Classifications

• • G—PHYSICS
  • G08—SIGNALLING
  • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
  • G08B13/00—Burglar, theft or intruder alarms
  • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B7/00—Radio transmission systems, i.e. using radiation field
  • H04B7/01—Reducing phase shift
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
  • H04B1/69—Spread spectrum techniques
  • H04B1/707—Spread spectrum techniques using direct sequence modulation
  • H04B1/7097—Interference-related aspects
  • H04B1/711—Interference-related aspects the interference being multi-path interference
  • H04B1/7115—Constructive combining of multi-path signals, i.e. RAKE receivers
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B7/00—Radio transmission systems, i.e. using radiation field
  • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
  • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
  • H04B7/0413—MIMO systems
  • H04B7/0452—Multi-user MIMO systems
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B7/00—Radio transmission systems, i.e. using radiation field
  • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
  • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
  • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
  • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
  • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
  • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
  • H04B7/0621—Feedback content
  • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B7/00—Radio transmission systems, i.e. using radiation field
  • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
  • H04B7/022—Site diversity; Macro-diversity
  • H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
  • Y02D30/00—Reducing energy consumption in communication networks
  • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
  • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y10S367/00—Communications, electrical: acoustic wave systems and devices
  • Y10S367/904—Doppler compensation systems

Definitions

• Prior art multi-user wireless systems may include only a single base station or several base stations.
• a single WiFi base station (e.g., utilizing 2.4 GHz 802.1 lb, g or n protocols) attached to a broadband wired Internet connection in an area where there are no other WiFi access points (e.g. a WiFi access point attached to DSL within a rural home) is an example of a relatively simple multi-user wireless system that is a single base station that is shared by one or more users that are within its transmission range. If a user is in the same room as the wireless access point, the user will typically experience a high-speed link with few transmission disruptions (e.g.
• TDMA time-division multiplexed access
• a user in a large apartment building with a WiFi adapter may well experience very poor throughput due to dozens of other interfering WiFi networks (e.g. in other apartments) serving other users that are in the same coverage area, even if the user's access point is in the same room as the client device accessing the base station. Although the link quality is likely good in that situation, the user would be receiving interference from neighbor WiFi adapters operating in the same frequency band, reducing the effective throughput to the user.
• MIMO multiple-input multiple-output
• MU-MIMO multiuser MIMO
• Spatial diversity is a function of antenna spacing and multipath angular spread in the wireless links.
• transmit antennas at a base station are typically clustered together and placed only one or two wavelengths apart due to limited real estate on antenna support structures (referred to herein as "towers," whether physically tall or not) and due to limitations on where towers may be located.
• multipath angular spread is low since cell towers are typically placed high up (10 meters or more) above obstacles to yield wider coverage.
• a given sector of a given cell ends up being a shared block of DL and UL spectrum among all of the users in the cell sector, which is then shared among these users primarily in only the time domain.
• cellular systems based on Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA) both share spectrum among users in the time domain.
• These methods include limiting transmission power from the base station so as to limit the range of interference, beamforming (via synthetic or physical means) to narrow the area of interference, time-domain multiplexing of spectrum and/or MU-MIMO techniques with multiple clustered antennas on the user device, the base station or both. And, in the case of advanced cellular networks in place or planned today, frequently many of these techniques are used at once.
• MANET mobile ad hoc network
• Mobile ad hoc network is an example of a cooperative self-configuring network intended to provide peer-to-peer communications, and could be used to establish communication among radios without cellular infrastructure, and with sufficiently low-power communications, can potentially mitigate interference among simultaneous transmissions that are out of range of each other.
• a vast number of routing protocols have been proposed and implemented for MANET systems (see http://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols for a list of dozens of routing protocols in a wide range of classes), but a common theme among them is they are all techniques for routing (e.g. repeating) transmissions in such a way to minimize transmitter interference within the available spectrum, towards the goal of particular efficiency or reliability paradigms.
• All of the prior art multi-user wireless systems seek to improve spectrum utilization within a given coverage area by utilizing techniques to allow for simultaneous spectrum utilization among base stations and multiple users.
• the techniques utilized for simultaneous spectrum utilization among base stations and multiple users achieve the simultaneous spectrum use by multiple users by mitigating interference among the waveforms to the multiple users. For example, in the case of 3 base stations each using a different frequency to transmit to one of 3 users, there interference is mitigated because the 3 transmissions are at 3 different frequencies. In the case of sectorization from a base station to 3 different users, each 180 degrees apart relative to the base station, interference is mitigated because the beamforming prevents the 3 transmissions from overlapping at any user.
• transmissions are distributed among different base stations (or ad hoc transceivers) and are structured and/or controlled so as to avoid the RF waveform transmissions from the different base stations and/or different ad hoc transceivers from interfering with each other at the receiver of a given user.
• Prior art related to the current invention describes beamforming systems and methods for null-steering in multiuser scenarios.
• Beamforming was originally conceived to maximize received signal-to-noise ratio (SNR) by dynamically adjusting phase and/or amplitude of the signals (i.e., beamforming weights) fed to the antennas of the array, thereby focusing energy toward the user's direction.
• beamforming can be used to suppress interfering sources and maximize signal-to-interference-plus-noise ratio (SINR).
• SINR signal-to-interference-plus-noise ratio
• the weights are computed to create nulls in the direction of the interfering sources.
• the weights are calculated to pre-cancel inter-user interfence and maximize the SINR to every user.
• Alternative techniques for multiuser systems such as BD precoding, compute the precoding weights to maximize throughput in the downlink broadcast channel.
• FIG. 1 illustrates a main DIDO cluster surrounded by neighboring DIDO clusters in one embodiment of the invention.
• FIG. 2 illustrates frequency division multiple access (FDMA) techniques employed in one embodiment of the invention.
• FDMA frequency division multiple access
• FIG. 3 illustrates time division multiple access (TDMA) techniques employed in one embodiment of the invention.
• TDMA time division multiple access
• FIG. 4 illustrates different types of interfering zones addressed in one embodiment of the invention.
• FIG. 5 illustrates a framework employed in one embodiment of the invention.
• FIG. 7 illustrates a graph showing SER derived from two IDCI-precoding techniques.
• FIG. 8 illustrates an exemplary scenario in which a target client moves from a main DIDO cluster to an interfering cluster.
• FIG. 9 illustrates the signal-to-interference-plus-noise ratio (SINR) as a function of distance (D).
• FIG. 10 illustrates the symbol error rate (SER) performance of the three scenarios for 4-QAM modulation in flat-fading narrowband channels.
• FIG. 11 illustrates a method for IDCI precoding according to one embodiment of the invention.
• FIG. 12 illustrates the SINR variation in one embodiment as a function of the client's distance from the center of main DIDO clusters.
• FIG. 13 illustrates one embodiment in which the SER is derived for 4-QAM modulation.
• FIG. 14 illustrates one embodiment of the invention in which a finite state machine implements a handoff algorithm.
• FIG. 15 illustrates depicts one embodiment of a handoff strategy in the presence of shadowing.
• FIG. 16 illustrates a hysteresis loop mechanism when switching between any two states in Fig. 93.
• FIG. 17 illustrates one embodiment of a DIDO system with power control.
• FIG. 18 illustrates the SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios.
• FIG. 19 illustrates MPE power density as a function of distance from the source of RF radiation for different values of transmit power according to one embodiment of the invention.
• FIGS. 20a-b illustrate different distributions of low-power and high-power DIDO distributed antennas.
• FIGS. 21a-b illustrate two power distributions corresponding to the configurations in Figs. 20a and 20b, respectively.
• FIG. 22a-b illustrate the rate distribution for the two scenarios shown in Figs. 99a and 99b, respectively.
• FIG. 23 illustrates one embodiment of a DIDO system with power control.
• FIG. 24 illustrates one embodiment of a method which iterates across all antenna groups according to Round-Robin scheduling policy for transmitting data.
• FIG. 25 illustrates a comparison of the uncoded SER performance of power control with antenna grouping against conventional eigenmode selection in U.S. Patent No. 7,636,381.
• FIGS. 26a-c illustrate three scenarios in which BD precoding dynamically adjusts the precoding weights to account for different power levels over the wireless links between DIDO antennas and clients.
• FIG. 28 illustrates one embodiment of a channel matrix frequency response for DIDO 2x2, with a single antenna per client.
• FIG. 30 illustrates exemplary SER for different QAM schemes (i.e., 4-QAM, 16- QAM, 64-QAM).
• FIG. 31 illustrates one embodiment of a method for implementing link adaptation (LA) techniques.
• FIG. 32 illustrates SER performance of one embodiment of the link adaptation (LA) techniques.
• FIG. 36 illustrates one embodiment of a system which employs super-clusters, DIDO-clusters and user-clusters.
• FIG. 37 illustrates a system with user clusters according to one embodiment of the invention.
• FIGS. 38a-b illustrate link quality metric thresholds employed in one embodiment of the invention.
• FIGS. 39-41 illustrate examples of link-quality matrices for establishing user clusters.
• FIG. 42 illustrates an embodiment in which a client moves across different different DIDO clusters.
• FIGS. 43-46 illustrate relationships between the resolution of spherical arrays and their area A in one embodiment of the invention.
• FIG. 47 illustrates the degrees of freedom of MIMO systems in practical indoor and outdoor propagation scenarios.
• FIG. 48 illustrates the degrees of freedom in DIDO systems as a function of the array diameter.
• FIG. 49 illustrates one embodiment which includes multiple centralized processors (CP) and distributed nodes (DN) that communicate via wireline or wireless connections.
• CP centralized processors
• DN distributed nodes
• FIG. 50 illustrates one embodiment in which CPs exchange control information with the unlicensed DNs and reconfigure them to shut down the frequency bands for licensed use.
• FIG. 51 illustrates one embodiment in which an entire spectrum is allocated to the new service and control information is used by the CPs to shut down all unlicensed DNs to avoid interference with the licensed DNs.
• FIG. 52 illustrates one embodiment of a cloud wireless system including multiple CPs, distributed nodes and a network interconnecting the CPs to the DNs.
• FIGS. 53-59 illustrate embodiments of a multiuser (MU) multiple antenna system (MAS) that adaptively reconfigures parameters to compensate for Doppler effects due to user mobility or changes in the propagation environment.
• MU multiuser
• MAS multiple antenna system
• FIG 60 illustrates a plurality of BTSs, some of which have good SNR and some of which have low Doppler with respect to a UE.
• FIG. 61 illustrates one embodiment of a matrix containing values of SNR and Doppler recorded by a CP for a plurality of BTS-UE links.
• FIG. 62 illustrates the channel gain (or CSI) at different times in accordance with one embodiment of the invention.
• DIDO Distributed-Input Distributed-Output
• M transmit antennas When M transmit antennas are employed, it is possible to create up to (M- 1) points of zero RF energy in predefined locations.
• the points of zero RF energy are wireless devices and the transmit antennas are aware of the channel state information (CSI) between the transmitters and the receivers.
• the CSI is computed at the receivers and fed back to the transmitters.
• the CSI is computed at the transmitter via training from the receivers, assuming channel reciprocity is exploited.
• the transmitters may utilize the CSI to determine the interfering signals to be simultaneously transmitted.
• block diagonalization (BD) precoding is employed at the transmit antennas to generate points of zero RF energy.
• receive beamforming computes the weights to suppress interference at the receive side (via null-steering), whereas some embodiments of the invention described herein apply weights at the transmit side to create interference patters that result in one or multiple locations in space with "zero RF energy.”
• receive beamforming computes the weights to suppress interference at the receive side (via null-steering)
• some embodiments of the invention described herein apply weights at the transmit side to create interference patters that result in one or multiple locations in space with "zero RF energy.”
• the systems and methods described herein minimize signal quality under certain conditions and/or from certain
• transmit antennas distributed in space provide higher degrees of freedom (i.e., higher channel spatial diversity) that can be exploited to create multiple points of zero RF energy and/or maximum SINR to different users. For example, with M transmit antennas it is possible to create up to (M-l) points of RF energy.
• practical beamforming or BD multiuser systems are typically designed with closely spaced antennas at the transmit side that limit the number of simultaneous users that can be serviced over the wireless link, for any number of transmit antennas M.
• H is the channel matrix obtained by combining the channel vectors (h fc G C lx the M transmit antennas to the K users as
• singular value decomposition of the channel matrix H is computed and the precoding weight w is defined as the right singular vector corresponding to the null subspace (identified by zero singular value) of H.
• the transmit antennas employ the weight vector defined above to transmit RF energy, while creating K points of zero RF energy at the locations of the K users such that the signal received at the k th user is given by
• n k G C lxl is the additive white Gaussian noise (AWGN) at the k th user.
• AWGN additive white Gaussian noise
• singular value decomposition of the channel matrix H is computed and the precoding weight w is defined as the right singular vector corresponding to the null subspace (identified by zero singular value) of H.
• the wireless system is a DIDO system and points of zero RF energy are created to pre-cancel interference to the clients between different DIDO coverage areas.
• a DIDO system is described which includes:
• BTS base transceiver stations
• DIDO cluster Every BTS is connected via the BSN to multiple distributed antennas that provide service to given coverage area called DIDO cluster.
• DIDO cluster In the present patent application we describe a system and method for removing interference between adjacent DIDO clusters. As illustrated in Figure 1, we assume the main DIDO cluster hosts the client (i.e. a user device served by the multi-user DIDO system) affected by interference (or target client) from the neighbor clusters.
• neighboring clusters operate at different frequencies according to frequency division multiple access (FDMA) techniques similar to conventional cellular systems. For example, with frequency reuse factor of 3, the same carrier frequency is reused every third DIDO cluster as illustrated in Figure 2. In Figure 2, the different carrier frequencies are identified as Fi, F 2 and F 3 . While this embodiment may be used in some implementations, this solution yields loss in spectral efficiency since the available spectrum is divided in multiple subbands and only a subset of DIDO clusters operate in the same subband. Moreover, it requires complex cell planning to associate different DIDO clusters to different frequencies, thereby preventing interference. Like prior art cellular systems, such cellular planning requires specific placement of antennas and limiting of transmit power to as to avoid interference between clusters using the same frequency.
• FDMA frequency division multiple access
• neighbor clusters operate in the same frequency band, but at different time slots according to time division multiple access (TDMA) technique.
• TDMA time division multiple access
• DIDO transmission is allowed only in time slots T ls T 2 , and T 3 for certain clusters, as illustrated.
• Time slots can be assigned equally to different clusters, such that different clusters are scheduled according to a Round-Robin policy.
• different clusters are characterized by different data rate requirements (i.e., clusters in crowded urban environments as opposed to clusters in rural areas with fewer number of clients per area of coverage), different priorities are assigned to different clusters such that more time slots are assigned to the clusters with larger data rate requirements.
• TDMA as described above may be employed in one embodiment of the invention, a TDMA approach may require time synchronization across different clusters and may result in lower spectral efficiency since interfering clusters cannot use the same frequency at the same time.
• all neighboring clusters transmit at the same time in the same frequency band and use spatial processing across clusters to avoid interference.
• the multi-cluster DIDO system uses conventional DIDO precoding within the main cluster to transmit simultaneous non-interfering data streams within the same frequency band to multiple clients (such as described in the related patents and applications, including 7,599,420; 7,633,994; 7,636,381; and Application Serial No. 12/143,503); (ii) uses DIDO precoding with interference cancellation in the neighbor clusters to avoid interference to the clients lying in the interfering zones 8010 in Figure 4, by creating points of zero radio frequency (RF) energy at the locations of the target clients.
• RF radio frequency
• a target client If a target client is in an interfering zone 410, it will receive the sum of the RF containing the data stream from the main cluster 411 and the zero RF energy from the interfering cluster 412-413, which will simply be the RF containing the data stream from the main cluster.
• adjacent clusters can utilize the same frequency simultaneously without target clients in the interfering zone suffering from interference.
• DIDO precoding may be affected by different factors such as: channel estimation error or Doppler effects (yielding obsolete channel state information at the DIDO distributed antennas); intermodulation distortion (IMD) in multicarrier DIDO systems; time or frequency offsets.
• IMD intermodulation distortion
• the link performance at the target client is unaffected by the interference.
• the client requires 20dB signal-to-noise ratio (SNR) to demodulate 4-QAM constellations using forward error correction (FEC) coding to achieve target bit error rate (BER) of 10 "6 .
• SNR signal-to-noise ratio
• FEC forward error correction
• BER target bit error rate
• the term "zero RF energy” as used herein does not necessarily mean that the RF energy from interfering RF signals is zero. Rather, it means that the RF energy is sufficiently low relative to the RF energy of the desired RF signal such that the desired RF signal may be received at the receiver.
• certain desirable thresholds for interfering RF energy relative to desired RF energy are described, the underlying principles of the invention are not limited to any particular threshold values.
• interfering zones 8010 There are different types of interfering zones 8010 as shown in Figure 4. For example, "type A” zones (as indicated by the letter “A” in Figure 80) are affected by interference from only one neighbor cluster, whereas “type B” zones (as indicated by the letter “B”) account for interference from two or multiple neighbor clusters.
• FIG. 5 depicts a framework employed in one embodiment of the invention.
• the dots denote DIDO distributed antennas, the crosses refer to the DIDO clients and the arrows indicate the directions of propagation of RF energy.
• the DIDO antennas in the main cluster transmit precoded data signals to the clients MC 501 in that cluster.
• the DIDO antennas in the interfering cluster serve the clients IC 502 within that cluster via conventional DIDO precoding.
• the green cross 503 denotes the target client TC 503 in the interfering zone.
• the DIDO antennas in the main cluster 511 transmit precoded data signals to the target client (black arrows) via conventional DIDO precoding.
• the DIDO antennas in the interfering cluster 512 use precoding to create zero RF energy towards the directions of the target client 503 (green arrows).
• k ⁇ ,...,K, with K being the number of clients in the interfering zone 801 OA, B, U is the number of clients in the main DIDO cluster, C is the number of interfering DIDO clusters 412- 413 and I c is the number of clients in the interfering cluster c.
• r k G NxM is the vector containing the receive data streams at client k, assuming M transmit DIDO antennas and N receive antennas at the client devices; s k G C Wxl is the vector of transmit data streams to client k in the main DIDO cluster; s u G C Wxl is the vector of transmit data streams to client u in the main DIDO cluster; s c i G C Wxl is the vector of transmit data streams to client i in the c th interfering DIDO cluster; n k G C Wxl is the vector of additive white Gaussian noise (AWGN) at the N receive antennas of client k; k G NxM is the DIDO channel matrix from the M transmit DIDO antennas to the N receive antennas at client k in the main DIDO cluster; H c k G NxM is the DIDO channel matrix from the M transmit DIDO antennas to the N receive antennas t client k in the c th interfering DIDO cluster; W fc G
• the DIDO precoding weights are computed to pre-cancel inter-client interference within the same DIDO cluster.
• block diagonalization (BD) precoding described in the related patents and applications, including 7,599,420; 7,633,994; 7,636,381 ; and Application Serial No. 12/143,503 and [7] can be used to remove inter-client interference, such that the following condition is satisfied in the main cluster
• H c ,i where c i is the matrix obtained from the channel matrix H c G c (w / c) xM f or the interfering cluster c, where the rows corresponding to the i th client are removed.
• the precoding weights W c i in (1) computed in the neighbor clusters are designed to transmit precoded data streams to all clients in those clusters, while pre-cancelling interference to the target client in the interfering zone.
• the target client receives precoded data only from its main cluster.
• the same data stream is sent to the target client from both main and neighbor clusters to obtain diversity gain.
• the signal model in (5) is expressed as
• W c k is the DIDO precoding matrix from the DIDO transmitters in the c th cluster to the target client k in the interfering zone. Note that the method in (6) requires time synchronization across neighboring clusters, which may be complex to achieve in large systems, but nonetheless, is quite feasible if the diversity gain benefit justifies the cost of implementation.
• FEC forwards error correction
• SIR signal-to-interference
• FIG 9 shows the signal-to-interference-plus-noise ratio (SINR) as a function of D (i.e., as the target client moves from the main cluster 802 towards the DIDO antennas 813 in the interfering cluster 8403).
• the SINR is derived as the ratio of signal power and interference plus noise power using the signal model in (8).
• IDCI the wireless link performance is only affected by noise and the SINR decreases due to pathloss.
• the interference from the DIDO antennas in the neighbor cluster contributes to reduce the SINR.
• Figure 10 shows the symbol error rate (SER) performance of the three scenarios above for 4-QAM modulation in flat-fading narrowband channels.
• SER symbol error rate
• the frame structure can be designed with short periods of silence. For example, periods of silence can be defined between training for channel estimation and precoded data transmissions during channel state information (CSI) feedback.
• the interference-plus-noise signal power from neighbor clusters is measured during the periods of silence from the DIDO antennas in the main cluster.
• null tones are typically used to prevent direct current (DC) offset and attenuation at the edge of the band due to filtering at transmit and receive sides.
• the interference-plus-noise signal power is estimated from the null tones. Correction factors can be used to compensate for transmit/receive filter attenuation at the edge of the band.
• Ps signal-plus-interference-and-noise power
• P IN interference-plus-noise power from neighbor clusters
• the SINR estimate is derived from the received signal strength indication (RSSI) used in typical wireless communication systems to measure the radio signal power.
• RSSI received signal strength indication
• metric in (9) cannot discriminate between noise and interference power level.
• clients affected by shadowing i.e., behind obstacles that attenuate the signal power from all DIDO distributed antennas in the main cluster
• SIR (10) where P N is the noise power.
• the noise power P in (10) is estimated from the null tones, assuming all DIDO antennas from main and neighbor clusters use the same set of null tones.
• the interference-plus-noise power (P IN ) is estimated from the period of silence as mentioned above.
• the signal-plus-interference-and-noise power (Ps) is derived from the data tones. From these estimates, the client computes the SIR in (10).
• SIR T predefined threshold
• MCS modulation and FEC coding scheme
• orthogonal sequences are used for time synchronization in different DIDO clusters.
• channel estimation is carried out at 1103.
• IDCI Precoding 1104 Once the channel estimates are available at the DIDO BTS in the neighbor clusters, IDCI-precoding is computed to satisfy the condition in (3).
• the DIDO antennas in the neighbor clusters transmit precoded data streams only to the clients in their cluster, while pre-cancelling interference to the clients in the interfering zone 410 in Figure 4.
• pre-cancelling interference to the clients in the interfering zone 410 in Figure 4.
• the IDCI-precoder to remove inter-cluster interference described above is used as a baseline for handoff methods in DIDO systems.
• Conventional handoff in cellular systems is conceived for clients to switch seamlessly across cells served by different base stations. In DIDO systems, handoff allows clients to move from one cluster to another without loss of connection.
• Figure 12 shows the SINR variation as a function of the client's distance from the center of clusters CI .
• target SINR 20dB.
• the line identified by circles represents the SINR for the target client being served by the DIDO antennas in CI, when both CI and C2 use DIDO precoding without interference cancellation.
• the SINR decreases as a function of D due to pathloss and interference from the neighboring cluster.
• IDCI-precoding is implemented at the neighboring cluster
• the SINR loss is only due to pathloss (as shown by the line with triangles), since interference is completely removed. Symmetric behavior is experienced when the client is served from the neighboring cluster.
• One embodiment of the handoff strategy is defined such that, as the client moves from CI to C2, the algorithm switches between different DIDO schemes to maintain the SINR above predefined target.
• One embodiment of the handoff strategy is as follows.
• SINR keeps decreasing until it again reaches a target.
• the client decides to switch to the neighbor cluster.
• CI starts using the CSI from the target client to create zero interference towards its direction with IDCI-precoding
• the neighbor cluster uses the CSI for conventional DIDO-precoding.
• the clusters CI and C2 try both DIDO- and IDCI-precoding schemes alternatively, to allow the client to estimate the SIR in both cases. Then the client selects the best scheme, to maximize certain error rate performance metric.
• the cross-over point for the handoff strategy occurs at the intersection of the curves with triangles and rhombus in Figure 12.
• One embodiment uses the modified IDCI-precoding method described in (6) where the neighbor cluster also transmits precoded data stream to the target client to provide array gain.
• the handoff strategy is simplified, since the client does not need to estimate the SINR for both strategies at the cross-over point.
• the method described above computes the SINR or SIR estimates for different schemes in real time and uses them to select the optimal scheme.
• the handoff algorithm is designed based on the finite-state machine illustrated in Figure 14. The client keeps track of its current state and switches to the next state when the SINR or SIR drops below or above the predefined thresholds illustrated in Figure 12.
• both clusters CI and C2 operate with conventional DIDO precoding independently and the client is served by cluster CI; in state 1202, the client is served by cluster CI, the BTS in C2 computes IDCI-precoding and cluster C 1 operates using conventional DIDO precoding; in state 1203, the client is served by cluster C2, the BTS in CI computes IDCI-precoding and cluster C2 operates using conventional DIDO precoding; and in state 1204, the client is served by cluster
• the signal quality or SIR may fluctuate around the thresholds as shown in Figure 15, causing repetitive switching between consecutive states in Figure 14. Changing states repetitively is an undesired effect, since it results in significant overhead on the control channels between clients and BTSs to enable switching between transmission schemes.
• Figure 15 depicts one example of a handoff strategy in the presence of shadowing.
• the shadowing coefficient is simulated according to log-normal distribution with variance 3 [3].
• One embodiment of the invention employs a hysteresis loop to cope with state switching effects. For example, when switching between "Cl-DIDO,C2-IDCI” 9302 and “Cl- IDCI,C2-DIDO” 9303 states in Figure 14 (or vice versa) the threshold SINR T i can be adjusted within the range Ai. This method avoids repetitive switches between states as the signal quality oscillates around SINR TI .
• Figure 16 shows the hysteresis loop mechanism when switching between any two states in Figure 14. To switch from state B to A the SIR must be larger than (SIR T i+Ai/2), but to switch back from A to B the SIR must drop below (SIR T i-Ai/2).
• the threshold SINR T2 is adjusted to avoid repetitive switching between the first and second (or third and fourth) states of the finite-state machine in Figure 14.
• a range of values A 2 may be defined such that the threshold SINR T2 is chosen within that range depending on channel condition and shadowing effects.
• the SINR threshold is dynamically adjusted within the range [SINR T2 , SINR T2 +A 2 ].
• the variance of the log-normal distribution can be estimated from the variance of the received signal strength (or RSSI) as the client moves from its current cluster to the neighbor cluster.
• the handoff decision is deferred to the DIDO BTSs, assuming communication across multiple BTSs is enabled.
• the methods above are derived assuming no FEC coding and 4-QAM. More generally, the SINR or SIR thresholds are derived for different modulation coding schemes (MCSs) and the handoff strategy is designed in combination with link adaptation ⁇ see, e.g., U.S. Patent No. 7,636,381) to optimize downlink data rate to each client in the interfering zone.
• MCSs modulation coding schemes
• DIDO systems employ closed-loop transmission schemes to precode data streams over the downlink channel. Closed-loop schemes are inherently constrained by latency over the feedback channel. In practical DIDO systems, computational time can be reduced by transceivers with high processing power and it is expected that most of the latency is introduced by the DIDO BSN, when delivering CSI and baseband precoded data from the BTS to the distributed antennas.
• the BSN can be comprised of various network technologies including, but not limited to, digital subscriber lines (DSL), cable modems, fiber rings, Tl lines, hybrid fiber coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi).
• DSL and cable modem connections typically have between 10-25ms in last-mile latency in the United States, but they are very widely deployed.
• the maximum latency over the BSN determines the maximum Doppler frequency that can be tolerated over the DIDO wireless link without performance degradation of DIDO precoding. For example, in [1] we showed that at the carrier frequency of 400MHz, networks with latency of about 10msec (i.e., DSL) can tolerate clients' velocity up to 8mph (running speed), whereas networks with 1msec latency (i.e., fiber ring) can support speed up to 70mph (i.e., freeway traffic).
• a low- Doppler DIDO network consists of a typically larger number of DIDO antennas with relatively low power (i.e., 1W to 100W, for indoor or rooftop installation) spread across a wide area
• a high-Doppler network consists of a typically lower number of DIDO antennas with high power transmission (i.e., 100W for rooftop or tower installation).
• the low-Doppler DIDO network serves the typically larger number of low-Doppler users and can do so at typically lower connectivity cost using inexpensive high-latency broadband connections, such as DSL and cable modems.
• the high-Doppler DIDO network serves the typically fewer number of high-Doppler users and can do so at typically higher connectivity cost using more expensive low-latency broadband connections, such as fiber.
• TDMA time division multiple access
• FDMA frequency division multiple access
• CDMA code division multiple access
• fa ⁇ s B (1 1)
• fd the maximum Doppler shift
• ⁇ the wavelength corresponding to the carrier frequency
• ⁇ the angle between the vector indicating the direction transmitter-client and the velocity vector.
• the Doppler shift of every client is calculated via blind estimation techniques.
• the Doppler shift can be estimated by sending F energy to the client and analyzing the reflected signal, similar to Doppler radar systems.
• one or multiple 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 zero-crossing rate of the channel gain, or performing spectrum analysis.
• the angular velocity v sin ⁇ in (1 1) may depend on the relative distance of the client from every DIDO antenna. For example, DIDO antennas in the proximity of a moving client yield larger angular velocity and Doppler shift than faraway antennas.
• the Doppler velocity is estimated from multiple DIDO antennas at different distances from the client and the average, weighted average or standard deviation is used as an indicator for the client's mobility. Based on the estimated Doppler indicator, the DIDO BTS decides whether to assign the client to low- or high-Doppler networks.
• the Doppler indicator is periodically monitored for all clients and sent back to the BTS. When one or multiple clients change their Doppler velocity (i.e., client riding in the bus versus client walking or sitting), those clients are dynamically re-assigned to different DIDO network that can tolerate their level of mobility.
• the Doppler of low-velocity clients can be affected by being in the vicinity of high-velocity objects (e.g. near a highway), the Doppler is typically far less than the Doppler of clients that are in motion themselves.
• the velocity of the client is estimated (e.g. by using a means such as monitoring the clients position using GPS), and if the velocity is low, the client is assigned to a low-Doppler network, and if the velocity if high, the client is assigned to a high-Doppler network.
• FIG. 17 The block diagram of DIDO systems with power control is depicted in Figure 17.
• One or multiple data streams (3 ⁇ 4) for every client ( ⁇ ,...,U) are first multiplied by the weights generated by the DIDO precoding unit.
• Precoded data streams are multiplied by power scaling factor computed by the power control unit, based on the input channel quality information (CQI).
• CQI channel quality information
• the CQI is either fed back from the clients to DIDO BTS or derived from the uplink channel assuming uplink-downlink channel reciprocity.
• the U precoded streams for different clients are then combined and multiplexed into M data streams (t m ), one for each of the M transmit antennas.
• the streams t m are sent to the digital-to-analog converter (DAC) unit, the radio frequency (RF) unit, power amplifier (PA) unit and finally to the antennas.
• DAC digital-to-analog converter
• RF radio frequency
• PA power amplifier
• the power control unit measures the CQI for all clients.
• the CQI is the average SNR or RSSI.
• the CQI varies for different clients depending on pathloss or shadowing.
• Our power control method adjusts the transmit power scaling factors P k for different clients and multiplies them by the precoded data streams generated for different clients. Note that one or multiple data streams may be generated for every client, depending on the number of clients' receive antennas.
• Figure 18 shows the SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios.
• the plot with squares refers to the case where clients have different pathloss coefficients and no power control.
• the power control method more power is assigned to the data streams intended to the clients that undergo higher pathloss/shadowing, resulting in 9dB SN gain (for this particular scenario) compared to the case with no power control.
• FCC Federal Communications Commission
• EM electromagnetic
• DIDO distributed antennas used for indoor/outdoor applications qualify for the FCC category of "mobile" devices, defined as [2]:
• MPE maximum permissible exposure
• Low-power (LP) transmitters located anywhere (i.e., indoor or outdoor) at any height, with maximum transmit power of 1W and 5Mbps consumer-grade broadband (e.g. DSL, cable modem, Fibe To The Home (FTTH)) backhaul connectivity.
• DSL digitalSL, cable modem, Fibe To The Home
• High-power (HP) transmitters rooftop or building mounted antennas at height of approximately 10 meters, with transmit power of 100W and a commercial-grade broadband (e.g. optical fiber ring) backhaul (with effectively "unlimited” data rate compared to the throughput available over the DIDO wireless links).
• LP transmitters with DSL or cable modem connectivity are good candidates for low-Doppler DIDO networks (as described in the previous section), since their clients are mostly fixed or have low mobility.
• HP transmitters with commercial fiber connectivity can tolerate higher client's mobility and can be used in high-Doppler DIDO networks.
• N HP 50 high-power transmitters.
• FIG. 21a and 21b show two power distributions corresponding to the configurations in Figure 20a and Figure 20b, respectively.
• the received power distribution (expressed in dBm) is derived assuming the pathloss/shadowing model for urban environments defined by the 3 GPP standard [3] at the carrier frequency of 700MHz. We observe that using 50% of HP transmitters yields better coverage over the selected area.
• Figures 22a-b depict the rate distribution for the two scenarios above.
• the throughput (expressed in Mbps) is derived based on power thresholds for different modulation coding schemes defined in the 3GPP long-term evolution (LTE) standard in [4,5].
• LTE long-term evolution
• the total available bandwidth is fixed to 10MHz at 700MHz carrier frequency.
• Two different frequency allocation plans are considered: i) 5MHz spectrum allocated only to the LP stations; ii) 9MHz to HP transmitters and lMHz to LP transmitters. Note that lower bandwidth is typically allocated to LP stations due to their DSL backhaul connectivity with limited throughput.
• Figures 22a-b shows that when using 50% of HP transmitters it is possible to increase significantly the rate distribution, raising the average per-client data rate from 2.4Mbps in Figure 22a to 38Mbps in Figure 22b.
• DIDO antennas can be conceived as inexpensive wireless devices (similar to WiFi access points) and can be placed anywhere there is DSL, cable modem, optical fiber, or other Internet connectivity.
• FIG. 23 The framework of DIDO systems with adaptive per-antenna power control is depicted in Figure 23.
• the amplitude of the digital signal coming out of the multiplexer 234 is dynamically adjusted with power scaling factors Si, ... ,S M , before being sent to the DAC units 235.
• the power scaling factors are computed by the power control unit 232 based on the CQI 233.
• N g DIDO antenna groups are defined. Every group contains at least as many DIDO antennas as the number of active clients (K). At any given time, only one group has N a >K active DIDO antennas transmitting to the clients at larger power level (S 0 ) than
• MPE MPE limit
• One method iterates across all antenna groups according to Round-Robin scheduling policy depicted in Figure 24.
• different scheduling techniques i.e., proportional-fair scheduling [8] are employed for cluster selection to optimize error rate or throughput performance.
• the ratio in (15) is the duty factor (DF) of the groups, defined such that the average transmit power from every DIDO antenna satisfies the
• MPE MPE limit
• the total transmit power from all N a of all N g groups is defined as
• Sj j (t) is the power spectral density for the i transmit antenna within the j group.
• the power spectral density in (19) is designed for every antenna to optimize error rate or throughput performance.
• Figure 25 compares the (uncoded) SER performance of the above power control with antenna grouping against conventional eigenmode selection in U.S. Patent No. 7,636,381. All schemes use BD precoding with four clients, each client equipped with single antenna.
• the SNR refers to the ratio of per-transmit-antenna power over noise power (i.e., per-antenna transmit SNR).
• the curve denoted with DIDO 4x4 assumes four transmit antenna and BD precoding.
• the curve with squares denotes the SER performance with two extra transmit antennas and BD with eigenmode selection, yielding lOdB SNR gain (at 1% SER target) over conventional BD precoding.
• DF 1/10.
• the antenna ID of every group can be pre-computed and shared among DIDO antennas and clients via lookup tables, such that only K channel estimates are required at any given time.
• K+2 channel estimates are computed and additional computational processing is required to select the eigenmode that minimizes the SER at any given time for all clients.
• Figure 26a shows one scenario where clients (dots) are spread randomly in one area covered by multiple DIDO distributed antennas (crosses). The average power over every transmit-receive wireless link can be computed as
• H the channel estimation matrix available at the DIDO BTS.
• FIG. 26a-c The matrices A in Figures 26a-c are obtained numerically by averaging the channel matrices over 1000 instances.
• Two alternative scenarios are depicted in Figure 26b and Figure 26c, respectively, where clients are grouped together around a subset of DIDO antennas and receive negligible power from DIDO antennas located far away.
• Figure 26b shows two groups of antennas yielding block diagonal matrix A.
• One extreme scenario is when every client is very close to only one transmitter and the transmitters are far away from one another, such that the power from all other DIDO antennas is negligible.
• the DIDO link degenerates in multiple SISO links and A is a diagonal matrix as in Figure 26c.
• the BD precoding dynamically adjusts the precoding weights to account for different power levels over the wireless links between DIDO antennas and clients. It is convenient, however, to identify multiple groups within the DIDO cluster and operate DIDO precoding only within each group. Our proposed grouping method yields the following advantages:
• SVD singular value decomposition
• the CSI is computed from the clients to the antennas only within the same group.
• antenna grouping reduces the number of channel estimates to compute the channel matrix H.
• antenna grouping further yields reduction of CSI feedback overhead over the wireless links between DIDO antennas and clients.
• different multiple access techniques are defined for the DIDO uplink channel. These techniques can be used to feedback the CSI or transmit data streams from the clients to the DIDO antennas over the uplink.
• feedback CSI and data streams are uplink streams.
• MIMO Multiple-input multiple-output
• the uplink streams are transmitted from the client to the DIDO antennas via open-loop MIMO multiplexing schemes. This method assumes all clients are time/frequency synchronized. In one embodiment, synchronization among clients is achieved via training from the downlink and all DIDO antennas are assumed to be locked to the same time/frequency reference clock. Note that variations in delay spread at different clients may generate jitter between the clocks of different clients that may affect the performance of MIMO uplink scheme.
• the receive DIDO antennas may use non-linear (i.e., maximum likelihood, ML) or linear (i.e., zeros-forcing, minimum mean squared error) receivers to cancel co-channel interference and demodulate the uplink streams individually.
• ML maximum likelihood
• linear i.e., zeros-forcing, minimum mean squared error
• Time division multiple access Different clients are assigned to different time slots. Every client sends its uplink stream when its time slot is available.
• Frequency division multiple access Different clients are assigned to different carrier frequencies.
• OFDM multicarrier
• CDMA Code division multiple access
• the clients are wireless devices that transmit at much lower power than the DIDO antennas.
• the DIDO BTS defines client subgroups based on the uplink SN information, such that interference across sub-groups is minimized.
• the above multiple access techniques are employed to create orthogonal channels in time, frequency, space or code domains thereby avoiding uplink interference across different clients.
• the uplink multiple access techniques described above are used in combination with antenna grouping methods presented in the previous section to define different client groups within the DIDO cluster.
• the PDP in (21) is normalized such that the total average power for all L channel taps is unitary
• the first subscript indicates the client, the second subscript the transmit antenna.
• the continuous line in Figure 29 refers to client 1, whereas the line with dots refers to client 2.
• LA time/frequency domain link adaptation
• Figure 30 shows the SER for different QAM schemes (i.e., 4-QAM, 16-QAM, 64- QAM).
• target SER 1% for uncoded systems.
• the SNR thresholds to meet that target SER in AWGN channels are 8dB, 15.5dB and 22dB for the three modulation schemes, respectively.
• the SNR thresholds are: 18.6dB, 27.3dB and 34.1dB, respectively.
• DIDO precoding transforms the multi-user downlink channel into a set of parallel SISO links.
• the same SNR thresholds as in Figure 30 for SISO systems hold for DIDO systems on a client-by-client basis.
• the thresholds in AWGN channels are used.
• the DIDO BTS computes the CSI from all users. Users may be equipped with single or multiple receive antennas.
• DIDO precoding At 3172, the BTS computes the DIDO precoding weights for all users.
• BD is used to compute these weights.
• the precoding weights are calculated on a tone-by-tone basis.
• Link-quality metric calculation At 3173 the BTS computes the frequency-domain link quality metrics.
• the metrics are calculated from the CSI and DIDO precoding weights for every tone.
• the link-quality metric is the average SNR over all OFDM tones.
• LAI based on average SNR performance
• the link quality metric is the frequency response of the effective channel in (23).
• LA2 based on tone-by-tone performance to exploit frequency diversity. If every client has single antenna, the frequency-domain effective channel is depicted in Figure 29. If the clients have 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 the singular values of the effective channel matrix in (23).
• the BTS determines the MCSs for different clients and different OFDM tones.
• the same MCS is used for all clients and all OFDM tones based on the SN thresholds for Rayleigh fading channels in Figure 30.
• different MCSs are assigned to different OFDM tones to exploit channel frequency diversity.
• the BTS transmits precoded data streams from the DIDO distributed antennas to the clients using the MCSs derived from the bit-loading algorithm.
• multiple subcarriers are grouped into subbands and the same MCS is assigned to all tones in the same subband to reduce the overhead due to control information.
• the MCS are adjusted based on temporal variations of the channel gain (proportional to the coherence time). In fixed-wireless channel (characterized by low Doppler effect) the MCS are recalculated every fraction of the channel coherence time, thereby reducing the overhead required for control information.
• Figure 32 shows the SER performance of the LA methods described above. For comparison, the SER performance in Rayleigh fading channels is plotted for each of the three QAM schemes used.
• the computational complexity of DIDO systems is mostly localized at the centralized processor or BTS.
• the most computationally expensive operation is the calculation of the precoding weights for all clients from their CSI.
• the BTS has to carry out as many singular value decomposition (SVD) operations as the number of clients in the system.
• SVD singular value decomposition
• One way to reduce complexity is through parallelized processing, where the SVD is computed on a separate processor for every client.
• each subcarrier undergoes flat-fading channel and the SVD is carried out for every client over every subcarrier.
• the complexity of the system increases linearly with the number of subcarriers.
• the cyclic prefix (L 0 ) must have at least eight channel taps (i.e., duration of 8 microseconds) to avoid intersymbol interference in outdoor urban macrocell environments with large delay spread [3].
• the size (NFFT) of the fast Fourier transform (FFT) used to generate the OFDM symbols is typically set to multiple of Lo to reduce loss of data rate.
• One way to reduce computational complexity at the DIDO precoder is to carry out the SVD operation over a subset of tones (that we call pilot tones) and derive the precoding weights for the remaining tones via interpolation.
• Weight interpolation is one source of error that results in inter-client interference.
• optimal weight interpolation techniques are employed to reduce inter-client interference, yielding improved error rate performance and lower computational complexity in multicarrier systems.
• the condition for the precoding weights of the k th client (W fc ) that guarantees zero interference to the other clients u is derived from (2) as
• the objective function of the weight interpolation method is defined as
• ⁇ /copt arg mine fe D0fe f(B fc ) (26) where 0 3 ⁇ 4 . is the feasible set of the optimization problem and ⁇ 3 ⁇ 4 . opt is the optimal solution.
• the objective function in (25) is defined for one OFDM tone.
• the objective function is defined as linear combination of the Frobenius norm in (25) of the matrices for all the OFDM tones to be interpolated.
• the OFDM spectrum is divided into subsets of tones and the optimal solution is given by
• ⁇ /copt ar ⁇ min e fe D0 fe ma3 ⁇ 4DA f(n, ⁇ 3 ⁇ 4 ) (27) where n is the OFDM tone index and A is the subset of tones.
• the weight interpolation matrix W fc (e fc ) in (25) is expressed as a function of a set of parameters Q k . Once the optimal set is determined according to (26) or (27), the optimal weight matrix is computed.
• the weight interpolation matrix of given OFDM tone n is defined as linear combination of the weight matrices of the pilot tones.
• weight interpolation function for beamforming systems with single client was defined in [1 1]. In DIDO multi-client systems we write the weight interpolation matrix as
• the weight matrix in (28) is then normalized such that
• the pilot tones are chosen uniformly within the range of the OFDM tones. In another embodiment, the pilot tones are adaptively chosen based on the CSI to minimize the interpolation error.
• the solid lines in Figure 33 represent the ideal functions, whereas the dotted lines are the interpolated ones.
• the interpolated weights match the ideal ones for the pilot tones, according to the definition in (28).
• the weights computed over the remaining tones only approximate the ideal case due to estimation error.
• One way to implement the weight interpolation method is via exhaustive search over the feasible set 0 3 ⁇ 4 .
• the number of clients is the same as the number of transmit antennas and every client is equipped with single antenna. As the number of clients increases the SER performance degrades due to increase inter-client interference produced by weight interpolation errors.
• weight interpolation functions other than those in (28) are used.
• linear prediction autoregressive models [12] can be used to interpolate the weights across different OFDM tones, based on estimates of the channel frequency correlation.
• the user cluster is a subset of transmitting antennas whose signal can be reliably detected by given user (i.e., received signal strength above noise or interference level). Every user in the system defines its own user-cluter.
• the waveforms sent by the transmitting antennas within the same user-cluster coherently combine to create RF energy at the target user's location and points of zero RF interference at the location of any other user reachable by those antennas.
• the precoding weights (w fc G C xl ) that create RF energy to user k and zero RF energy to all other K- ⁇ users are computed to satisfy the following condition
• k is the effective channel matrix of user k obtained by removing the k-t row of matrix H and 0 ⁇ ⁇ 1 is the vector with all zero entries
• the wireless system is a DIDO system and user clustering is employed to create a wireless communication link to the target user, while pre-cancelling interference to any other user reachable by the antennas lying within the user-cluster.
• a DIDO system is described which includes:
• DIDO clients user terminals equipped with one or multiple antennas
• DIDO distributed antennas transceiver stations operating cooperatively to transmit precoded data streams to multiple users, thereby suppressing inter-user interference
• BTS base transceiver stations
• BSN DIDO base station network
• the DIDO distributed antennas are grouped into different subsets depending on their spatial distribution relative to the location of the BTSs or DIDO clients.
• Super-cluster 3640 is the set of DIDO distributed antennas connected to one or multiple BTSs such that the round-trip latency between all BTSs and the respective users is within the constraint of the DIDO precoding loop;
• DIDO-cluster 3641 is the set of DIDO distributed antennas connected to the same BTS.
• User-cluster 3642 is the set of DIDO distributed antennas that cooperatively transmit precoded data to given user.
• the BTSs are local hubs connected to other BTSs and to the DIDO distributed antennas via the BSN.
• the BSN can be comprised of various network technologies including, but not limited to, digital subscriber lines (DSL), ADSL, VDSL [6], cable modems, fiber rings, Tl lines, hybrid fiber coaxial (HFC) networks, and/or fixed wireless (e.g., WiFi). All BTSs within the same super-cluster share information about DIDO precoding via the BSN such that the round-trip latency is within the DIDO precoding loop.
• One embodiment of a method employing user clustering consists of the following steps:
• Link-quality measurements the link quality between every DIDO distributed antenna and every user is reported to the BTS.
• the link-quality metric consists of signal-to-noise ratio (SN ) or signal-to-interference-plus-noise ratio (SINR).
• the DIDO distributed antennas transmit training signals and the users estimate the received signal quality based on that training.
• the training signals are designed to be orthogonal in time, frequency or code domains such that the users can distinguish across different transmitters.
• the DIDO antennas transmit narrowband signals (i.e., single tone) at one particular frequency (i.e., a beacon channel) and the users estimate the link-quality based on that beacon signal.
• One threshold is defined as the minimum signal amplitude (or power) above the noise level to demodulate data successfully as shown in Figure 38a. Any link- quality metric value below this threshold is assumed to be zero.
• the link-quality metric is quantized over a finite number of bits and fed back to the transmitter.
• the training signals or beacons are sent from the users and the link quality is estimated at the DIDO transmit antennas (as in Figure 38b), assuming reciprocity between uplink (UL) and downlink (DL) pathloss.
• pathloss reciprocity is a realistic assumption in time division duplexing (TDD) systems (with UL and DL channels at the same frequency) and frequency division duplexing (FDD) systems when the UL and DL frequency bands are reatively close.
• the link-quality metrics of all wireless links in the DIDO clusters are the entries to the link-quality matrix shared across all BTSs via the BSN.
• link-quality matrix for the scenario in Figure 37 is depicted in Figure 39.
• the link-quality matrix is used to define the user clusters.
• Figure 39 shows the selection of the user cluster for user U8.
• the subset of transmitters with non-zero link-quality metrics (i.e., active transmitters) to user U8 is first identified. These transmitters populate the user-cluster for the user U8. Then the sub-matrix containing non-zero entries from the transmitters within the user-cluster to the other users is selected.
• the link-quality metrics are only used to select the user cluster, they can be quantized with only two bits (i.e., to identify the state above or below the thresholds in Figure 38) thereby reducing feedback overhead.
• FIG. 40 Another example is depicted in Figure 40 for user Ul.
• the number of active transmitters is lower than the number of users in the sub-matrix, thereby violating the condition K ⁇ M. Therefore, one or more columns are added to the sub-matrix to satisfy that condition. If the number of transmitters exceeds the number of users, the extra antennas can be used for diversity schemes (i.e., antenna or eigenmode selection).
• CSI report to the BTSs Once the user clusters are selected, the CSI from all transmitters within the user-cluster to every user reached by those transmitters is made available to all BTSs. The CSI information is shared across all BTSs via the BSN. In TDD systems, UL/DL channel reciprocity can be exploited to derive the CSI from training over the UL channel. In FDD systems, feedback channels from all users to the BTSs are required. To reduce the amount of feedback, only the CSI corresponding to the non-zero entries of the link-quality matrix are fed back.
• DIDO precoding is applied to every CSI sub-matrix corresponding to different user clusters (as described, for example, in the related U.S. Patent Applications).
• singular value decomposition (SVD) of the effective channel matrix H k is computed and the precoding weight w k for user k is defined as the right sigular vector corresponding to the null subspace of K .
• the DIDO precoding weight for user k is given by
• U 0 is the matrix with columns being the singular vectors of the null subspace of K .
• u x is the vector with real entries equal to 1 (i.e., the precoding weight vector is the sum of the columns of C -1 ).
• the DIDO precoding calculation requires one matrix inversion.
• matrix inversions such as the Strassen's algorithm [1] or the Coppersmitb-Winograd's algorithm [2,3], Since C is Hermitian matrix by definition, an alternative solution is to decompose C in its real and imaginary components and compute matrix inversion of a real matrix, according to the method in [4, Section 11.4].
• Another feature of the proposed method and system is its reconfigurability. As the client moves across different DIDO clusters as in Figure 42, the user-cluster follows its moves. In other words, the subset of transmit antennas is constantly updated as the client changes its position and the effective channel matrix (and corresponding precoding weights) are recomputed.
• MAS multiple antenna systems
• Spatial diversity is determined by the distribution of scattering objects in the wireless channel as well as the geometry of transmit and receive antenna arrays.
• Clustered channel model defines groups of scatterers as clusters located around the transmitters and receivers.
• clustered channel models have been validated through practical measurements [1-2] and variations of those models have been adopted by different indoor (i.e., IEEE 802.11 ⁇ Technical Group [3] for WLAN) and outdoor (3GPP Technical Specification Group for 3G cellular systems [4]) wireless standards.
• antenna element spacing [5-7] number of antennas [8-9]
• array aperture [10-11] array geometry [5,12,13], polarization and antenna pattern [14-28].
• x(p) G C 3 is the polarized vector describing the transmit signal
• p, q G R 3 are the polarized vector positions describing the transmit and receive arrays, respectively
• a t (- , ⁇ ) , A r (- , ⁇ ) G C 3x3 are the transmit and receive array responses respectively
• H(m, n) G C 3x3 is the channel response matrix with entries being the complex gains between transmit direction n and receive direction m.
• user devices may have single or multiple antennas. For the sake of simplicity, we assume single antenna receivers with ideal isotropic patterns and rewrite the system response matrix as
• A(n, p) (I - nn") a(n, p)
• P is the space that defines the antenna array
• A is the area of the spherical array
• Figure 45 depicts another example where the array size covers even larger area than Figure 44, yielding additional degrees of freedom.
• the array aperture can be approximated by the total area covered by all DIDO transmitters (assuming antennas are spaced fractions of wavelength apart). Then Figure 45 shows that DIDO systems can achieve increasing numbers of degrees of freedom by distributing antennas in space, thereby reducing the size of the areas of coherence. Note that these figures are generated assuming ideal spherical arrays. In practical scenarios, DIDO antennas spread random across wide areas and the resulting shape of the areas of coherence may not be as regular as in the figures.
• Figure 46 shows that, as the array size increases, more clusters are included within the wireless channel as radio waves are scatterered by increasing number of objects between DIDO transmitters. Hence, it is possible to excite an increasing number of basis functions (that span the radiated field) , yielding additional degrees of freedom, in agreement with the definition above.
• the multi-user (MU) multiple antenna systems (MAS) described in this patent application exploit the area of coherence of wireless channels to create multiple simultaneous independent non- interfering data streams to different users. For given channel conditions and user distribution, the basis functions of the radiated field are selected to create independent and simultaneous wireless links to different users in such a way that every user experiences interference-free links. As the MU-MAS is aware of the channel between every transmitter and every user, the precoding transmission is adjusted based on that information to create separate areas of coherence to different users.
• the MU-MAS employs non-linear precoding, such as dirty-paper coding (DPC) [30-31] or Tomlinson-Harashima (TH) [32-33] precoding.
• the MU-MAS employs non-linear precoding, such as block diagonalization (BD) as in our previous patent applications [0003-0009] or zero-forcing beamforming (ZF-BF) [34].
• BD block diagonalization
• ZF-BF zero-forcing beamforming
• the MU-MAS requires knowledge of the channel state information (CSI).
• the CSI is made available to the MU-MAS via a feedback channel or estimated over the uplink channel, assuming uplink/downlink channel reciprocity is possible in time division duplex (TDD) systems.
• TDD time division duplex
• the MU-MAS uses limited feedback techniques to reduce the CSI overhead of the control channel. Codebook design is critical in limited feedback techniques.
• One embodiment defines the codebook from the basis functions that span the radiated field of the transmit array.
• the areas of coherence change their locations and shape. This is due to well know Doppler effect in wireless communications.
• the MU-MAS described in this patent application adjusts the precoding to adapt the areas of coherence constantly for every user as the environment changes due to Doppler effects. This adaptation of the areas of coherence is such to create simultaneous non-interfering channels to different users.
• Another embodiment of the invention adaptively selects a subset of antennas of the MU-MAS system to create areas of coherence of different sizes. For example, if the users are sparsely distributed in space (i.e., rural area or times of the day with low usage of wireless resources), only a small subset of antennas is selected and the size of the area of coherence are large relative to the array size as in Figure 43. Alternatively, in densely populated areas (i.e., urban areas or time of the day with peak usage of wireless services) more antennas are selected to create small areas of coherence for users in direct vicinity of each other.
• densely populated areas i.e., urban areas or time of the day with peak usage of wireless services
• the MU-MAS is a DIDO system as described in previous patent applications [0003-0009].
• the DIDO system uses linear or non-linear precoding and/or limited feedback techniques to create area of coherence to different users.
• Figure 47 shows the degrees of freedom of MIMO systems in practical indoor and outdoor propagation scenarios. For example, considering linear arrays with ten antennas spaced one wavelength apart, the maximum degrees of freedom (or number of spatial channels) available over the wireless link is limited to about 3 for outdoor scenarios and 7 for indoor. Of course, indoor channels provide more degrees of freedom due to the larger angular spread.
• Figure 48 shows the degrees of freedom in DIDO systems as a function of the array diameter.
• a diameter equal to ten wavelengths
• 1000 degrees of freedom are available in the DIDO system.
• the increased spatial diversity due to distributed antennas in space is the key to the multiplexing gain provided by DIDO over conventional MIMO systems.
• the number of channels allocated by the FCC grew from the initial 333 in 1983 to 416 in the late 1980s to support the increasing number of cellular clients. More recently, the commercialization of technologies like Wi-Fi, Bluetooth and ZigBee has been possible with the use of the unlicensed ISM band allocated by the FCC back in 1985 [17].
• DIDO distributed-input distributed-output
• Spectrum reconfigurability to enable new types of wireless operations (i.e., licensed vs. unlicensed) and/or meet new RF power emission limits. This feature allows spectrum auctions whenever is necessary, without need to plan in advance for use of licensed versus unlicensed spectrum. It also allows transmit power levels to be adjusted to meet new power emission levels enforced by the FCC.
• One embodiment of the system consists of one or multiple centralized processors (CP) 4901-4904 and one or multiple distributed nodes (DN) 4911-4913 that communicate via wireline or wireless connections as depicted in Figure 49.
• the centralized processor is the access core gateway (ACGW) connected to several Node B transceivers.
• the centralized processor is the internet service provider (ISP) and the distributed nodes are Wi-Fi access points connected to the ISP via modems or direct connection to cable or DSL.
• ISP internet service provider
• the system is a distributed-input distributed-output (DIDO) system [0002-0009] with one centralized processor (or BTS) and distributed nodes being the DIDO access points (or DIDO distributed antennas connected to the BTS via the BSN).
• DIDO distributed-input distributed-output
• the DNs 4911-4913 communicate with the CPs 4901-4904.
• the information exchanged from the DNs to the CP is used to dynamically adjust the configuration of the nodes to the evolving design of the network architecture.
• the DNs 4911-4913 share their identification number with the CP.
• the CP store the identification numbers of all DNs connected through the network into lookup tables or shared database. Those lookup tables or database can be shared with other CPs and that information is synchronized such that all CPs have always access to the most up to date information about all DNs on the network.
• the FCC may decide to allocate a certain portion of the spectrum to unlicensed use and the proposed system may be designed to operate within that spectrum. Due to scarcity of spectrum, the FCC may subsequently need to allocate part of that spectrum to licensed use for commercial carriers (i.e., AT&T, Verizon, or Sprint), defense, or public safety. In conventional wireless systems, this coexistence would not be possible, since existing wireless devices operating in the unlicensed band would create harmful interference to the licensed RF transceivers.
• the distributed nodes exchange control information with the CPs 4901-4903 to adapt their RF transmission to the evolving band plan.
• the DNs 4911-4913 were originally designed to operate over different frequency bands within the available spectrum.
• the CPs exchange control information with the unlicensed DNs and reconfigure them to shut down the frequency bands for licensed use, such that the unlicensed DNs do not interfere with the licensed DNs.
• This scenario is depicted in Figure 50 where the unlicensed nodes (e.g., 5002) are indicated with solid circles and the licensed nodes with empty circles (e.g., 5001).
• the whole spectrum can be allocated to the new licensed service and the control information is used by the CPs to shut down all unlicensed DNs to avoid interference with the licensed DNs.
• Figure 51 where the obsolete unlicensed nodes are covered with a cross.
• the wireless system may originally be designed for fixed wireless links with the DNs 4911-4913 connected to outdoor rooftop transceiver antennas. Subsequently, the same system may be updated to support DNs with indoor portable antennas to offer better indoor coverage.
• the FCC exposure limits of portable devices are more restrictive than rooftop transmitters, due to possibly closer proximity to the human body. In this case, the old DNs designed for outdoor applications can be re-used for indoor applications as long as the transmit power setting is adjusted.
• the DNs are designed with predefined sets of transmit power levels and the CPs 4901-4903 send control information to the DNs 4911-4913 to select new power levels as the system is upgraded, thereby meeting the FCC exposure limits.
• the DNs are manufactured with only one power emission setting and those DNs exceeding the new power emission levels are shut down remotely by the CP.
• the CPs 4901-4903 monitor periodically all DNs 4911-4913 in the network to define their entitlement to operate as F transceivers according to a certain standard. Those DNs that are not up to date can be marked as obsolete and removed from the network. For example, the DNs that operate within the current power limit and frequency band are kept active in the network, and all the others are shut down. Note that the DN parameters controlled by the CP are not limited to power emission and frequency band; it can be any parameter that defines the wireless link between the DN and the client devices.
• the DNs 4911-4913 can be reconfigured to enable the coexistence of different standard systems within the same spectrum.
• the power emission, frequency band or other configuration parameters of certain DNs operating in the context of WLAN can be adjusted to accommodate the adoption of new DNs designed for WPAN applications, while avoiding harmful interference.
• the DNs 4911-4913 can be updated to support those standards.
• the DNs are software defined radios (SDR) equipped with programmable computational capability such as FPGA, DSP, CPU, GPU and/or GPGPU that run algorithms for baseband signal processing. If the standard is upgraded, new baseband algorithms can be remotely uploaded from the CP to the DNs to reflect the new standard.
• the first standard is CDMA-based and subsequently it is replaced by OFDM technology to support different types of systems.
• the sample rate, power and other parameters can be updated remotely to the DNs. This SDR feature of the DNs allows for continuous upgrades of the network as new technologies are developed to improve overall system performance.
• the system described herein is a cloud wireless system consisting of multiple CPs, distributed nodes and a network interconnecting the CPs to the DNs.
• Figure 52 shows one example of cloud wireless system where the nodes identified with solid circles (e.g., 5203) communicate to CP 5206, the nodes identified with empty circles communicate to CP 5205 and the CPs 5205-5206 communicate between each other all through the network 5201.
• the cloud wireless system is a DIDO system and the DNs are connected to the CP and exchange information to reconfigure periodically or instantly system parameters, and dynamically adjust to the changing conditions of the wireless architecture.
• the CP is the DIDO BTS
• the distributed nodes are the DIDO distributed antennas
• the network is the BSN and multiple BTSs are interconnected with each other via the DIDO centralized processor as described in our previous patent applications [0002-0009].
• All DNs 5202-5203 within the cloud wireless system can be grouped in different sets. These sets of DNs can simultaneously create non-interfering wireless links to the multitude of client devices, while each set supporting a different multiple access techniques (e.g., TDMA, FDMA, CDMA, OFDMA and/or SDMA), different modulations (e.g., QAM, OFDM) and/or coding schemes (e.g., convolutional coding, LDPC, turbo codes). Similarly, every client may be served with different multiple access techniques and/or different modulation/coding schemes. Based on the active clients in the system and the standard they adopt for their wireless links, the CPs 5205- 5206 dynamically select the subset of DNs that can support those standards and that are within range of the client devices.
• Wi-Fi alliance "Wi-Fi certified makes it Wi-Fi”
• the MAS is a distributed-input distributed-output (DIDO) system as described the co-pending patent applications [0002-0016] and depicted in Figure 53.
• DIDO distributed-input distributed-output
• the UE 5301 of one embodiment includes an RF transceiver for fixed or mobile clients receiving data streams over the downlink (DL) channel from the DIDO backhaul and transmitting data to the DIDO backhaul via the uplink (UL) channel
• BTS Base Transceiver Station
• the BTSs 5310-5314 of one embodiment interface the DIDO backhaul with the wireless channel.
• BTSs 5310-5314 are access points consisting of D AC/ ADC and radio frequency (RF) chain to convert the baseband signal to RF.
• RF radio frequency
• the BTS is a simple RF transceiver equipped with power amplifier/antenna and the RF signal is carried to the BTS via RF-over-fiber technology as described in our patent application [0010].
• the CTR 5320 in one embodiment is one particular type of BTS designed for certain specialized features such as transmitting training signals for time/frequency synchronization of the BTSs and/or the UEs, receiving/transmitting control information from/to the UEs, receiving the channel state information (CSI) or channel quality information from the UEs.
• CSI channel state information
• the CP 5340 of one embodiment is a DIDO server interfacing the Internet or other types of external networks 5350 with the DIDO backhaul.
• the CP computes the DIDO baseband processing and sends the waveforms to the distributed BTSs for DL transmission
• the BSN 5330 of one embodiment is the network connecting the CP to the distributed BTSs carrying information for either the DL or the UL channel.
• the BSN is a wireline or a wireless network or a combination of the two.
• the BSN is a DSL, cable, optical fiber network, or line-of-sight or non-line- of-sight wireless link.
• the BSN is a proprietary network, or a local area network, or the Internet.
• the DIDO system creates independent channels to multiple users, such that each user receives interference-free channels.
• this is achieved by employing distributed antennas or BTSs to exploit spatial diversity.
• the DIDO system exploits spatial, polarization and/or pattern diversity to increase the degrees of freedom within each channel.
• the increased degrees of freedom of the wireless link are used to transmit independent data streams to an increased number of UEs (i.e., multiplexing gain) and/or improve coverage (i.e., diversity gain).
• the BTSs 5310-5314 are placed anywhere that is convenient where there is access to the Internet or BSN.
• the UEs 5301-5305 are placed randomly between, around and/or surrounded by the BTSs or distributed antennas as depicted in Figure 54.
• the BTSs 5310-5314 send a training signal and/or independent data streams to the UEs 5301 over the DL channel as depicted in Figure 55.
• the training signal is used by the UEs for different purposes, such as time/frequency synchronization, channel estimation and/or estimation of the channel state information (CSI).
• the MU-MAS DL employs non-linear precoding, such as dirty-paper coding (DPC) [1-2] or Tomlinson-Harashima (TH) [3-4] precoding.
• the MU-MAS DL employs non-linear precoding, such as block diagonalization (BD) as described in the co-pending patent applications [0003-0009] or zero-forcing beamforming (ZF-
• the extra BTSs are used to increase link quality to every UE via diversity schemes such as antenna selection or eigenmode selection described in [0002-0016]. If the number of BTSs is smaller than the UEs, the extra UEs share the wireless links with the other UEs via conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDM A).
• conventional multiplexing techniques e.g., TDMA, FDMA, CDMA, OFDM A.
• the UL channel is used to transmit data from the UEs 5301 to the CP 5340 and/or the CSI (or channel quality information) employed by the DIDO precoder.
• the UL channels from the UEs are multiplexed via conventional multiplexing techniques (e.g., TDMA, FDMA, CDMA, OFDMA) to the CT as depicted in Figure 56 or to the closest BTS.
• spatial processing techniques are used to separate the UL channels from the UEs 5301 to the distributed BTSs 5310-5314 as depicted in Figure 57.
• UL streams are transmitted from the client to the DIDO antennas via multiple-input multiple-output (MIMO) multiplexing schemes.
• MIMO multiple-input multiple-output
• the MIMO multiplexing schemes include transmitting independent data streams from the clients and using linear or non-linear receivers at the DIDO antennas to remove co-channel interference.
• the downlink weights are used over the uplink to demodulate the uplink streams, assuming UL/DL channel reciprocity holds and the channel does not vary significantly between DL and UL transmission due to Doppler effects.
• a maximum ratio combining (MRC) receiver is used over the UL channel to increase signal quality at the DIDO antennas from every client.
• the data, control information and CSI sent over the DL/UL channels is shared between the CP 5340 and the BTSs 5310-5314 via the BSN 5330.
• the known training signals for the DL channel can be stored in memory at the BTSs 5310-5314 to reduce overhead over the BSN 5330.
• the BTSs transmit 10Mbps independent data streams to every UE over 5MHz bandwidth (depending on the digital modulation and FEC coding scheme used over the wireless link). If 16 bits of quantization are used for the real and 16 for the imaginary components, the baseband signal requires 160Mbps of data throughput from the CP to the BTSs over the BSN.
• the CP and the BTSs are equipped with encoders and decoders to compress and decompress information sent over the BSN. In the forward link, the precoded baseband data sent from the CP to the BTSs is compressed to reduce the amount of bits and overhead sent over the BSN.
• the CSI as well as data (sent over the uplink channel from the UEs to the BTSs) are compressed before being transmitted over the BSN from the BTSs to the CP.
• Different compression algorithms are employed to reduce the amount of bits and overhead sent over the BSN, including but not limited to lossless and/or lossy techniques [6].
• One feature of DIDO systems employed in one embodiment is making the CP 5340 aware of the CSI or channel quality information between all BTSs 53105314 and UEs 5301 to enable precoding.
• the performance of DIDO depends on the rate at which the CSI is delivered to the CP relative to the rate of change of the wireless links. It is well known that variations of the channel complex gain are due to UE mobility and/or changes in the propagation environment that cause Doppler effects.
• the rate of change of the channel is measured in terms of channel coherence time (T c ) that is inversely proportional to the maximum Doppler shift.
• the latency due to CSI feedback must be a fraction (e.g., 1/10 or less) of the channel coherence time.
• the latency over the CSI feedback loop is measured as the time between the time at which the CSI training is sent and the time the precoded data is demodulated at the UE side, as depicted in Figure 58.
• the BTSs 5310-5314 send CSI training to the UEs 5301, that estimate the CSI and feedback to the BTSs. Then the BTSs send the CSI via the BSN to the CP 5340, that computes the DIDO precoded data streams and sends those back to the BTSs via the BSN 5330. Finally the BTSs send precoded streams to the UEs that demodulate the data.
• FDD frequency division duplex
• T DL and T UL include the times to build, send and process the downlink and uplink frames, respectively
• T BSN is the round-trip delay over the BSN
• Tcp is the time taken by the CP to process the CSI, generate the precoded data streams for the UEs and schedule different UEs for the current transmission.
• T DL is multiplied by 2 to account for the training signal time (from the BTS to the UE) and the feedback signal time (from the UE to the BTS).
• the first step is skipped (i.e., transitting a CSI training signal from the BTS to the UE) as the UEs send CSI training to the BTSs that compute the CSI and send it to the CP.
• the overall latency for the DIDO feedback loop is
• the latency T BSN depends on the type of BSN whether dedicated cable, DSL, fiber optic connection or general Internet. Typical values may vary between fractions of 1msec to 50msec.
• the computational time at the CP can be reduced if the DIDO processing is implemented at the CP on dedicated processors such as ASIC, FPGA, DSP, CPU, GPU and/or GPGPU.
• the number of BTSs 5310-5314 exceeds the number of UEs 5301 , all the UEs can be served at the same time, thereby removing latency due to multiuser scheduling.
• transmit and receive processing for the DL and UL is typically implemented on ASIC, FPGA or DSP with negligible computational time and if the signal bandwidth is relatively large (e.g. more than lMHz) the frame duration can be made very small (i.e., less than 1msec). Therefore, also T DL and T UL are negligible compared to T B SN-
• the CP 5340 tracks the Doppler velocity of all UEs 5301 and dynamically assigns the BTSs 5310-5314 with the lowest TBSN to the UEs with higher Doppler. This adaptation is based on different criteria:
• Type of BSN For example, dedicated fiber optic links typically experience lower latency than cable modems or DSL. Then the lower latency BSNs are used for high- mobility UEs (e.g., cars on freeways, trains), whereas the higher-latency BSNs are used for the fixed-wireless or low-mobility UEs (e.g., home equipment, pedestrians, cars in residential areas)
• Type of QoS For example, the BSN can support different types of DIDO or non-DIDO traffic. It is possible to define quality of service (QoS) with different priorities for different types of traffic. For example, the BSN assigns high priority to DIDO traffic and low priority to non-DIDO traffic. Alternatively, high priority QoS is assigned to traffic for high-mobility UEs and low priority QoS to UEs with low-mobility.
• QoS quality of service
• the traffic over the BSN may vary significantly depending on the time of the day (e.g., night use for homes and day use for offices). Higher traffic load may result in higher latency. Then, in different times of the day, the BSNs with higher traffic, if it results in higher latency, are used for low-mobility UEs, whereas the BSNs with lower traffic, if it results in lower latency, are used for the high- mobility UEs
• any BSN can be affected by temporary network congestion that can result in higher latency. Then the CP can adaptively select the BTSs from congested BSNs, if the congestion cause higher latency, for the low-mobility UEs and the remaining BSNs, if they are lower latency, for the high-mobility UEs.
• the BTSs 5310-5314 are selected based on the Doppler experienced on each individual BTS-UE link.
• the maximum Doppler shift is a function of the angle ( ⁇ ) between the BTS-UE link and the vehicular velocity (v), according to the well known equation
• ⁇ is the wavelength corresponding to the carrier frequency.
• ⁇ is the wavelength corresponding to the carrier frequency.
• ⁇ is the wavelength corresponding to the carrier frequency.
• the Doppler shift is maximum for link A and nearly zero for link C in Figure 59.
• NLOS non-LOS
• the maximum Doppler shift depends on the direction of the multipaths around the UEs, but in general because of the distributed nature of the BTSs in DIDO systems, some BTSs will experience higher Doppler for a given UE (e.g., BTS 5312) whereas other BTSs will experience lower Doppler for that given UE (e.g., BTS 5314).
• the CP tracks the Doppler velocity over every BTS-UE link and selects only the links with the lowest Doppler effect for every UE.
• the CP 5340 defines the "user cluster" for every UE 5301.
• the user cluster is the set of BTSs with good link quality (defined based on certain signal-to-noise ratio, SN , threshold) to the UE and low Doppler (defined, for example, based on a predefined Doppler threshold) as depicted in Figure 60.
• BTSs 5 through 10 all have good SNR to the UEl, but only BTSs 6 through 9 experience low Doppler effect (e.g., below the specified threshold).
• the CP of this embodiement records all of the values of SNR and Doppler for every BTS-UE link into a matrix and for each UE it selects the submatrix that satisfies the SNR and Doppler thresholds.
• the submatrix is identified by the green dotted line surrounding C 2 , 6 , C 2 , 7 , C 3 ,9, C 4 , 7 , C 4 , 8 , C 4 ,9, and C 5 , 6 .
• DIDO precoding weights are computed for that UE based on that submatrix.
• BTSs 5 and 10 are reachable by UEs 2,3,4,5 and 7 as shown in the table in Figure 61.
• the BTSs 5 and 10 either must switched off or assigned to different orthogonal channels based on conventional multiplexing techniques such as TDMA, FDMA, CDMA or OFDMA.
• the adverse effect of Doppler on the performance of DIDO precoding systems is reduced via linear prediction, which is one technique to estimate the complex channel coefficients in the future based on past channel estimates.
• linear prediction is one technique to estimate the complex channel coefficients in the future based on past channel estimates.
• different prediction algorithms for single-input single-output (SISO) and OFDM wireless systems were proposed in [7-11]. Knowing the future channel complex coefficients it is possible to reduce the error due to outdated CSI.
• Figure 62 shows the channel gain (or CSI) at different times: i) tcTR is the time at which the CTR in Figure 58 receives the CSI from the UEs in FDD systems (or equivalently the BTSs estimate the CSI from the UL channel exploiting DL/UL reciprocity in TDD systems); ii) tcp is the time at which the CSI is delivered to the CP via the BSN; iii) t B TS is the time at which the CSI is used for precoding over the wireless link.
• the predicted CSI at time tcp reproduces reliably the channel gain in the future.
• the time difference between the predicted CSI and the current CSI is called prediction horizon and in SISO systems typically scales with the channel coherence time.
• Described herein is are prediction techniques that exploit temporal and spatial diversity of DIDO systems to predict the vector channel (i.e., CSI from the BTSs to the UEs) in the future. These embodiments exploit spatial diversity available in wireless channels to obtain negligible CSI prediction error and an extended prediction horizon over any existing SISO and MIMO prediction algorithms.
• One important feature of these techniques is to exploit distributed antennas given that they receive uncorrelated complex channel coefficients from the distributed UEs.
• the spatial and temporal predictor is combined with estimator in the frequency domain to allow CSI prediction over all the available subcarriers in the system, such as in OFDM systems.
• the DIDO precoding weights are predicted (rather than the CSI) based on previous estimates of the DIDO weights.
• Embodiments of the invention may include various steps as set forth above.
• the steps may be embodied in machine-executable instructions which cause a general-purpose or special-purpose processor to perform certain steps.
• the various components within the Base Stations/ APs and Client Devices described above may be implemented as software executed on a general purpose or special purpose processor.
• various well known personal computer components such as computer memory, hard drive, input devices, etc., have been left out of the figures.
• the various functional modules illustrated herein and the associated steps may be performed by specific hardware components that contain hardwired logic for performing the steps, such as an application-specific integrated circuit ("ASIC") or by any combination of programmed computer components and custom hardware components.
• ASIC application-specific integrated circuit
• certain modules such as the Coding, Modulation and Signal Processing Logic 903 described above may be implemented on a programmable digital signal processor ("DSP") (or group of DSPs) such as a DSP using a Texas Instruments' TMS320x architecture (e.g., a TMS320C6000, TMS320C5000, . . . etc).
• DSP programmable digital signal processor
• the DSP in this embodiment may be embedded within an add-on card to a personal computer such as, for example, a PCI card.
• a variety of different DSP architectures may be used while still complying with the underlying principles of the invention.
• Elements of the present invention may also be provided as a machine-readable medium for storing the machine-executable instructions.
• the machine-readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of machine- readable media suitable for storing electronic instructions.
• the present invention may be downloaded as a computer program which may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection).
• This invention relates generally to the field of communication systems. More particularly, the invention relates to a system and method for distributed input-distributed output wireless communications using space-time coding techniques.
• MIMO Multiple Input Multiple Output
• MIMO technology is based on the use of spatially distributed antennas for creating parallel spatial data streams within a common frequency band.
• the radio waves are transmitted in such a way that the individual signals can be separated at the receiver and demodulated, even though they are transmitted within the same frequency band, which can result in multiple statistically independent (i.e. effectively separate) communications channels.
• MIMO can rely on uncorrelated or weakly-correlated multi-path signals to achieve a higher throughput and improved signal-to-noise ratio within a given frequency band.
• Airgo Networks was recently able to achieve 108 Mbps in the same spectrum where a conventional 802.1 1 g system can achieve only 54 Mbps (this is described on Airgo's website, currently at http://www. ai rgonetworks. com ).
• MIMO systems typically face a practical limitation of fewer than 10 antennas per device (and therefore less than 10X throughput improvement in the network) for several reasons:
• MIMO antennas on a given device must have sufficient separation between them so that each receives a statistically
• MIMO bandwidth improvements can be seen with antenna spacing of even one-sixth wavelength ( ⁇ /6), the efficiency rapidly deteriorates as the antennas get closer, resulting in lower MIMO bandwidth multipliers. Also, as the antennas are crowded together, the antennas typically must be made smaller, which can impact bandwidth efficiency as well. Finally, with lower frequencies and longer wavelengths, the physical size of a single MIMO device can become unmanageable. An extreme example is in the HF band, where MIMO device antennas may have to be separated from each other by 10 meters or more.
• Each MIMO receiver/transmitter subsystem produces a certain level of noise. As more and more of these subsystems are placed in close proximity to each other, the noise floor increases. Meanwhile, as increasingly more distinct signals need to be distinguished from each other in a many-antenna MIMO system, an increasingly lower noise floor is required.
• a separate RF subsystem is required for each MIMO antenna, including separate Analog-to-Digital (A/D) and Digital-to-Analog (D/A) converters.
• A/D Analog-to-Digital
• D/A Digital-to-Analog
• MIMO-type technology is a virtual antenna array.
• a virtual antenna array Such a system is proposed in a research paper presented at European Cooperation in the field of Scientific and Technical Research, EURO-COST, Barcelona, Spain, Jan 15-17, 2003: Center for Telecommunications Research, King's College London, UK: "A step towards MIMO: Virtual Antenna Arrays", Mischa Dohler & Hamid Aghvami.
• Virtual antenna arrays are systems of cooperative wireless devices (such as cell phones), which communicate amongst each other (if and when they are near enough to each other) on a separate communications channel than their primary communications channel to the their base station so as to operate cooperatively (e.g. if they are GSM cellular phones in the UHF band, this might be a 5 GHz Industrial Scientific and Medical (ISM) wireless band).
• This allows single antenna devices, for example, to potentially achieve MIMO-like increases in bandwidth by relaying information among several devices in range of each other (in addition to being in range of the base station) to operate as if they are physically one device with multiple antennas.
• a method comprising: transmitting a training signal from each antenna of a base station to each of a plurality of client devices utilizing tropospheric scatter, each of the client devices analyzing each training signal to generate channel characterization data, and transmitting the channel
• the channel characterization data for each of the plurality of client devices; receiving data to be transmitted to each of the client devices; and precoding the data using the channel characterization data associated with each respective client device to generate precoded data signals for each antenna of the base station; and transmitting the precoded data signals through each antenna of the base station to each respective client device.
• FIG. 1 illustrates a prior art MIMO system.
• FIG. 2 illustrates an N-antenna Base Station communicating with a plurality of Single-antenna Client Devices.
• FIG. 3 illustrates a three Antenna Base Station communicating with three Single-Antenna Client Devices
• FIG. 4 illustrates training signal techniques employed in one embodiment of the invention.
• FIG. 5 illustrates channel characterization data transmitted from a client device to a base station according to one embodiment of the invention.
• FIG. 6 illustrates a Multiple-Input Distributed-Output (“MIDO")
• FIG. 7 illustrates a Multiple-Input Multiple Output (“MIMO") upstream transmission according to one embodiment of the invention.
• MIMO Multiple-Input Multiple Output
• FIG. 8 illustrates a base station cycling through different client groups to allocate bandwidth according to one embodiment of the invention.
• FIG. 9 illustrates a grouping of clients based on proximity according to one embodiment of the invention.
• FIG. 10 illustrates an embodiment of the invention employed within an NVIS system.
• FIG. 11 illustrates an embodiment of the invention employing the use of tropospheric scatter.
• FIG. 12 illustrates a prior art tropospheric scatter transmission system.
• FIG. 13 illustrates an embodiment of the invention employing the use of a tropospheric scatter transmission system over a coverage area.
• FIG. 14 illustrates a Direct Broadcast Satellite dish and RF signal paths in an embodiment of the invention.
• FIG. 15 illustrates an embodiment of the invention employing the use of conventional Ml MO with tropospheric scatter.
• Figure 16 illustrates an overhead view of a coverage area surrounded by 12 clusters of 3 antennas.
• Figures 17a-c illustrates 3 client antennas in a coverage area from different elevation views.
• Figure 1 shows a prior art MIMO system with transmit antennas 104 and receive antennas 105. Such a system can achieve up to 3X the throughput that would normally be achievable in the available channel.
• MIMO system with transmit antennas 104 and receive antennas 105.
• Such a system can achieve up to 3X the throughput that would normally be achievable in the available channel.
• There are a number of different approaches in which to implement the details of such a MIMO system which are described in published literature on the subject, and the following explanation describes one such approach.
• the channel is "characterized.” This is accomplished by initially transmitting a "training signal" from each of the transmit antennas 104 to each of the receivers 105.
• the training signal is generated by the coding and modulation subsystem 102, converted to analog by a D/A converter (not shown), and then converted from baseband to RF by each transmitter 103, in succession.
• Each receive antenna 105 coupled to its RF Receiver 106 receives each training signal and converts it to baseband.
• the baseband signal is converted to digital by a D/A converter (not shown), and the signal processing subsystem 107 characterizes the training
• Each signal's characterization may include many factors including, for example, phase and amplitude relative to a reference internal to the receiver, an absolute reference, a relative reference, characteristic noise, or other factors.
• Each signal's characterization is typically defined as a vector that characterizes phase and amplitude changes of several aspects of the signal when it is transmitted across the channel. For example, in a quadrature amplitude modulation (“QAM”)-modulated signal the characterization might be a vector of the phase and amplitude offsets of several multipath images of the signal. As another example, in an orthogonal frequency division multiplexing (“OFDM”)- modulated signal, it might be a vector of the phase and amplitude offsets of several or all of the individual sub-signals in the OFDM spectrum.
• QAM quadrature amplitude modulation
• OFDM orthogonal frequency division multiplexing
• the signal processing subsystem 107 stores the channel characterization received by each receiving antenna 105 and corresponding receiver 106. After all three transmit antennas 104 have completed their training signal
• the signal processing subsystem 107 will have stored three channel characterizations for each of three receiving antennas 105, resulting in a 3x3 matrix 108, designated as the channel characterization matrix, "H.”
• Each individual matrix element 3 ⁇ 4 is the channel characterization (which is typically a vector, as described above) of the training signal transmission of transmit antenna 104 i as received by the receive antenna 105 j .
• the signal processing subsystem 107 inverts the matrix H 108, to produce H "1 , and awaits transmission of actual data from transmit
• a payload of data to be transmitted is presented to the data Input subsystem 100. It is then divided up into three parts by splitter 101 prior to being presented to coding and modulation subsystem 102. For example, if the payload is the ASCII bits for "abcdef,” it might be divided up into three sub- payloads of ASCII bits for "ad,” "be,” and “cf" by Splitter 101. Then, each of these sub-payloads is presented individually to the coding and modulation subsystem 102.
• Each of the sub-payloads is individually coded by using a coding system suitable for both statistical independence of each signal and error correction capability. These include, but are not limited to Reed-Solomon coding, Viterbi coding, and Turbo Codes.
• each of the three coded sub-payloads is modulated using an appropriate modulation scheme for the channel.
• Example modulation schemes are differential phase shift key (“DPSK") modulation, 64- QAM modulation and OFDM. It should be noted here that the diversity gains provided by MIMO allow for higher-order modulation constellations that would otherwise be feasible in a SISO (Single Input-Single Output) system utilizing the same channel.
• DPSK differential phase shift key
• MISO Single Input-Single Output
• the three transmitted signals x n are then demodulated, decoded, and error-corrected by signal processing subsystem 107 to recover the three bit streams that were originally separated out by splitter 101. These bit streams are combined in combiner unit 108, and output as a single
• FIG. 2 illustrates one embodiment of the invention in which a Base Station 200 is configured with a Wide Area Network interface (e.g. to the Internet through a T1 or other high speed connection) 201 and is provisioned with a number (n) of antennas 202.
• a Base Station 200 is configured with a Wide Area Network interface (e.g. to the Internet through a T1 or other high speed connection) 201 and is provisioned with a number (n) of antennas 202.
• Client Devices 203-207 each with a single antenna, which are served wirelessly from the Base Station 200.
• the Base Station could be based at a cellular phone tower, or on a television broadcast tower.
• the Base Station 200 is positioned on the ground and is
• Exp. Mail No.: EV4717133726US 06181.P014X configured to transmit upward at HF frequencies (e.g., frequencies up to 24MHz) to bounce signals off the ionosphere as described in co-pending application entitled SYSTEM AND METHOD FOR ENHANCING NEAR VERTICAL
• the Base Station 200 is positioned on the ground and is configured to transmit at angle into the troposphere using tropospheric scatter (“troposcatter”) techniques.
• the Base Station 200 and Client Devices 203-207 set forth above are for the purpose of illustration only and are not required for complying with the underlying principles of the invention.
• the Base Station may be connected to a variety of different types of wide area networks via WAN interface 201 including application-specific wide area networks such as those used for digital video distribution.
• the Client Devices may be any variety of wireless data processing and/or
• communication devices including, but not limited to cellular phones, personal digital assistants (“PDAs”), receivers, and wireless cameras.
• PDAs personal digital assistants
• receivers and wireless cameras.
• the Base Station's n Antennas 202 are separated spatially such that each is transmitting and receiving signals which are not spatially correlated, just as if the Base Station was a prior art MIMO transceiver. As described in the Background, experiments have been done where antennas
• Exp. Mail No.: EV4717133726US 06181.P014X placed within ⁇ /6 (i.e. 1/6 wavelength) apart successfully achieve an increase in bandwidth from MIMO, but generally speaking, the further apart these Base Station antennas are placed, the better the system performance, and ⁇ /2 is a desirable minimum.
• ⁇ /6 i.e. 1/6 wavelength
• a single Base Station 200 may very well have its antennas located very far apart.
• the antennas may be 10 meters apart or more (e.g., in an NVIS implementation mentioned above). If 100 such antennas are used, the Base Station's antenna array could well occupy several square kilometers.
• one embodiment of the invention polarizes the signal in order to increase the effective bandwidth of the system.
• Increasing channel bandwidth through polarization is a well known technique which has been employed by satellite television providers for years.
• Using polarization it is possible to have multiple (e.g., three) Base Station antennas very close to each other, and still be not spatially correlated.
• conventional RF systems usually will only benefit from the diversity of two dimensions (e.g. x and _y) of polarization, the architecture descried herein may further benefit from the diversity of three dimensions of polarization (x, y and z).
• FIG 3 provides additional detail of one embodiment of the Base Station 200 and Client Devices 203-207 shown in Figure 2.
• the Base Station 300 is shown with only three antennas 305 and only
• Figure 3 is similar to the prior art MIMO architecture shown in Figure 1 in that both have three antennas on each sides of a communication channel.
• the three antennas 105 on the right side of Figure 1 are all a fixed distance from one another (e.g., integrated on a single device), and the received signals from each of the antennas 105 are processed together in the Signal Processing subsystem 107.
• the three antennas 309 on the right side of the diagram are each coupled to a different Client Device 306-308, each of which may be distributed anywhere within range of the Base Station 305.
• the signal that each Client Device receives is processed independently from the other two received signals in its Coding, Modulation, Signal Processing subsystem 31 1.
• FIG. 3 illustrates a Multiple Input (i.e. antennas 309) Distributed Output (i.e. antennas 305) system, referred to hereinafter as a "Ml DO" system.
• the Ml DO architecture shown in Figure 3 achieves a similar bandwidth increase as MIMO over a SISO system for a given number of transmitting antennas. However, one difference between MIMO and the particular MIDO
• each Ml DO Client Device 306-308 requires only a single receiving antenna, whereas with MIMO, each Client Device requires as least as many receiving antennas as the bandwidth multiple that is hoped to be achieved. Given that there is usually a practical limit to how many antennas can be placed on a Client Device (as explained in the Background), this typically limits MIMO systems to between four to ten antennas (and 4X to 10X bandwidth multiple).
• each antenna is equipped with a transceiver 304 and a portion of the processing power of a Coding, Modulation, and Signal Processing section 303.
• each Client Device 306-308 no matter how much Base Station 300 is expanded, each Client Device 306-308 only will require one antenna 309, so the cost for an individual user Client Device 306-308 will be low, and the cost of Base Station 300 can be shared among a large base of users.
• the channel is characterized.
• a training signal is
• Each training signal is generated by the Coding, Modulation, and Signal Processing subsystem 403, converted to analog through a D/A converter, and transmitted as RF through each RF Transceiver 404.
• Various different coding, modulation and signal processing techniques may be employed including, but not limited to, those described above (e.g., Reed Solomon, Viterbi coding; QAM, DPSK, QPSK modulation, . . . etc).
• Each Client Device 406-408 receives a training signal through its antenna 409 and converts the training signal to baseband by Transceiver 410.
• An A/D converter (not shown) converts the signal to digital where is it processed by each Coding, Modulation, and Signal Processing subsystem 41 1.
• characterization logic 320 then characterizes the resulting signal (e.g., identifying phase and amplitude distortions as described above) and stores the
• the Coding Modulation and Signal Processing subsystem 420 of client device 406 is initialized with a known pattern of the training signal (either at the time of manufacturing, by receiving it in a transmitted message, or through another initialization process).
• antenna 405 transmits the training signal with this known pattern, Coding Modulation and Signal
• Exp. Mail No.: EV4717133726US 06181.P014X Processing subsystem 420 uses correlation methods to find the strongest received pattern of the training signal, it stores the phase and amplitude offset, then it subtracts this pattern from the received signal. Next, it finds then second strongest received pattern that correlates to the training signal, it stores the phase and amplitude offset, then it subtracts this second strongest pattern from the received signal. This process continues until either some fixed number of phase and amplitude offsets are stored (e.g. eight), or a detectable training signal pattern drops below a given noise floor. This vector of phase/amplitude offsets becomes element H-n of the vector 413. Simultaneously, Coding Modulation and Signal Processing subsystems for Client Devices 407 and 408 implement the same processing to produce their vector elements H 2 i and H 3 -
• the memory in which the characterization is stored may be a non-volatile memory such as a Flash memory or a hard drive and/or a volatile memory such as a random access memory (e.g., SDRAM, RDAM).
• a non-volatile memory such as a Flash memory or a hard drive
• a volatile memory such as a random access memory (e.g., SDRAM, RDAM).
• different Client Devices may concurrently employ different types of memories to store the characterization information (e.g., PDA's may use Flash memory whereas notebook computers may use a hard drive).
• PDA's may use Flash memory whereas notebook computers may use a hard drive).
• the underlying principles of the invention are not limited to any particular type of storage mechanism on the various Client Devices or the Base Station.
• each Client Device 406-408 since each Client Device 406-408 has only one antenna, each only stores a 1x3 column 413-415 of the H matrix.
• Figure 4 illustrates the stage after the first training
• Exp. Mail No.: EV4717133726US 06181.P014X signal transmission where the first row of 1x3 columns 413-415 has been stored with channel characterization information for the first of the three Base Station antennas 405. The remaining two columns are stored following the channel characterization of the next two training signal transmissions from the remaining two base station antennas. Note that for the sake of illustration the three training signals are transmitted at separate times. If the three training signal patterns are chosen such as not to be correlated to one another, they may be transmitted simultaneously, thereby reducing training time.
• each Client Device 506-508 transmits back to the Base Station 500 the 1x3 column 513-515 of matrix H that it has stored. To the sake of simplicity, only one Client Device 506 is illustrated transmitting its characterization information in Figure 5.
• An appropriate modulation scheme e.g. DPSK, 64QAM, OFDM
• adequate error correction coding e.g. Reed
• the Coding, Modulation and Signal Processing subsystem 503 of Base Station 500 receives the 1x3 column 513-515, from each Client Device 507-508, it stores it in a 3x3 H matrix 516.
• the Base Station may employ various different storage technologies including, but not limited to non-volatile mass storage memories (e.g., hard drives) and/or volatile memories (e.g., SDRAM) to store the matrix 516.
• Figure 5 illustrates a stage at which the Base Station 500 has received and stored the 1x3 column 513 from Client Device 509.
• the 1x3 columns 514 and 515 may be transmitted and stored in H matrix 516 as they are received from the remaining Client Devices, until the entire H matrix 516 is stored.
• a Base Station 600 includes a Router 602 communicatively positioned between the WAN Interface 601 and the Coding, Modulation and Signal Processing subsystem 603 that sources multiple data streams (formatted into bit streams) from the WAN interface 601 and routes them as separate bit streams U3 intended for each Client Device 606-608, respectively.
• Various well known routing techniques may be employed by the router 602 for this purpose.
• the three bit streams, - , shown in Figure 6 are then routed into the Coding, Modulation and Signal Processing subsystem 603 and coded into statistically distinct, error correcting streams (e.g. using Reed Solomon, Viterbi, or Turbo Codes) and modulated using an appropriate modulation scheme for the channel (such as DPSK, 64QAM or OFDM).
• error correcting streams e.g. using Reed Solomon, Viterbi, or Turbo Codes
• an appropriate modulation scheme for the channel such as DPSK, 64QAM or OFDM
• the embodiment illustrated in Figure 6 includes signal precoding logic 630 for uniquely coding the signals transmitted from each of the antennas 605 based on the signal characterization matrix 616.
• the precoding logic 630 multiplies the three bit streams w w 3 in Figure 6 by the inverse of the H matrix 616, producing three new bit streams, u - u .
• the three precoded bit streams are then converted to analog by D/A converters (not shown) and transmitted as RF by Transceivers 604 and antennas 605.
• each m contains the data from one of the three bit streams routed by the Router 602, and each such bit stream is intended for one of the three Client Devices 606-608.
• each v ; is calculated by the precoding logic 630 within the Coding, Modulation and Signal Processing subsystem 603 by implementing the following formulas:
• each 3 ⁇ 4 is calculated at the receiver after the signals have been transformed by the channel
• the embodiments of the invention described herein solve for each v ; at the transmitter before the signals have been transformed by the channel.
• Each antenna 609 receives m already separated from the other w n _i bit streams intended for the other antennas 609.
• Each Transceiver 610 converts each received signal to baseband, where it is digitized by an A/D converter (now shown), and each Coding, Modulation and Signal Processing subsystem 61 1 , demodulates and decodes the ; bit stream intended for it, and sends its bit stream to a Data Interface 612 to be used by the Client Device (e.g., by an application on the client device).
• Exp. Mail No.: EV4717133726US 06181.P014X The embodiments of the invention described herein may be implemented using a variety of different coding and modulation schemes. For example, in an OFDM implementation, where the frequency spectrum is separated into a plurality of sub-bands, the techniques described herein may be employed to characterize each individual sub-band. As mentioned above, however, the underlying principles of the invention are not limited to any particular modulation scheme.
• the Channel characterization matrix 616 at the Base Station is continually updated.
• the Base Station 600 periodically (e.g., every 250
• the training signal is interleaved within the actual data signal sent to each client device.
• the training signals are much lower bandwidth than the data signals, so this would have little impact on the overall throughput of the system. Accordingly, in this embodiment, the channel characterization matrix 616 may be updated continuously as the Base Station actively communicates with each Client Device, thereby maintaining an accurate
• One embodiment of the invention illustrated in Figure 7 employs MIMO techniques to improve the upstream communication channel (i.e., the channel from the Client Devices 706-708 to the Base Station 700).
• the channel from each of the Client Devices is continually analyzed and characterized by upstream channel characterization logic 741 within the Base Station. More specifically, each of the Client Devices 706-708 transmits a training signal to the Base Station 700 which the channel characterization logic 741 analyzes (e.g., as in a typical MIMO system) to generate an N M channel characterization matrix 741 , where N is the number of Client Devices and M is the number of antennas employed by the Base Station.
• the embodiment illustrated in Figure 7 employs three antennas 705 at the Base Station and three Client Devices 706-608, resulting in a 3x3 channel characterization matrix 741 stored at the Base Station 700.
• the MIMO upstream transmission illustrated in Figure 7 may be used by the Client Devices both for transmitting data back to the Base Station 700, and for transmitting channel characterization vectors back to the Base Station 700 as illustrated in Figure 5.
• the method shown in Figure 7 allows for the simultaneous transmission of channel characterization vectors from multiple Client Devices back to the Base Station 700, thereby dramatically reducing the channel characterization vectors' impact on return channel throughput.
• each signal's characterization may include many factors including, for example, phase and amplitude relative to a reference internal to the receiver, an absolute reference, a relative reference, characteristic noise, or other factors.
• the characterization might be a vector of the phase and amplitude offsets of several multipath images of the signal.
• QAM quadrature amplitude modulation
• OFDM orthogonal frequency division multiplexing
• the training signal may be generated by each Client Device's coding and modulation subsystem 71 1 , converted to analog by a D/A converter (not shown), and then converted from baseband to RF by each Client Device's transmitter 709.
• Client Devices in order to ensure that the training signals are synchronized, Client Devices only transmit training signals when requested by the Base Station (e.g., in a round robin manner).
• training signals may be interleaved within or transmitted concurrently with the actual data signal sent from each client device.
• the training signals may be continuously transmitted and analyzed by the upstream channel characterization logic 741 , thereby ensuring that the channel characterization matrix 741 remains up-to-date.
• the total channel bandwidth supported by the foregoing embodiments of the invention may be defined as min (N, M) where N is the number of Client Devices and M is the number of Base Station antennas. That is, the capacity is
• Exp. Mail No.: EV4717133726US 06181.P014X limited by the number of antennas on either the Base Station side or the Client side.
• one embodiment of the invention employs synchronization techniques to ensure that no more than min (N, M) antennas are transmitting/ receiving at a given time.
• the number of antennas 705 on the Base Station 700 will be less than the number of Client Devices 706-708.
• An exemplary scenario is illustrated in Figure 8 which shows five Client Devices 804-808 communicating with a base station having three antennas 802. In this
• Exp. Mail No.: EV4717133726US 06181.P014X excludes Client Device 806 (i.e., because Client Device 806 was engaged in communication with the Base Station for the first two cycles).
• the Base Station may employ the foregoing techniques to transmit training signals to each of the Client Devices and receive training signals and signal
• certain Client Devices or groups of client devices may be allocated different levels of bandwidth. For example, Client Devices may be prioritized such that relatively higher priority Client Devices may be
• the "priority" of a Client Device may be selected based on a number of variables including, for example, the designated level of a user's subscription to the wireless service (e.g., user's may be willing to pay more for additional bandwidth) and/or the type of data being communicated to/from the Client Device (e.g., real-time communication such as telephony audio and video may take priority over non-real time communication such as email).
• the Base Station dynamically allocates bandwidth based on the Current Load required by each Client Device. For example, if Client Device 804 is streaming live video and the other devices 805-808 are performing non-real time functions such as email, then the Base Station 800 may allocate relatively more bandwidth to this client 804. It should be noted, however,
• two Client Devices 907, 908 may be so close in proximity, that the channel characterization for the clients is effectively the same.
• the Base Station will receive and store effectively equivalent channel characterization vectors for the two Client Devices 907, 908 and therefore will not be able to create unique, spatially distributed signals for each Client Device. Accordingly, in one embodiment, the Base Station will ensure that any two or more Client Devices which are in close proximity to one another are allocated to different groups. In Figure 9, for example, the Base Station 900 first
• Client Devices 907 and 908 communicates with a first group 910 of Client Devices 904, 905 and 908; and then with a second group 91 1 of Client Devices 905, 906, 907, ensuring that Client Devices 907 and 908 are in different groups.
• the Base Station 900 communicates with both Client Devices 907 and 908 concurrently, but multiplexes the
• the Base Station may employ time division multiplexing (“TDM”), frequency division multiplexing (“FDM”) or code division multiple access
• TDM time division multiplexing
• FDM frequency division multiplexing
• code division multiple access code division multiple access
• CDMA Code Division Multiple Access
• each Client Device described above is equipped with a single antenna, the underlying principles of the invention may be employed using Client
• Exp. Mail No.: EV4717133726US 06181.P014X Devices with multiple antennas to increase throughput For example, when used on the wireless systems described above, a client with 2 antennas will realize a 2x increase in throughput, a client with 3 antennas will realize a 3x increase in throughput, and so on (i.e., assuming that the spatial and angular separation between the antennas is sufficient).
• the Base Station may apply the same general rules when cycling through Client Devices with multiple antennas. For example, it may treat each antenna as a separate client and allocate bandwidth to that "client" as it would any other client (e.g., ensuring that each client is provided with an adequate or equivalent period of communication).
• one embodiment of the invention employs the MIDO and/or MIMO signal transmission techniques described above to increase the signal-to-noise ratio and transmission bandwidth within a Near Vertical Incidence Skywave ("NVIS") system.
• NVIS Near Vertical Incidence Skywave
• a first NVIS station 1001 equipped with a matrix of N antennas 1002 is configured to communicate with M client devices 1004.
• the NVIS antennas 1002 and antennas of the various client devices 1004 transmit signals upward to within about 15 degrees of vertical in order to achieve the desired NVIS and minimize ground wave interference effects.
• the antennas 1002 and client devices 1004 support multiple independent data streams 1006 using the various MIDO and MIMO techniques described above at a designated frequency within the NVIS spectrum (e.g., at a carrier frequency at or below 23 MHz, but typically below 10 MHz), thereby significantly increasing
• the NVIS antennas serving a given station may be physically very far apart from each other. Given the long wavelengths below 10 MHz and the long distance traveled for the signals (as much as 300 miles round trip), physical separation of the antennas by 100s of yards, and even miles, can provide advantages in diversity. In such situations, the individual antenna signals may be brought back to a centralized location to be processed using conventional wired or wireless communications systems. Alternatively, each antenna can have a local facility to process its signals, then use conventional wired or wireless communications systems to communicate the data back to a centralized location. In one embodiment of the invention, NVIS Station 1001 has a broadband link 1015 to the Internet 1010 (or other wide area network), thereby providing the client devices 1003 with remote, high speed, wireless network access.
• the Internet 1010 or other wide area network
• one embodiment of the invention employs the MIDO and/or MIMO signal transmission techniques described above (collective referred to heretofore as "DIDO") to increase the signal-to-noise ratio and transmission bandwidth within a tropospheric scatter (“troposcatter”) system.
• DIDO the MIDO and/or MIMO signal transmission techniques described above
• troposcatter a tropospheric scatter
• a first troposcatter station 1 101 equipped with a matrix of N antennas 1 102 is configured to communicate with M client devices 1 104.
• the upward angle of transmission is exaggerated for illustration purposes in Figure 11. A more typical low angle for
• the antennas of the various client devices 1 104 transmit signals back through tropospheric scatter, and they are received by base station antennas 1 102.
• the troposcatter base station antennas 1 102 are aimed at an upward angle so that part of the transmission scatters and reflects off the troposphere so as to hit the target area where the M client devices 1 104 are located.
• Calculating specific antenna elevation angles and optimizing antennas for troposcatter is well understood to those skilled in the art, and several online calculators exist for making such calculations. As an example, one such calculator can be
• This particular troposcatter calculator's input parameters include distance between the transmit and receive antennas, transmission frequency, antenna heights, output power, station noise characteristics, obstacle distance/heights, antenna gain, and bandwidth.
• An exemplary prior art troposcatter radio terminal i.e. transceiver and antenna
• the system has a nominal
• the antennas 1 102 and client devices 1 104 support multiple independent data streams 1 106 using the various DIDO techniques described herein at a designated frequency within the troposcatter spectrum (e.g., at a carrier frequency from below 50 MHz to above 10 GHz).
• DIDO techniques include, but are not limited to, the transmission of training signals, the characterization of the channel vectors, and the transmission back to the troposcatter base station 1 101 of the channel vectors so as to form a channel matrix.
• the troposcatter antennas served by a given troposcatter base station 1 101 may be close (e.g. as close as ⁇ /6) or physically very far apart (10s or 100s of miles) from each other and/or they may be clustered in groups.
• the term "troposcatter base station 1 101" as used herein refers to a common channel matrix computation system, similar to Figure 2's Base Station 200, but one in which the transmitting antennas 1 102 may in fact be distributed very far from a given site.
• the specific configuration will depend on the desired coverage area, the need to avoid obstacles in the terrain, and if necessary, the need to achieve more diversity and/or a wider angle between transmit antennas.
• a DIDO base station by utilizing channel state information feedback from the client devices after sending training signals, will produce a combination of transmitted signals from its antennas 1 102, such that the client devices will receive independent signals. And, when the client devices 1 104 transmit back to the base stations antennas 1 102, the base station will use the channel state information determined from client device training signals.
• troposcatter Station 1 101 has a broadband link 1 1 15 to the Internet 1 1 10 (or other wide area network), thereby providing the client devices 1 103 with remote, high speed, wireless network access.
• the troposcatter base station antennas 1 102 and the client device antennas 1 104 will work best if they each have a line-of-sight (LOS) view of the troposphere to the common volume 1121.
• the common volume 1 121 is an area of the troposphere where tropospheric scattering will cause some of the transmitted signal to reflect back to the ground. Typically, most of the transmitted signal will pass through the troposphere as indicated by 1 120. Perfect LOS transmissions over long distances with very narrow angles between antennas may result in poor diversity. This can be mitigated by separating the base station antennas 1 102 by large distances, but the scattering effect of the troposphere itself may also create diversity.
• a LOS path to the common volume 1 121 can be planned for when the base station antennas 1 102 are installed, it is more difficult to guarantee that a client device antenna 1 104 has a LOS view of the common volume 1 121.
• the common volume 1 121 is often going to be at a low angle in the sky. If, for example, a consumer wishes to place a client device antenna 1 104 in
• Exp. Mail No.: EV4717133726US 06181.P014X a window of her house, or on the roof of her house, even though the antenna may have a view of some of the sky, it may be obstructed from having a view of the particular patch of the sky which contains the common volume 1 121.
• Troposcatter base station 1301 serves the same function as troposcatter base station 1 101 , but its antennas are deliberately distributed far apart in antenna clusters 1341 -1344.
• the antenna clusters 1341 -1344 are aimed such that their transmissions reflect from the troposphere to a common ground coverage area 1360.
• This coverage area may be a town, a city, a rural area, or an uninhabited area under exploration. It may also be an area on a body of water.
• Antenna cluster 1341 transmits RF transmission 1330, which scatters in common volume 1321 and then reflects back to earth as RF reflection 1331 into coverage area 1360 where it then is received in coverage area 1360 by one or more client antennas 1361 -1363.
• antenna clusters 1342-1344 transmit RF that scatters in common volumes 1322-1324, respectively, and then the RF reflects back to earth in to coverage area 1360 where it is then received by one or more client antennas 1361 -1363.
• one or more client antennas 1361 -1363 transmit back through common volumes 1321 -1324 to antenna clusters 1341 - 1344 as a return path.
• Some or all client antennas 1361-1363 may not have a LOS view the sky to see all common volumes 1321 -1324. But so long as each client antenna 1361 - 1363 can see at least one common volume 1321 -1324, then it will be able to have communications with the troposcatter base station 1301. Clearly, the more antenna clusters 1341 -1344 that are established around the coverage area 1360, the less chance that a client antenna 1361 will be unable to see at least one common volume 1321-1324.
• the troposcatter base station 1 101 communicates to the antenna clusters 1341 -1344 through communication links 1351 -1354.
• These communications links 1351 -1354 may be physically implemented via various means, including optical fiber, leased communications lines, such as DS3 lines, or they may be
• communication links 1351 -1354 may be implemented utilizing troposcatter communications.
• each of the antenna clusters 1341 -1344 will have its own local RF transceivers which are directed by the troposcatter base 1301 as to precisely what RF signals are to be generated in synchrony so that all antenna clusters 1341 -1344 work in a coordinated fashion as a single DIDO system.
• each antenna cluster 1341 -1344 will have its own base station 1301 and will operate independently from the other antenna clusters 1341 -1344. In this situation each antenna cluster may transmit at a
• FIG. 16 and Figures 17a-c An alternative embodiment of the system illustrated in Figure 13 is illustrated in Figures 16 and Figures 17a-c.
• the communications links, then base station and the common volumes from Figure 13 are not shown in Figures 16 and Figures 17a-c for the sake of clarity, but such components still exist, and are implemented as previously described.
• Figure 16 shows an overhead (plan) view of a coverage area 1360 surrounded by 12 clusters 161 1 -1643 of 3 antennas 1651 -1653 each, for a total of 36 antennas. All of these antennas are aimed such that when they scatter off of their respective common volumes, the reflected RF reaches the coverage area 1360. Coverage area 1360 has many client antennas, of which 3, 1361 -1363 are illustrated. Figure 16 also indicates the north/south/east/west orientation of the illustration.
• Figures 17a-c shows the 3 client antennas 1361 -1363 in the coverage area 1360 schematically as antennas 1701.
• Figure 17a shows the antennas 1701 in an elevation view from the south;
• Figure 17b shows the antennas 1701 in an elevation view from the west;
• Figure 17c shows the antennas 1701 in an overhead (plan) view from above.
• Note the schematic illustration of the antennas 1701 shows them as triangles in the elevation views and as squares in the overhead view, but they are the same antennas.
• the antennas could be any combination of the antennas 1701.
• the 3 antennas may be located in many different positions relative to each other, including being miles apart. And finally, in one embodiment, far more than 3 antennas are deployed in a given coverage area.
• Figure 17a-c shows how the RF beams from the various antennas in Figure 16 arrive at a large variety of angles to antennas 1701.
• antenna cluster 1613's transmission arrives at angle 1713
• 1612's transmission arrives at angle 1712
• 161 1 's transmission arrives at angle 171 1.
• antenna clusters 1613-1615 are positioned successively further from coverage area 1360, but are all aimed to reflect down to coverage area 1360, resulting in varied angles of arrival.
• antenna clusters' 1631 - 1633's transmission arrive at angles 1731 -1733, respectively;
• clusters 1621 -1623 arrive at angles 1721 -1723, respectively; and
• clusters 1641 -1643 arrive at angles 1741 -1743, respectively.
• some or all of antennas 1701 may be directional and only utilize certain transmission and reception angles. This may be used to either increase the gain off the signal (e.g. using a dish antenna), or can be used to limit the return path transmissions to certain angles to avoid interfering with other receivers using a similar frequency.
• One desirable frequency range to use for tropospheric communications is above 12 GHz.
• Some of the 12 GHz band is currently used in the US for Direct Broadcast Satellite (DBS) communications.
• DBS Direct Broadcast Satellite
• Typically, DBS radio signals are transmitted from geostationary satellites, and a consumer has a dish installed on the roof of his home (or someplace where the dish as a view of the southern sky in the direction of the desired satellite).
• the satellite signal is received at angle 1410 of Figure 14, and then is collected by dish 1401 and reflected to antenna and low-noise block (LNB) 1402.
• LNB antenna and low-noise block
• Some satellite dishes 1401 are constructed to receive satellite signals from 2 or 3 angles, and reflect them to multiple LNBs 1402.
• the 12 GHz band is largely unutilized in the US except for this purpose. Because of the high frequency 12 GHz is easily absorbed by various terrestrial
• the DIDO troposcatter system described above, and illustrated in Figures 11 and 13 is used at the same frequency as DBS satellite transmission 1410, but the base station antennas (either 1 102 or 1341 -1344) are positioned and angled such that the angle(s) of RF reflection from the common volume(s) 1 121 or 1321 -1324 are such that they will not be reflected by the satellite dishes 1401 into their LNBs 1402.
• This can be accomplished by placing the base station antennas 1 102 or 1341 -1344 at angles so that they never transmit in the same direction as the satellite signal 1410 (e.g. always transmit from the north, since all geosynchronous satellites transmit from the south), or choose an elevation angle for the transmission such that the RF reflection 1420 back to the ground bounces away from the LNBs 1402.
• the 12 GHz troposcatter approach just described not only applies to DIDO systems, but can be also used for 1 -way conventional broadcast without return path or spatial multiplexing. In this case, each client receiver would receive the same signal.
• both the base station 1 101 and the client station 1 102 have multiple antennas, and each receiver creates a full H matrix after training, and then inverts that matrix and multiplies it by the received data from the multiple antennas.
• the configuration of a conventional MIMO system is show in Figure 1 ,
• Embodiments of the invention may include various steps as set forth above.
• the steps may be embodied in machine-executable instructions which cause a general-purpose or special-purpose processor to perform certain steps.
• the various components within the Base Stations and Client Devices described above may be implemented as software executed on a general purpose or special purpose processor.
• various well known personal computer components such as computer memory, hard drive, input devices, . . . etc, have been left out of the figures.
• the various functional modules illustrated herein and the associated steps may be performed by specific hardware components that contain hardwired logic for performing the steps, such as an application-specific integrated circuit (“ASIC") or by any combination of programmed computer components and custom hardware components.
• ASIC application-specific integrated circuit
• certain modules such as the Coding, Modulation and Signal Processing Logic 603 described above may be implemented on a
• DSP programmable digital signal processor
• a DSP using a Texas Instruments' TMS320x architecture e.g., a TMS320C6000, TMS320C5000, . . . etc.
• the DSP in this embodiment may be embedded within an add-on card to a personal computer such as, for example, a PCI card.
• a variety of different DSP architectures may be used while still complying with the underlying principles of the invention.
• Elements of the present invention may also be provided as a machine- readable medium for storing the machine-executable instructions.
• the machine- readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of machine-readable media suitable for storing electronic instructions.
• the present invention may be downloaded as a computer program which may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection).
• a method comprising:
• tropospheric scatter to transmit a training signal from each antenna of a base station to each of a plurality of client devices, each of the client devices analyzing each training signal to generate channel characterization data, and transmitting the channel characterization data back to the base station;
• tropospheric scatter uses tropospheric scatter to transmit the precoded data signals through each antenna of the base station to each respective client device.
• a method comprising: transmitting a training signal from each antenna of a base station to each of a plurality of client devices utilizing tropospheric scatter, each of the client devices analyzing each training signal to generate channel characterization data, and transmitting the channel
• the channel characterization data for each of the plurality of client devices; receiving data to be transmitted to each of the client devices; and precoding the data using the channel characterization data associated with each respective client device to generate precoded data signals for each antenna of the base station; and transmitting the precoded data signals through each antenna of the base station to each respective client device.
• Prior art multi-user wireless systems may include only a single base station or several base stations.
• a single WiFi base station (e.g., utilizing 2.4 GHz 802.1 1 b, g or n protocols) attached to a broadband wired Internet connection in an area where there are no other WiFi access points (e.g. a WiFi access point attached to DSL within a rural home) is an example of a relatively simple multi-user wireless system that is a single base station that is shared by one or more users that are within its transmission range. If a user is in the same room as the wireless access point, the user will typically experience a high-speed link with few transmission disruptions (e.g.
• TDMA time-division multiplexed access
• MIMO multiple-input multiple- output
• MU-MIMO multiuser MIMO
• Spatial diversity is a function of antenna spacing and multipath angular spread in the wireless links.
• transmit antennas at a base station are typically clustered together and placed only one or two wavelengths apart due to limited real estate on antenna support structures (referred to herein as "towers," whether physically tall or not) and due to limitations on where towers may be located.
• multipath angular spread is low since cell towers are typically placed high up (10 meters or more) above obstacles to yield wider coverage.
• a given sector of a given cell ends up being a shared block of DL and UL spectrum among all of the users in the cell sector, which is then shared among these users primarily in only the time domain.
• cellular systems based on Time Division Multiple Access (TDMA) and Code Division Multiple Access (CDMA) both share spectrum among users in the time domain.
• EFS-Web 6181 P515X typically independent of the user's location, but the available data rate varies depending on the link quality between the user and the base station. For example, a user further from a cellular base station will typically have less available data rate than a user closer to a base station. Since the data rate is typically shared among all of the users in a given cellular sector, the result of this is that all users are impacted by high data rate demands from distant users with poor link quality (e.g. on the edge of a cell) since such users will still demand the same amount of data rate, yet they will be consuming more of the shared spectrum to get it.
• a mobile ad hoc network (see http ://en . wi ki pedia.org/w ki/ Mobile ad hoc network) is an example of a cooperative self-configuring network intended to provide peer-to-peer communications, and could be used to establish communication among radios without cellular infrastructure, and with sufficiently low-power communications, can potentially mitigate interference among simultaneous transmissions that are out of range of each other.
• MANET mobile ad hoc network
• the techniques utilized for simultaneous spectrum utilization among base stations and multiple users achieve the simultaneous spectrum use by multiple users by mitigating interference among the waveforms to the multiple users. For example, in the case of 3 base stations each using a different frequency to transmit to one of 3 users, there interference is mitigated because the 3 transmissions are at 3 different frequencies. In the case of sectorization from a base station to 3 different users, each 180 degrees apart relative to the base station, interference is mitigated because the beamforming prevents the 3 transmissions from overlapping at any user.
• cellularization, sectorization not only typically suffer from increasing the cost of the multi-user wireless system and/or the flexibility of deployment, but they typically run into physical or practical limitations of aggregate throughput in a given coverage area. For example, in a cellular system, there may not be enough available locations to install more base stations to create smaller cells. And, in an MU-MIMO system, given the clustered antenna spacing at each base station location, the limited spatial diversity results in asymptotically diminishing returns in throughput as more antennas are added to the base station.
• the users in the multi-user wireless system are subject to interference from multiple base stations and/or ad hoc transceivers (e.g. in the event of the malfunction of a component of a multi-user wireless system), then it can result in a situation where the aggregate throughput of the multi-user wireless system is dramatically reduced, or even rendered non-functional..
• Prior art multi-user wireless systems add complexity and introduce limitations to wireless networks and frequently result in a situation where a given user's experience (e.g. available bandwidth, latency, predictability, reliability) is impacted by the utilization of the spectrum by other users in the area.
• a given user's experience e.g. available bandwidth, latency, predictability, reliability
• the utilization of the spectrum by other users in the area e.g. available bandwidth, latency, predictability, reliability
• prior art multi-user wireless technology suffers from many limitations. Indeed, with the limited availability of spectrum suitable for particular types of wireless communications (e.g. at wavelengths that are efficient in penetrating building walls), it may be the case that prior art wireless techniques will be insufficient to meet the increasing demands for bandwidth that is reliable, predictable and low-latency.
• FIG. 1 illustrates a prior art MIMO system.
• FIG. 2 illustrates an A/-antenna Base Station communicating with a plurality of Single-antenna Client Devices.
• FIG. 3 illustrates a three Antenna Base Station communicating with three Single-Antenna Client Devices
• FIG. 4 illustrates training signal techniques employed in one
• FIG. 5 illustrates channel characterization data transmitted from a client device to a base station according to one embodiment of the invention.
• FIG. 6 illustrates a Multiple-Input Distributed-Output ("MIDO") downstream transmission according to one embodiment of the invention.
• MISO Multiple-Input Distributed-Output
• FIG. 7 illustrates a Multiple-Input Multiple Output (“MIMO") upstream transmission according to one embodiment of the invention.
• MIMO Multiple-Input Multiple Output
• FIG. 8 illustrates a base station cycling through different client groups to allocate throughput according to one embodiment of the invention.
• FIG. 9 illustrates a grouping of clients based on proximity according to one embodiment of the invention.
• FIG. 10 illustrates an embodiment of the invention employed within an NVIS system.
• FIG. 11 illustrates an embodiment of the DIDO transmitter with l/Q compensation functional units.
• FIG. 12 a DIDO receiver with l/Q compensation functional units.
• FIG. 13 illustrates one embodiment of DIDO-OFDM systems with l/Q compensation.
• FIG. 14 illustrates one embodiment of DIDO 2 x 2 performance with and without l/Q compensation.
• FIG. 15 illustrates one embodiment of DIDO 2 x 2 performance with and without l/Q compensation.
• FIG. 16 illustrates one embodiment of the SER (Symbol Error Rate) with and without l/Q compensation for different QAM constellations.
• FIG. 17 illustrates one embodiment of DIDO 2 x 2 performances with and without compensation in different user device locations.
• FIG. 18 illustrates one embodiment of the SER with and without l/Q compensation in ideal (i.i.d. (independent and identically-distributed)) channels.
• FIG. 19 illustrates one embodiment of a transmitter framework of adaptive DIDO systems.
• FIG. 20 illustrates one embodiment of a receiver framework of adaptive DIDO systems.
• FIG. 21 illustrates one embodiment of a method of adaptive DIDO- OFDM.
• FIG. 22 illustrates one embodiment of the antenna layout for DIDO measurements.
• FIG. 23 illustrates embodiments of array configurations for different order DIDO systems.
• FIG. 24 illustrates the performance of different order DIDO systems.
• FIG. 25 illustrates one embodiment of the antenna layout for DIDO measurements.
• FIG. 26 illustrates one embodiment of the DIDO 2 x 2 performance with 4-QAM and FEC rate 1 ⁇ 2 as function of the user device location.
• FIG. 27 illustrates one embodiment of the antenna layout for DIDO measurements.
• FIG. 28 illustrates how, in one embodiment, DIDO 8 x 8 yields larger SE than DIDO 2 x 2 for lower TX power requirement.
• FIG. 29 illustrates one embodiment of DIDO 2 x 2 performance with antenna selection.
• FIG. 30 illustrates average bit error rate (BER) performance of different DIDO precoding schemes in i.i.d. channels.
• FIG. 31 illustrates the signal to noise ratio (SNR) gain of ASel as a function of the number of extra transmit antennas in i.i.d. channels.
• SNR signal to noise ratio
• FIG. 32 illustrates the SNR thresholds as a function of the number of users ⁇ M) for block diagnalization (BD) and ASel with 1 and 2 extra antennas in i.i.d. channels.
• FIG. 33 illustrates the BER versus per-user average SNR for two users located at the same angular direction with different values of Angle Spread (AS).
• AS Angle Spread
• FIG. 34 illustrates similar results as FIG. 33, but with higher angular separation between the users.
• FIG. 35 plots the SNR thresholds as a function of the AS for different values of the mean angles of arrival (AOAs) of the users.
• AOAs mean angles of arrival
• FIG. 36 illustrates the SNR threshold for an exemplary case of five users.
• FIG. 37 provides a comparison of the SNR threshold of BD and ASel, with 1 and 2 extra antennas, for two user case.
• FIG. 38 illustrates similar results as FIG. 37, but for a five user case.
• FIG. 39 illustrates the SNR thresholds for a BD scheme with different values of AS.
• FIGS. 43-44 illustrate the SNR thresholds as a function of the number of users ⁇ M) and angle spread (AS) for BD and ASel schemes, with 1 and 2 extra antennas, respectively.
• FIG 45 illustrates a receiver equipped with frequency offset
• FIG. 46 illustrates DIDO 2 x 2 system model according to one embodiment of the invention.
• FIG. 47 illustrates a method according to one embodiment of the invention.
• FIG. 48 illustrates SER results of DIDO 2 x 2 systems with and without frequency offset.
• FIG. 49 compares the performance of different DIDO schemes in terms of SNR thresholds.
• FIG. 50 compares the amount of overhead required for different embodiments of methods.
• FIG. 52 illustrates results when turning off the integer offset estimator.
• FIG. 53 illustrates downlink spectral efficiency (SE) in [bps/Hz] as a function of mutual information in [bps/Hz].
• SE downlink spectral efficiency
• FIG. 54 illustrates average per-user symbol error rare (SER) performance as a function of the mutual information in [bps/Hz].
• FIG. 55 illustrates average per-user SER performance as a function of the minimum mutual information in [bps/Hz] and the thresholds used to switch between different DIDO modes.
• FIG. 56 illustrates average per-user SER vs. SNR for fixed modulation and adaptive DIDO systems.
• FIG. 57 illustrates downlink SE vs. SNR for fixed modulation and adaptive DIDO systems.
• FIG. 58 illustrates average per-user SER vs. SNR for adaptive DIDO systems with different thresholds.
• FIG. 59 illustrates downlink SE vs. SNR for adaptive DIDO systems with different thresholds

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