [5] using an additional antenna to create a zero RF energy point in the interference zone between DIDO clusters can be used.

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

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

  [2] D. Coppersmith and S.C. Winograd, “Matrix Multiplexing via Arithmetic Progression”, J. Am. Symb. Comp. vol. 9, p. 251-280, 1990.

  [3] H. Cohn, R.A. Kleinberg, B.M. Szegedy, C.I. Umans, “Group-theoretics for Matrix Multiplication”, p. 379-388, Nov. 2005.

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

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

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

III. Systems and Methods Utilizing Coherent Areas in a Wireless System The capacity of a multiple antenna system (NAS) in a practical propagation environment is a function of the spatial diversity available across the wireless link. Spatial diversity is determined by the dispersion of scattered objects in the radio channel and the shape of the transmit and receive antenna arrays.

  One common model for the MAS channel is called the so-called clustered channel model, which defines a group of scattered objects as clusters placed around the transmitter and receiver. In general, the more clusters exist and the more widely these angles spread, the higher the spatial diversity and capacity available over the wireless link. Clustered channel models have been identified by practical measurements [1-2], and variations in these models are based on indoor wireless standards (ie, IEEE 802.11n Technical Group [3] for WLAN) and outdoor wireless. It is introduced in the standard (3GPP Technical Specification Groups [4] for 3G seller systems).

  Other factors that determine spatial diversity in the radio channel include antenna element spacing [5-7], number of antennas [8-9], array apertures [10-11], array structures [5, 12, 13], Features of the antenna array, including polarization and antenna pattern [14-28].

  An integrated model describing the effects of antenna array design as well as the characteristics of the propagation channel in the radio link spatial diversity (or degrees of freedom) is shown in [29]. The received signal model of [29] is shown by:

Where

Is the polarization vector describing the transmitted signal,

Are the positions of the polarization vectors describing the transmit and receive arrays respectively.

Is a matrix describing the system response between the position of the transmit vector and the position of the receive vector as indicated by

Where

Are the transmit array response and the receive array response, respectively.

Is the direction in which the entry is sent

And receiving direction

A channel response matrix that is a complex gain between and. In a DIDO system, a user device may have one or more antennas. For simplicity, assume a single antenna receiver with an ideal isotropic pattern and rewrite the system response matrix as follows:

Where transmit antenna pattern

Only considered.

  From Maxwell's equation and the far-field term of the Green function, the array response can be approximated by [29].

Where P is the space defining the antenna array, where

And

It is. For an unpolarized antenna, studying the array response is equivalent to studying the integration kernel described above. In the following, the closed-form equations of the integral kernels of different types of arrays are presented.

Unpolarized linear array For a non-polarized linear array of length L (normalized by wavelength) and antenna elements oriented along the z-axis and centered at the origin, the integration kernel is denoted by [29] It is.

Extending the above equation to a series of moving dyads, the sinc function has a resolution of 1 / L, and the dimensions (ie degrees of freedom) of the array limited and nearly wave vector limited subspaces are: Obtained.
DF = L | Ω _θ |
In the formula, Ω _θ = {cos θ; θεΘ}. Horizontal array _| Ω θ | = | Θ | a and, on the other hand, end-fire is | Omega _theta | admission that it is ^2/2 | ≒ | ^Θ.

Unpolarized spherical array The integrating kernel of a spherical array of radius R (normalized by wavelength) is denoted by [29].

The above function was decomposed by the sum of the spherical Bessel functions of the first type, and the resolution of the spherical array was 1 / (πR ² ), and the degree of freedom was obtained as follows.
D _F = A | Ω | = πR ² | Ω |

  Where A is the area of the spherical array,

It is.

Coherent area in radio channel FIG. 43 shows the relationship between the resolution of the spherical array and the area A thereof. The central sphere is a spherical array of area A. Projection of channel clusters onto the unit sphere defines scattering regions of different sizes that are proportional to the angular spread of the clusters. An area of size 1 / A within each cluster (referred to as the “coherent area”) represents the projection of the basis function of the array's radiation field and defines the resolution of the array in the wave vector domain.

Comparing FIG. 43 with FIG. 44, it can be seen that the size of the coherent area decreases in inverse proportion to the size of the array. In fact, a larger array can concentrate energy in a smaller area, resulting in a greater number of degrees of freedom _DF . Note that the total number of degrees of freedom also depends on the angular spread of the clusters, as shown in the definition above.

FIG. 45 shows another example in which the array size occupies an area larger than that in FIG. 44 and further freedom is obtained. In a DIDO system, the array aperture can be approximated by the total area occupied by all DIDO transmitters (assuming the antennas are separated by a few wavelengths).
As a result, FIG. 45 shows that the DIDO system can increase the degree of freedom by distributing the antennas in space, thus reducing the size of the coherent area. Note that these figures are generated assuming an ideal spherical array. In practical scenarios, DIDO antennas are randomly distributed over a wide area, and the resulting coherent area shape may not be regular as shown in these figures.

  FIG. 46 shows that as the array size increases, the number of objects between DIDO transmitters increases so that radio waves scatter and thus more clusters are included in the radio channel. It is therefore possible to increase the number of basis functions (over the radiation field) and obtain further degrees of freedom according to the above definition.

  The multi-user (MU) multi-antenna system (MAS) described in this patent application utilizes a coherent area of a radio channel to create multiple independent simultaneous non-interfering data streams for different users. For a given channel condition and user distribution, a radial field basis function is selected to create independent simultaneous radio links for different users in such a way that every user experiences a link without interference. Since MU-MAS recognizes the channel between every transmitter and every user, the precoded transmission is adjusted based on this information to create separate coherent areas for different users.

  In one embodiment of the present invention, the MU-MAS utilizes non-linear precoding such as Dirty Paper Coding (DPC) [30-31] or Tomlinson-Harashima (TH) [32-33] precoding. In another embodiment of the present invention, the MU-MAS may be block diagonalization (BD) [0003-0009] or zero forcing beamforming (ZF-BF) as in our previous patent application. 34] or the like is used.

  In order to enable precoding, the MU-MAS needs to know channel state information (CSI). CSI is estimated on the uplink channel assuming it is available in MU-MAS via a feedback channel or uplink / downlink channel correlation is possible in a time division duplex (TDD) system. The One way to reduce the amount of feedback required for CSI is to use the limited feedback method [35-37]. In one embodiment, the MU-MAS uses a limited feedback method to reduce the CSI overhead of the control channel. In the limited feedback method, codebook design is important. One embodiment defines a codebook from basis functions over the radiation field of the transmit array.

  As the user moves through space or the propagation environment changes over time due to a moving object (such as a person or a car), the coherent area changes its location and shape. This is due to the Doppler effect in known wireless communication. The MU-MAS described in this patent application adjusts the pre-coding to constantly adapt the coherence area for every user as the environment changes due to the Doppler effect. This coherent area adaptation is for creating simultaneous non-interfering channels for different users.

  Another embodiment of the invention adaptively selects a subset of antennas in the MU-MAS system to create different sized coherence areas. For example, if users are sparsely distributed in space (ie, rural areas or times when radio resource usage is low), a small subset of antennas is selected and the size of the coherent area may be Big. Alternatively, in a dense area (ie, when the usage rate of urban areas or wireless services is peak), more antennas are selected to create an area with less coherence for users closest to each other. .

  In one embodiment of the present invention, the MU-MAS is a DIDO system as described in previous patent applications [0003-0009]. DIDO systems use linear or non-linear precoding and / or limited feedback methods to create coherent areas for different users.

Numerical Results Start by calculating the number of degrees of freedom in a conventional multiple input multiple output (MIMO) system as a function of array size. Consider an unpolarized linear array and two types of channel models (indoors like the IEEE 802.11n standard for WiFi systems and outdoors like 3GPP-LTE standards for seller systems). The indoor channel mode [3] defines the number of clusters in the range [2, 6] and the angular spread [15 °, 40 °] in the range. The urban micro outdoor channel model defines an angular spread in about 6 clusters and about 20 ° base station.

  FIG. 47 shows the degree of freedom of the MIMO system in practical indoor and outdoor propagation scenarios. For example, considering a linear array with 10 antennas separated by one wavelength, the maximum degree of freedom (or number of spatial channels) available for the entire wireless link is about 3 for outdoor scenarios and indoors It is limited to 7. Of course, indoor channels offer more freedom because of the large angular spread.

  Next, the degree of freedom in the DIDO system is calculated. Consider the case where antennas are distributed over 3D space, such as a downtown urban scenario where DIDO access points may be distributed on different floors of adjacent buildings. Therefore, the DIDO transmit antennas (all connected together via a fiber or DSL backbone) are modeled as a spherical array. It is also assumed that the clusters are uniformly distributed over the solid angle.

  FIG. 48 shows the degrees of freedom in the DIDO system as a function of array diameter. For a diameter equal to 10 wavelengths, approximately 1000 degrees of freedom are available in the DIDO system. Theoretically, it is possible to create up to 1000 non-interfering channels for a user. The increased spatial diversity due to spatially distributed antennas is important for the multiplexing gain provided by DIDO over conventional MIMO systems.

  For comparison, the degree of freedom achievable with the DIDO system in a suburban environment is shown. Assuming that the clusters are distributed within the elevation angle (α, π−α), the solid angle of the cluster is defined as | Ω | = 4π cos α. For example, in a suburban scenario involving a two-story building, the elevation angle of the scattering may be α = 60 °. In this case, the number of degrees of freedom as a function of wavelength is shown in FIG.

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IV. Systems and Methods for Planned Evolution and Decommissioning of Multiuser Spectrum Increasing demand for high-speed wireless services and an increase in the number of mobile phone subscribers have led to digital voice (AMPS [1-2]) GSM [3-4], IS-95 CDMA [5]), data traffic (EDGE [6], EV-DO [7]), and Internet browsing (WiFi [8-9], WiMAX [10-11], 3G [12-13], 4G [14-15]) standards have supported radical innovation in the wireless industry over the last 30 years. The growth of wireless technology over the last 30 years has been made possible by two major initiatives:
i) The Federal Communications Commission (FCC) [16] has allocated new spectra to support emerging standards. For example, in the first generation of AMPS systems, the number of channels allocated by the FCC increased from initially 333 in 1983 to 416 in the late 1980s, supporting the increase in the number of cellular clients. More recently, the commercialization of technologies such as Wi-Fi, Bluetooth, and ZigBee has been made possible through the use of unlicensed ISM bands, once allocated by the FCC in 1985 [17].
ii) The wireless industry has created new technologies that use the limited available spectrum more efficiently to support higher data rate links and an increasing number of subscribers. One major innovation in the wireless world was the transition from analog AMPS systems to digital D-AMPS and GSM in the 1990s. This allows for much higher call volume for a given frequency band due to improved spectral efficiency. Another fundamental change is to increase the data rate of previous wireless networks by a factor of 4 and was introduced in different standards (ie IEEE 802.11n for Wi-Fi, IEEE 802.16 for WiMAX, 4G-LTE). 3GPP), multiple input multiple output (MIMO) and other spatial processing techniques were introduced in the early 2000s.

  Despite efforts to provide a solution for high-speed wireless connectivity, the wireless industry has provided high-definition (HD) video streaming to meet the growing demand for services such as games, and anywhere (wireless backbone) Is facing the new challenge of providing wireless coverage in rural areas that are expensive and unrealistic). Currently, state-of-the-art wireless standard systems (ie, 4G-LTE) cannot provide data rate requirements and latency constraints that support HD streaming services. This is especially true when the network is overloaded with a large number of simultaneous links. Again, the main drawback was limited spectrum availability, the lack of spectrally efficient technology that can truly increase data rates and provide complete coverage.

  In recent years, a new technology has emerged, called Distributed Input Distributed Output (DIDO) [18-21], described in our previous patent application [0002-0009]. DIDO technology promises several orders of magnitude increase in spectral efficiency and enables HD wireless streaming services in overloaded networks.

  At the same time, the US government is addressing the spectrum problem by advancing plans to open the 500 MHz spectrum over the next decade. This plan was announced on June 28, 2010 and aims to enable new wireless technologies to operate in new frequency bands and provide high-speed wireless coverage in urban and rural areas [22]. As part of this plan, the FCC released the approximately 200 MHz VHF and UHF spectrum, called “White Space”, on September 23, 2010 for unlicensed use [23]. One limitation for operating in these frequency bands is that they should not cause harmful interference with existing wireless microphone devices operating in the same band. Therefore, on July 22, 2011, the IEEE 802.22 working group will focus on dynamically monitoring the spectrum and avoiding harmful interference with coexisting wireless devices by operating in the available band. A new wireless system standard that uses cognitive radio technology (or spectrum detection) is summarized [24]. More recently, there has been a debate over allocating a portion of white space for authorized use and opening up this white space according to a frequency auction [25].

  Coexistence of unlicensed devices within the same frequency band and spectrum competition between unlicensed vs. licensed use have been two key challenges for FCC spectrum allocation plans over the years. For example, in white space, coexistence of a wireless microphone and a wireless communication device is possible through cognitive radio technology. However, cognitive radio provides only a fraction of the spectral efficiency of other technologies that use spatial processing such as DIDO. Similarly, the performance of Wi-Fi systems has been significantly degraded over the past decade due to the increased use of Bluetooth / ZigBee devices that operate at the same number of access points and the same unlicensed ISM band, resulting in uncontrolled interference. One drawback of the unlicensed spectrum is the random use of RF devices that will continue to be contaminated over the next few years. RF contamination also prevents unlicensed spectrum from being used for future licensed operations. Therefore, it limits important market opportunities for wireless broadband commercial services and spectrum auctions.

We propose a new system and method that dynamically allocates radio spectrum to enable coexistence and evolution of different services and standards. One embodiment of this method can dynamically assign the RF transceiver to operate in a particular part of the spectrum and dispose of the same RF device to achieve the following:
i) Spectrum reconfigurability to allow new types of radio operation (ie licensed vs. unauthorized) and / or meet new RF power emission limits. This feature allows spectrum auctions to be held as needed without having to plan in advance the use of licensed versus unlicensed spectrum. Also, the transmission power level can be adjusted to meet the new power emission level imposed by the FCC.
ii) different technologies that operate within the same band (ie, white space, wireless microphone, WiFi, and Bluetooth) so that bands can be dynamically reassigned when new technologies appear while avoiding interference with existing technologies / ZigBee)).
iii) Migrating the system to a more advanced technology that supports higher types of services (ie HD video streaming) that require higher QoS, providing higher spectral efficiency, better coverage, and improved performance Seamless evolution of wireless infrastructure over time.

  In the following, systems and methods for the planned evolution and deprecation of multiuser spectrum will be described. One embodiment of the system, as shown in FIG. 49, is one or more distributed nodes (DN) 4911 that communicate with one or more centralized processors (CP) 4901-4904 via a wired or wireless connection. To 4913. For example, in the context of a 4G-LTE network [26], the centralized processor is an access core gateway (ACGW) connected to several Node B transceivers. In the Wi-Fi context, the centralized processor is an Internet service provider (ISP) and the distributed node is a Wi-Fi access point connected to the ISP through a direct connection to a modem or cable or DSL. In another embodiment of the invention, the system is a distributed input distributed output (DIDO) system [0002-0009], connected to a BTS through a centralized processor (or BTS) and a DIDO access point (or BSN). And a distributed node that is a DIDO distributed antenna).

  DN 4911-4913 communicates with CP 4901-4904. The information exchanged from the DN to the CP is used to dynamically adjust the node configuration to the evolutionary design of the network architecture. In one embodiment, DNs 4911-4913 share an identification number with the CP. The CP stores the identification numbers of all DNs connected via the network in a lookup table or a shared table. These lookup tables or databases can be shared with other CPs, and this information is synchronized so that all CPs always have access to the latest information about all DNs on the network.

  For example, the FCC may decide to allocate a certain portion of the spectrum for unauthorized use, but the proposed system can be designed to operate within that spectrum. Because of the lack of spectrum, it may be necessary for the FCC to allocate some of these spectra in the future for authorized use in commercial carriers (ie AT & T, Verizon, or Sprint), defense, or public safety applications. In conventional wireless systems, this coexistence is not possible because wireless devices operating in the unlicensed band cause harmful interference to authorized RF transceivers. In our proposed system, distributed nodes exchange control information with CP 4901-4903 to adapt the RF transmission to the evolutionary band plan. In one embodiment, DN 4911-4913 was originally designed to operate over different frequency bands within the available spectrum. When the FCC allocates part or parts of this spectrum to an authorized operation, the CP exchanges control information with the unauthorized DN and reconfigures them for authorized use so that the unauthorized DN does not interfere with the authorized DN Stop the frequency band. This scenario is illustrated in FIG. 50, where unauthorized nodes (eg, 5002) are indicated by black circles and authorized nodes (eg, 5001) are indicated by white circles. In another embodiment, the entire spectrum can be allocated to a new authorized service, and the control information is used by the CP to stop all unauthorized DNs and avoid interference with authorized DNs. This scenario is illustrated in FIG. 51, where abandoned unauthorized nodes are covered with a cross symbol.

  As another example, it may be necessary to limit power emission for a particular device that operates at a predetermined frequency to meet FCC exposure limits [27]. For example, the wireless system may be designed for a fixed wireless link with DN 4911-4913 originally connected to an outdoor rooftop transceiver antenna. Later, the same system may be updated to support DNs including indoor portable antennas to provide better indoor coverage. FCC exposure limits for portable devices are more stringent than rooftop transmitters because they can be closer to the human body. In this case, the old DN designed for outdoor use can be reused for indoor use as long as the transmit power setting is adjusted. In one embodiment of the invention, the DN is designed with a predetermined set of transmit power levels, and CPs 4901-4903 send control information to DN 4911-4913 to select a new power level during system upgrade, This satisfies the FCC exposure limit. In another embodiment, DNs are manufactured with only one power discharge setting, and those DNs that exceed the new power discharge level are remotely stopped by the CP.

  In one embodiment, CP 4901-4903 periodically monitors all DN 4911-4913 in the network and defines the qualification to operate as an RF transceiver according to a particular standard. Those DNs that are not up to date are marked as obsolete and can be removed from the network. For example, DNs operating within current power limits and frequency bands remain active in the network and all others are turned off. It should be noted that the DN parameters controlled by the CP are not limited to power emission and frequency bands, but can be any parameters that define the radio link between the DN and the client device.

  In another embodiment of the invention, DNs 4911-4913 can be reconfigured to allow co-existence of systems with different standards within the same spectrum. For example, the power release, frequency band, or other configuration parameters of a specific DN operating in the context of a WLAN can be adjusted to accommodate the introduction of new DNs designed for WPAN applications while avoiding harmful interference Can be done.

  As new wireless standards are defined to improve data rates and coverage within wireless networks, DNs 4911-4913 may be updated to support these standards. In one embodiment, the DN is a software defined radio (SDR) with programmable computing functions such as FPGA, DSP, CPU, GPU and / or GPUPU that executes baseband signal processing algorithms. If the standard is upgraded, a new baseband algorithm is uploaded remotely from the CP to the DN to reflect the new standard. For example, in one embodiment, the first standard is CDMA-based, and then OFDM technology will be used instead to support different types of systems. Similarly, sample rate, power, and other parameters can be updated remotely to the DN. With this SDR function of DN, it is possible to continuously upgrade the network when new technology is developed and improve the overall system performance.

  In another embodiment, the system described herein is a cloud radio system consisting of multiple CPs, distributed nodes, and a network that interconnects the CPs to the DN. FIG. 52 shows an example of a cloud wireless system, in which a node identified by a black circle (for example, 5203) communicates with CP 5206, a node identified by a white circle communicates with CP 5205, and CPs 5205 to 5206 are , And communicate with each other throughout the network 5201. In one embodiment of the present invention, the cloud radio system is a DIDO system, and the DN is connected to the CP and exchanges information to reconfigure system parameters periodically or immediately and dynamically Adjust to match the changing state of the radio architecture. In the DIDO system, the CP is a DIDO BTS, the distributed node is a DIDO distributed antenna, the network is a BSN, and multiple BTSs are as described in our previous patent application [0002-0009]. Interconnected with each other via a DIDO centralized processor.

  All DNs 5202-5203 in the cloud radio system can be grouped into different sets. These sets of DNs can have multiple access technologies (eg, TDMA, FDMA, CDMA, OFDMA, and / or SDMA), different modulations (eg, QAM, OFDM), and / or coding schemes (eg, Non-interfering radio links can be created simultaneously for a number of client devices while supporting convolutional coding, LDPC, turbo codes). Similarly, any client may be served with different multiple access technologies and / or different modulation / coding schemes. CPs 5205-5206 can support these standards and dynamically select a subset of DNs that are within range of the client device based on the active clients in the system and the standards they introduce into the radio link.

References [1] Wikipedia, “Advanced Mobile Phone System”
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[5] Wikipedia, “IS-95”
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[7] Q. Bi (2004-03). “A Forward Link Performance Study of the 1xEV-DO Rel. 0 System Using Field Measurements and Simulations” (PDF). Lucent Technologies.
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[9] Wi-Fi alliance, “Wi-Fi certified make it Wi-Fi”
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  [10] WiMAX forum, http: // www. wimaxforum. org /

[11] C.I. Eklund, R.M. B. Marks, K.M. L. Stanwood and S.M. Wang, “IEEE Standard 802.16: A Technical Over of the WirelessMAN ™ Air Interface for Broadband Wireless Access”
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  [13] H. Ekstrom, A.M. Furuskar, J. et al. Karlsson, M .; Meyer, S.M. Parkvall, J. et al. Torsner, and M.M. Wahlqvist “Technical Solutions for the 3G Long-Term Evolution”, IEEE Communications Magazine, pp. 38-45, Mar. 2006

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

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[18] S.M. Perlman and A.M. Forenza, “Distributed-input distributed-output (DIDO) wireless technology: a new approach to multi-user wireless”, Aug. 2011
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[19] Bloomberg Businessesweek, “Steve Perlman's Wireless Fix”, July 27, 2011
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[22] The White House, “Presentative Memory: Unleasing the Wireless Broadband Revolution”, June 28, 2010.
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[23] FCC, “Open commitment meeting”, Sept. 23 ^rd, 2010
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[25] “A bill”, 112th congress, 1 ^st session, July 12, 2011
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  [26] H.M. Ekstrom, A.M. Furuskar, J. et al. Karlsson, M .; Meyer, S.M. Parkvall, J. et al. Torsner, and M.M. Wahlqvist “Technical Solutions for the 3G Long-Term Evolution”, IEEE Communications Magazine, pp. 38-45, Mar. 2006

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

V. System and method for correcting the Doppler effect in a distributed input distributed output radio system In the best mode for carrying out this part of the invention, the parameters are adaptively reconfigured so that the user mobility or propagation environment is A multi-user (MU) multi-antenna system (MAS) for multi-user radio transmission that corrects for Doppler effects due to changes is described.
In one embodiment, the MAS is a distributed input distributed output (DIDO) system, as described in co-pending patent application [0002-0016] and shown in FIG. The DIDO system of one embodiment includes the following components.
User equipment (UE): The UE 5301 in one embodiment receives a data stream from the DIDO backhaul on the downlink (DL) channel and transmits data to the DIDO backhaul via the uplink (UL) channel. Includes RF transceivers for fixed or mobile clients.
Base transceiver station (BTS): The BTS 5310-5314 of one embodiment provides an interface for DIDO backhaul and radio channels. BTS 5310-5314 is an access point consisting of a DAC / ADC and a radio frequency (RF) chain that converts baseband signals to RF. In some cases, the BTS is a simple RF transceiver with a power amplifier / antenna, and the RF signal is transmitted to the BTS through RF-over-fiber technology as described in our patent application.
Controller (CTR): CTR 5320 in one embodiment transmits training signals for BTS and / or UE time / frequency synchronization, transmission / reception of control signals to UE, channel state information (CSI) or channel quality information from UE A specific type of BTS designed for a specific dedicated function such as
Centralized processor (CP): In one embodiment, CP 5340 is a DIDO server that interfaces to the Internet or other type of external network 5350 and DIDO backhaul. The CP calculates DIDO baseband processing and sends the waveform to the distributed BTS for DL transmission.
Base Station Network (BSN): The BSN 5330 in one embodiment is a network that connects a CP to a distributed BTS that transmits information for either the DL channel or the UL channel. A BSN is a wired network, a wireless network, or a combination of the two. For example, the BSN is a DSL, cable, fiber optic network, or line-of-sight or non-line-of-sight radio link. Further, the BSN is a private network, a local area network, or the Internet.

  As described in the co-pending application, the DIDO system creates independent channels for multiple users so that each user can receive a channel without interference. In a DIDO system, this is achieved by utilizing spatial diversity using a distributed antenna or BTS. In one embodiment, the DIDO system uses spatial diversity, polarization diversity, and / or pattern diversity to increase the degree of freedom within each channel. The increased freedom of the radio link is used for transmission of independent data streams to an increased number of UEs (ie, multiplexing gain) and / or improved coverage (ie, diversity gain).

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

  In one embodiment, the BTSs 5310-5314 send training signals and / or independent data streams to the UE 5301 over the DL channel, as shown in FIG. The training signal is used by the UE for different purposes such as time / frequency synchronization, channel estimation, and / or channel state information (CSI) estimation. In one embodiment of the present invention, MU-MAS DL utilizes non-linear precoding such as Dirty Paper Coding (DPC) [1-2] or Tomlinson-Harashima (TH) [3-4] precoding. In another embodiment of the present invention, the MU-MAS DL is configured with block diagonalization (BD) [0003-0009] or zero forcing beamforming (ZF-BF) as described in our co-pending patent application. ) Use non-linear precoding such as [5]. If the number of BTSs is larger than the UE, additional BTSs are used to increase the link quality for every UE through diversity schemes such as antenna selection or eigenmode selection as described in [0002-0016]. If the number of BTS is smaller than the UE, the additional UE shares the radio link with other UEs through conventional multiplexing techniques (eg, TDMA, FDMA, CDMA, OFDMA).

  The UL channel is used for data transmission from the UE 5301 to the CP 5340 and / or CSI (or channel quality information) used by the DIDO pre-encoder. In one embodiment, the UL channel from the UE is multiplexed to the CTR as shown in FIG. 56 or to the nearest BTS through conventional multiplexing techniques (eg, TDMA, FDMA, CDMA, OFDMA). In another embodiment of the present invention, spatial processing techniques are used to separate the UL channel from UE 5301 into distributed BTS 5310-5314 as shown in FIG. For example, a UL stream is transmitted from a client to a DIDO antenna through a multiple input multiple output (MIMO) multiplexing scheme. MIMO multiplexing schemes include the transmission of independent data streams from clients and the elimination of co-channel interference through the use of linear or non-linear receivers at DIDO antennas. In another embodiment, UL / DL channel correlation is maintained and the channel uses downlink weights across the uplink assuming that the Doppler effect does not change much between DL and UL transmissions. Then, the uplink stream is demodulated. In another embodiment, a maximum ratio combining (MRC) receiver is used on the UL channel to increase the signal quality from any client at the DIDO antenna.

  Data, control information, and CSI transmitted on the DL / UL channel are shared between CP 5340 and BTS 5310-5314 via BSN 5330. DL channel known training signals can be stored in memory at BTS 5310-5314 to reduce overhead on BSN 5330. Depending on the type of network (ie, wireless-to-wired, DSL-to-cable or fiber), the data rate available to exchange information between CP 5340 and BTS 5310-5314 is not sufficient on BSN 5330. There may not be (especially when baseband signals are delivered to the BTS). For example, suppose a BTS transmits a 10 Mbps independent data stream to any UE over a 5 MHz bandwidth (depending on the digital modulation and FEC coding scheme used on the radio link). If 16-bit quantization is used for the real part and 16 is used for the imaginary part, the baseband signal requires a data yield of 160 Mbps from CP to BTS on the BSN. In one embodiment, the CP and BTS are equipped with an encoder for compressing information transmitted on the BSN and a decoder for decompressing the information. In the transmission link, the pre-encoded baseband data transmitted from the CP to the BTS is compressed to reduce the amount of bit and overhead transmitted on the BSN. Similarly, on the reverse link, CSI as well as data (sent on the uplink channel from the UE to the BTS) are compressed before being sent on the BSN from the BTS to the CP. In order to reduce the amount of bit and overhead transmitted on the BSN, different compression algorithms are used, including but not limited to lossless and / or lossy techniques [6].

One feature of the DIDO system used in one embodiment is to allow CP 5340 to recognize CSI or channel quality information between all BTS 53105314 and UE 5301 to allow pre-encoding. As explained, DIDO performance depends on the rate at which CSI is delivered to the CP with respect to the rate of change of the radio link. It is known that channel complex gain variations are due to changes in propagation environment that cause UE mobility and / or Doppler effects. The rate of change of the channel is measured in terms of the channel coherence time (T _c ) that is inversely proportional to the maximum Doppler shift. In order to reliably execute DIDO transmission, the waiting time by CSI feedback needs to be small (for example, 1/10 or less) with respect to the channel coherence time. In one embodiment, the latency in the CSI feedback loop is measured as the difference between the CSI training transmission time and the demodulation time at the UE side of the pre-encoded data, as shown in FIG.

In a frequency division duplex (FDD) DIDO system, BTS 5310-5314 sends CSI training to UE 5301, UE 5301 estimates CSI and feeds back to BTS. The BTS then sends CSI to the CP 5340 via the BSN, which calculates the DIDO pre-encoded data stream and returns them to the BTS via the BSN 5330. Finally, the BTS sends a pre-coded stream to the UE that demodulates the data. Referring to FIG. 58, the total latency of DIDO feedback is shown by:
2 ^* _TDL + _TUL + _TBSN + _TCP
Where T _DL includes the time to construct, transmit and process the downlink frame, T _UL includes the time to construct, transmit and process the uplink frame, and T _BSN is on the BSN. T _CP is the time it takes for the CP to process the CSI, generate a pre-encoded data stream for the UE, and determine different UE schedules for the current transmission. In this case, _TDL is multiplied by 2 to correspond to training signal time (BTS to UE) and feedback signal time (UE to BTS). In time division duplex (TDD), if channel correlation is available, the UE calculates CSI and sends CSI training to the BTS that sends it to the CP, so the first step is omitted (ie. , Send CSI training signal from BTS to UE). Thus, in this embodiment, the total latency of the DIDO feedback loop is as follows:
T _DL + T _UL + T _BSN + T _CP

The waiting time T _BSN depends on the type of BSN (whether dedicated cable, DSL, fiber optic connection, or general Internet). Typical values can vary between some fraction of 1 msec and 50 msec. The calculation time in the CP can be shortened when DIDO processing is executed in the CP by a dedicated processor such as ASIC, FPGA, DSP, CPU, GPU, and / or GPUPU. Furthermore, if the number of BTSs 5310-5314 exceeds the number of UEs 5301, all UEs are served at the same time by multi-user scheduling, thereby eliminating latency. Therefore, the waiting time T _CP is _{insignificant} compared to _TBSN . Finally, DL and UL transmission and reception processes are typically performed in an ASIC, FPGA, or DSP with insignificant computation time and have a relatively large signal bandwidth (eg, greater than 1 MHz). In some cases, the frame time can be very small (ie, less than 1 msec). Therefore, _TDL and _TUL are also _{insignificant} compared to _TBSN .

In one embodiment of the present invention, CP 5340 tracks the Doppler rate of all UEs 5301 and dynamically assigns BTS 5310-5314 to higher Doppler UEs with the lowest T _BSN . This adaptation is based on the following different criteria:
BSN type: For example, dedicated fiber optic links typically experience lower latency than cable modems or DSL. As a result, lower latency BSNs are used for high mobility UEs (eg, cars on highways, trains), while higher latency BSNs are used for fixed radio or low mobility UEs (eg, residential Used in equipment, pedestrians, residential vehicles).
QoS type: For example, the BSN can support different types of DIDO traffic or non-DIDO traffic. It is possible to define quality of service (QoS) with different priorities for different types of traffic. For example, the BSN assigns high priority to DIDO traffic and assigns low priority to non-DIDO traffic. Alternatively, high priority QoS is assigned to high mobility UE traffic and low priority QoS is assigned to low mobility UEs.
Long-term statistics: For example, traffic on the BSN can vary significantly with time (eg, home night use and office day use). Higher traffic load can result in higher latency. As a result, at different times, a high traffic BSN is used for a low mobility UE if it results in higher latency, while a low traffic BSN is used for a high mobility UE if it results in lower latency. used.
Short-term statistics: For example, any BSN may be affected by temporary network congestion that may result in higher latency. As a result, the CP is congested due to the low mobility UE and the rest of the BSN when the congestion results in higher latency, and because of the high mobility UE when they are at lower latency. A BTS can be selected adaptively from the BSN.

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

In the equation, λ is a wavelength corresponding to the carrier frequency. Thus, in the LOS channel, the Doppler shift is maximum for link A in FIG. 59 and almost zero for link C. In non-LOS (NLOS), the maximum Doppler shift is based on the direction of the multipath around the UE, but in general, due to the distributed nature of BTSs in DIDO systems, some BTSs are defined for a given UE (eg, , BTS 5312) will experience higher Doppler, while other BTSs will experience lower Doppler for a given UE (eg, BTS 5314).

  In one embodiment, the CP tracks the Doppler rate on every BTS-UE link and selects only the link with the lowest Doppler effect for every UE. Similar to the techniques described herein, CP 5340 defines a “user cluster” for every UE 5301. The user cluster has good link quality (defined based on specific signal-to-noise ratio, SNR, threshold) and low Doppler (eg, based on a predetermined Doppler threshold) as shown in FIG. A set of BTSs with In FIG. 60, all BTSs 5-10 have good SNR for UE1, but only BTS 6-9 experience a low Doppler effect (eg, below a specified threshold).

The CP of this embodiment records all SNR and Doppler values for every BTS-UE link in a matrix and selects a submatrix that satisfies the SNR threshold and Doppler threshold. In the example shown in FIG. 61, _{submatrix, C 2,6, C 2,7, C} 3,9, C 4,7, C 4,8, green surrounding the C _{4, 9,} and C _{5, 6} Identified with a dotted line. The DIDO pre-encoding weight is calculated for the corresponding UE based on the corresponding submatrix. Note that BTSs 5 and 10 are reachable by UEs 2, 3, 4, 5, and 7 as shown in FIG. As a result, in order to avoid interference to UE1 during transmission to these other UEs, either BTS 5 and 10 can be switched or replaced with conventional multiplexing techniques such as TDMA, FDMA, CDMA, or OFDMA. Need to be assigned to different orthogonal channels.

In another embodiment, the adverse effects of Doppler effects on DIDO precoding system performance are reduced by linear prediction, one technique for predicting future complex channel coefficients based on past channel estimates. Rather than intending constraints, by way of example, different prediction algorithms for single input single output (SISO) and OFDM wireless systems were proposed in [7-11]. If the future channel complex coefficients are known, it is possible to reduce errors due to old CSI. For example, FIG. 62 shows different times, i) t _CTR , UL where the CTR of FIG. 58 receives CSI from the UE in the FDD system (or at the same time the BTS uses DL / UL correlation in the TDD system. Ii) t _CP is the time at which CSI is delivered to the CP via the BSN, and iii) t _BTS is used for precoding on the radio link. Channel gain (or CSI) at In FIG. 62, due to the waiting time T _BSN (also shown in FIG. 58), the CSI estimated at time t _CTR becomes obsolete by the time it is used for radio transmission on the DL channel at time t _BTS . (Ie, the complex channel gain has changed). One way to avoid this effect by Doppler is to perform the prediction method at the CP. The CSI estimate available at t _CTR is delayed by T _BSN / 2 due to the delay from CTR to CP, corresponding to the channel gain at time t ₀ in FIG. As a result, the CP uses all or part of the CSI expected before time t ₀ and stores it in memory to predict future channel coefficients at time t ₀ + T _BSN = t _CP . If the prediction algorithm has minimal error propagation, the expected CSI at time t _CP will reliably reproduce the future channel gain. The time difference between the expected CSI and the current CSI is called the expected range and typically corresponds to the channel coherence time in a SISO system.

  In DIDO systems, the prediction algorithm is more complex because it estimates future channel coefficients in both time and space domains. A linear prediction algorithm that takes advantage of the spatio-temporal characteristics of MIMO radio channels was described in [12-13]. In [13], the performance of the prediction algorithm in a MIMO system (measured by mean square error (MSE)) has a higher channel coherence time (ie, reduced Doppler effect) and a lower channel (due to lower spatial correlation). It has been shown to improve to a coherent distance. Therefore, the expected range (in seconds) of the spatiotemporal method is directly proportional to the channel coherence time and inversely proportional to the channel coherence distance. In DIDO systems, the coherence distance is low due to the high spatial selectivity provided by the distributed antenna.

  Described herein is a predictive technique that utilizes the temporal and spatial diversity of the DIDO system to predict future vector channels (ie, BTS to UE CSI). These embodiments take advantage of the spatial diversity available on the wireless channel to obtain insignificant CSI prediction errors and an expanded prediction range over any existing SISO and MIMO prediction algorithms. One important feature of these techniques is the use of distributed antennas in view of receiving uncorrelated complex channel coefficients from distributed UEs.

  In one embodiment of the present invention, spatio-temporal predictors are combined with frequency domain estimators to enable CSI prediction across all available subcarriers in a system such as an OFDM system. In another embodiment of the present invention, DIDO pre-encoded weights (rather than CSI) are predicted based on previous estimates of DIDO weights.

References [1] Costa, “Writing on Dirty Paper”, IEEE Transactions on Information Theory, Vol. 29, no. 3, Page (s): 439-441, May 1983.

  [2] U.I. Erez, S.M. Shamai (Shitz), and R.M. Zamir, “Capacity and lattice-strategies for canceling known interference,” Proceedings of International Symposium on Information Theory, Honolu, Hawa. 2000.

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

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

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

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

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

  Embodiments of the invention can include various stages as described above. These stages can be implemented with machine-executable instructions that cause a general purpose or special purpose processor to perform certain stages. For example, the various components within the base station / AP and the client device described above can be implemented as software running on a general purpose processor or a dedicated processor. In order to avoid obscuring the relevant aspects of the present invention, various known personal computer components such as computer memory, hard drives, input devices, etc. have been omitted from the figures.

  Instead, in one embodiment, the various functional modules and associated stages shown herein are pre-determined hardware components that include hardwired logic that performs the stages, such as an application specific integrated circuit (“ASIC”). Or by any combination of programmed computer components and custom hardware components.

  In one embodiment, certain modules, such as the encoding, modulation, and signal processing logic 903 described above, are digital signal processors (“DSP”) using Texas Instruments' TMS320x architecture (eg, TMS320C6000, TMS320C5000..., Etc.). ') Can be executed on a programmable DSP (or group of DSPs). The DSP in this embodiment can be embedded in an add-on card to a personal computer such as a PCI card, for example. Of course, a variety of different DSP architectures can be used while still adhering to the underlying principles of the present invention.

  Various embodiments of the present invention may be provided as a machine-readable medium storing machine-executable instructions. The machine readable medium may include flash memory, optical disk, CD-ROM, DVD ROM, RAM, EPROM, EEPROM, magnetic card, optical card, or any other type of machine readable medium suitable for storing electronic instructions. However, it is not limited to these. For example, the present invention transfers data from a remote computer (eg, a server) to a requesting computer (eg, a client) as a data signal implemented in a carrier wave or other propagation medium over a communication link (eg, a modem or network connection). Can be downloaded as a computer program.

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

  Furthermore, in the above description, many documents are cited so that the present invention can be understood more completely. All of these references are incorporated herein by reference.

Claims (35)

A multi-user (MU) multi-antenna system (MAS) comprising one or more centralized units communicatively connected to a plurality of distributed transceiver stations or antennas over a network,
The network consists of a wired link, a wireless link, or a combination of both used as a backhaul communication channel;
A multi-user (MU) multi-antenna system (MAS) that adaptively reconfigures to compensate for Doppler effects due to changes in user mobility or propagation environment.   The MU-MAS system includes one or more sets of user equipment (UE), a base transceiver station (BTS), a controller (CTR), a centralized processor (CP), and a base station network (BSN). The system of claim 1, comprising:   The system of claim 1, wherein a distributed antenna is used to improve data rate and / or coverage for one or more users in a wireless system using spatial diversity, polarization diversity, and / or pattern diversity. .   The system according to claim 1, wherein the UE is arranged around or between the distributed antennas or surrounded by the distributed antennas.   The system of claim 1, wherein the MU-MAS demodulates an independent stream (eg, data or CSI) from the UE using complex weights at the receiver of an uplink channel.   6. The system of claim 5, wherein the complex weights of the uplink receiver are derived from the downlink precoding weights or calculated via a maximum ratio combining receiver.   The system of claim 2, wherein the CP and the BTS are equipped with an encoder / decoder to compress / decompress information exchanged between them on the BSN.   8. The system of claim 7, wherein the pre-encoded baseband data stream is compressed prior to transmission / reception between the BTS and the MU-MAS distributed antenna to reduce overhead on the BSN.   The system according to claim 2, wherein the CP adaptively selects the BTS used for a low mobility UE or a high mobility UE based on a latency on the BSN.   The adaptation may be a high data rate BSN type versus a low data rate BSN type, or QoS, or average traffic statistics (eg, daytime or nighttime use of different networks), or instantaneous traffic statistics on the BSN (eg, 10. The system of claim 9, wherein the system is based on temporary network congestion.   The system according to claim 2, wherein the CP adaptively selects the BTS to be used for a low mobility UE or a high mobility UE based on the Doppler speed of every BTS-UE link.   The system of claim 2, wherein linear prediction is used to estimate the future CSI or MU-MAS precoding weights, thereby removing Doppler's adverse effects on the performance of the MU-MAS system.   The system of claim 12, wherein the prediction is used in a time domain, a frequency domain, and / or a spatial domain. Measuring a first mobile user's Doppler speed for a plurality of base transceiver stations (BTS);
Dynamically assigning the first mobile user to the first BTS or a first set of BTSs of the plurality of BTSs based on the measured Doppler speed of the first BTS relative to other BTSs A method implemented in a multi-user (MU) multi-antenna system (MAS) to correct the Doppler effect.   If the first mobile user has a measured Doppler speed that is relatively higher than the second mobile user, the dynamically assigning assigns the first mobile user to the first BTS or first Assigning the second mobile user to a second BTS or a second set of BTSs, wherein the first BTS or the first set of BTSs comprises: 15. The method of claim 14, having a latency associated therewith that is relatively lower than a second BTS or a second set of BTSs.   The waiting time includes: (a) the time required for transmitting a first training signal from the first mobile user to the BTS; and (b) a base station network connecting the BTS to a centralized processor (CP) ( (C) the CP processes channel state information (CSI) of the radio channel between the BTS and the first mobile user, and based on the CSI, the round trip waiting time on the BSN) Generating a first mobile user pre-encoded data stream and determining a schedule for transmission to the different mobile users including the first user for the current transmission. The method described in 1.   The method of claim 16, wherein the waiting time further comprises a time taken to transmit a second training signal from the BTS to the first mobile user.   The dynamically allocating is a combination of the link quality of the communication channel between each BTS and the first mobile user and the measured Doppler rate for each BTS for the first user. 15. The method of claim 14, further comprising assigning based on.   The method of claim 18, wherein the BTS having the relatively high link quality is selected for a predetermined Doppler rate.   The method of claim 18, wherein the BTS having the relatively low Doppler rate is selected for a given link quality.   15. The method of claim 14, further comprising estimating complex channel coefficients based on past complex channel coefficients to correct Doppler adverse effects on communications between the BTS and the first mobile user.   The method of claim 21, wherein linear prediction is used for the prediction.   The first mobile user is based on both the Doppler speed and channel state information (CSI) that defines the quality of a communication channel between the mobile user and each BTS of the plurality of BTSs. 15. The method of claim 14, wherein the method is dynamically assigned to a BTS or a first set of BTSs. Constructing a Doppler rate and link quality matrix for each BTS of the plurality of BTSs for the first mobile user;
24. The method of claim 23, further comprising: selecting a BTS having a Doppler speed below a specified threshold and a link quality exceeding the specified threshold. Multiple base transceiver stations (BTS);
A first mobile user establishing a communication link with each of the BTSs;
Measure a first mobile user's Doppler rate for each of the BTSs, and for other BTSs, based on the measured Doppler rate of the first BTS, to the first BTS of the plurality of BTSs A multi-user (MU) multi-antenna system (MAS) for correcting the Doppler effect, comprising a centralized processor (CP) that dynamically allocates the first mobile user.   If the first mobile user has a measured Doppler speed that is relatively higher than the second mobile user, the dynamically assigning assigns the first mobile user to the first BTS. And assigning the second mobile user to a second BTS, wherein the first BTS has a latency associated therewith that is relatively lower than the second BTS. The described system.   The waiting time includes: (a) the time required for transmitting a first training signal from the first mobile user to the BTS; and (b) a base station network connecting the BTS to a centralized processor (CP) ( (C) the CP processes channel state information (CSI) of the radio channel between the BTS and the first mobile user, and based on the CSI, the round trip waiting time on the BSN) Generating a first mobile user pre-encoded data stream and determining a schedule for transmission to the different users including the first user for the current transmission. The described system.   28. The system of claim 27, wherein the waiting time further comprises a time required to transmit a second training signal from the BTS to the first mobile user.   The dynamically assigning includes: a link quality of a communication channel between each BTS and the first mobile user, and the measured Doppler rate associated with each BTS for the first user. 26. The system of claim 25, further comprising assigning based on the combination.   30. The system of claim 29, wherein the BTS having the relatively high link quality is selected for a predetermined Doppler rate.   30. The system of claim 29, wherein the BTS having the relatively low Doppler rate is selected for a given link quality.   26. The system of claim 25, wherein the CP estimates future complex channel coefficients based on past complex channel coefficients to correct Doppler's adverse effects on communications between the BTS and the first mobile user. .   The system of claim 32, wherein linear prediction is used for estimation.   The first mobile user is based on both the Doppler speed and channel state information (CSI) that defines the quality of a communication channel between the mobile user and each BTS of the plurality of BTSs. 26. The system of claim 25, dynamically assigned to one BTS. The CP is
Constructing a Doppler rate and link quality matrix for each BTS of the plurality of BTSs for the first mobile user;
35. The system of claim 34, wherein the system performs an additional operation consisting of: selecting a BTS having a Doppler speed below a specified threshold and a link quality exceeding the specified threshold.