Abstract:
A method and apparatus for controlling an antenna array for wireless communication are described. The method is applied at a receiver and it uses a least squares algorithm for recovering the spatial signature of each of a plurality of signals transmitted simultaneously by a plurality of transmitters. The spatial signature is used for controlling the antenna array in order to achieve directional reception in a wireless communication system and suppress co-channel interference. The method can be used either in single transmitter configurations for smart antenna reception or multi-transmitter configurations for space-division multiple access systems. Furthermore, it can be used in conjunction with multi-carrier modulation signaling.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    (1) Field of the Invention  
           [0002]    The invention relates to wireless communications, in particular to method for controlling an antenna array for burst wireless communications  
           [0003]    (2) Brief Description of Related Art  
           [0004]    In the area of burst wireless communications the directional signal transmission and reception enhance all the performance metrics of the communication links such as range, throughput rate, emitted signal power, power dissipation, as well as link reliability and interference immunity. Directionality is achieved by employing an antenna array controlled by a beamformer logic at the transmitter site and a signal combiner logic at the receiver site. Antenna arrays can also be coupled with logic for supporting multiple communication links with spatially separated users that share the same spectrum and time frame. For example, spatial division multiple access (SDMA) systems are based on this notion. The above pieces of logic can be modeled in many different ways [1]. However, incorporating high performance adaptation techniques in practical applications is a highly non-trivial task because of the computational complexity factor.  
           [0005]    A number of different methods for diversity combining and beamforming for burst wireless communications systems have been proposed. However, these methods suffer from one or more weaknesses such as the need of unrealistic modeling assumptions, high computational complexity, slow convergence and the need of coupling with ad-hoc algorithms that alleviate the above.  
           [0006]    In [2] an algorithm for diversity combining is proposed. The performance of this algorithm is very good but the required computational complexity is high since the algorithm is based on joint space-frequency domain signal processing.  
           [0007]    In [3] and [4], two categories of algorithms for diversity combining and beamforming are reviewed. In the first category, the direction of arrival (DOA) of the beam needs to be identified at the receiver. This presents many deficiencies. First, DOA estimation is an extremely computation intensive process that cannot be implemented efficiently in the current art of semiconductor technology, thus it cannot find applications in high volume consumer products. Second, the DOA estimation methods are very sensitive to model imperfections such as antenna element intervals and antenna array geometry. Third, the number of antenna elements in the antenna array limits the number of multipaths and interferers DOA based methods can cope with.  
           [0008]    In the second category, a training sequence is required along with an estimation of the correlation with this training sequence and the input signal correlations. Although the problems of the algorithms in the previous paragraph are avoided, the need for estimating the correlations of the input signals introduces a low algorithm convergence rate, especially in relation to multicarrier wireless communication systems. For instance, averaging over a particular subcarrier requires multiple multicarrier symbols.  
         SUMMARY OF THE INVENTION  
         [0009]    An object of the present invention is to provide a method for controlling an antenna array appropriate for burst wireless communications. Another object of this invention is to provide spatial feature processing, performed independently of the time or frequency. Still another object of this invention is to provide a computationally efficient framework applicable to a wide spectrum of applications. This method exhibits smart antenna characteristics for the receiver including co-channel interference suppression and multi-user support. Also it can be applied in burst wireless systems employing the Orthogonal Frequency Division Multiplexing (OFDM) signaling scheme.  
           [0010]    These objects are achieved by using a least squares algorithm for controlling an antenna array in order to achieve directional reception and suppress co-channel interference in a burst wireless OFDM communication system. The invention also features novel physical layer processing for an SDMA system.  
           [0011]    The advantages of this invention are as follows:  
           [0012]    Enables non-line of sight communication.  
           [0013]    Improves the reliability and performance of the wireless communication system in the presence of interference.  
           [0014]    Exploits spatial diversity in order to support multiple users at the same frequency spectrum and time frame, thus it increases dramatically the communication capacity.  
           [0015]    Low computational complexity allowing the use of this method in devices targeting the consumer market.  
           [0016]    Fast convergence.  
           [0017]    No assumption of the statistical characteristics of the signal or the channel is necessary.  
           [0018]    No assumption about the antenna array geometry is necessary, while the method is immune to antenna element placement and element interval inaccuracies.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0019]    [0019]FIG. 1. Block diagram of a wireless communications receiver employing multiple diversity combiner means according to the present invention  
         [0020]    [0020]FIG. 2A. Flowchart of operation for a diversity combining means according to the invention;  
         [0021]    [0021]FIG. 2B. Explanatory details table for the diversity combining means in FIG. 2A 
         [0022]    [0022]FIG. 3. Frequency domain diversity combiner for multiple user configuration  
         [0023]    [0023]FIG. 4. First time domain diversity combiner for multiple user configuration  
         [0024]    [0024]FIG. 5. Second time domain diversity combiner for multiple user configuration  
         [0025]    [0025]FIG. 6. Frequency domain diversity combiner for single user configuration  
         [0026]    [0026]FIG. 7. First time domain diversity combiner for single user configuration  
         [0027]    [0027]FIG. 8. Second time domain diversity combiner for single user configuration  
         [0028]    [0028]FIG. 9. Simplified frequency domain diversity combiner for multiple user configuration  
         [0029]    [0029]FIG. 10. Simplified first time domain diversity combiner for multiple user configuration  
         [0030]    [0030]FIG. 11. Second simplified time domain diversity combiner for multiple user configuration 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0031]    With reference to FIG. 1, a wireless communications receiver  10  in accordance with a first preferred embodiment of the present invention receives a plurality of M input signals, for example M=4, using an array of antenna elements  11 - 1  though  11 - 4 . Each of the signals received in the antenna array is a sum of a plurality of K, for example K=3, useful information signals, as well as noise and/or interference signals. The useful information signals are generated by respective transmitter devices and they share essentially the same frequency spectrum. Each information signal is characterized by a frame comprising a known training sequence and an information data sequence. The transmitting devices and the receiver  10  are synchronized so that the receiver is aware of the starting time instants and ending time instants of the training sequences and the information data sequences of all transmitting devices. Also, the training sequences used for channel estimation are known to the receiver that performs a joint channel estimation for the channel pertaining to each transmitting device. The receiver  10  further comprises a plurality of diversity combiners  20 - 1  through  20 - 3 . A diversity combiner  20 -I, where I takes values in the range 1 through 3, is coupled to receive input from all M antenna elements in the antenna array constituting a sequence of M-element received samples and produce as output a sequence of scalar estimated logic levels Data-I and a sequence of M-element reconstructed input samples ReconstrI. Also,  20 -I is coupled to receive as input the sum of the reconstructed input samples of all diversity combiners in the receiver excluding the I th  one. Note that a receiver in accordance to this preferred embodiment of the present invention may use any number M&gt;1 of antenna elements in the antenna array, while the number of diversity combiners K can take any value in the set 1, 2, . . . , M. As an example, the description in FIG. 1 uses the numbers M=4 and K=3.  
         [0032]    With reference to FIG. 2A and FIG. 2B, a detailed flowchart of the operation of the diversity combiner  20 -I is used by the receiver in FIG. 1. The flowchart begins with power up  201 . When a sequence of received training samples is received in block  202  a sequence of four-step processing takes place. In the first step  204  a combiner weight vector of length M is computed using a least squares algorithm based on the sequence of received training samples and the known training sequence. In the second step  205  the channel responses respective to each antenna element of the antenna array are estimated on the basis of the received training samples and the known training sequence. In the third step  206  a weighted channel response is computed based on the said channel responses and the combiner weight vector. In the fourth step  207  the combiner weight vector and the weighted channel response are stored in a memory means. When a sequence of received information data samples is received in block  203  a sequence of seven-step processing takes place. In the first step  208  the combiner weight vector and the said weighted channel response are resumed from the memory means. In the second step  209  a sequence of scalar weighted samples is computed on the basis of the received M-element information data samples and the combiner weight vector. In the third step  210  the sequence of weighted samples are fed to a channel equalization unit and the equalized data is properly sliced to produce a sequence of estimated logic levels. In the fourth step  211  the M-element input samples are properly delayed to achieve time alignment with their respective estimated scalar logic levels. In the fifth step  212  the sequence of estimated scalar logic levels is properly transformed to produce a sequence of M-element reconstructed input samples. In the sixth step  213  the sequence of estimated logic levels is fragmented into a number of equal length data fragments and then the data fragments are periodically sampled. In particular for multi-carrier communication systems, a fragment may correspond to one multi-carrier symbol. Furthermore, similar fragmentation and sampling is also applied on the input information data samples and the reconstructed input data samples in a way so that the sampled fragments of the received information data samples and the reconstructed input samples correspond and are time aligned to the respective estimated logic levels. In the seventh step  214  the data of each fragment is processed following a sequence of three sub-steps. In the first one  215  a fragment of modified information data samples is produced based on the respective received information data samples and the reconstructed input samples generated by all diversity combiners excluding  20 -I. In the second one  216  the combiner weight vector is updated using the least squares algorithm based on the sequence of modified information data samples and the respective fragment of estimated logic levels. In the third one  217  the updated combiner weight vector is stored back in the memory means.  
         [0033]    The flowchart described above is appropriate both for multi-user communication using directional reception and co-channel interference (CCI) suppression of single carrier or multi-carrier signals. Examples of the least squares algorithm are the Recursive Least Squares (RLS) algorithm and the Householder algorithm [1]. Furthermore, with reference to FIG. 1, the diversity combiners  20 - 1  through  20 - 3  may share a common least squares means on a time-sharing basis. This reduces the computational complexity of the receiver while it imposes a constraint in the period of fragment sampling affecting the convergence speed of the algorithm.  
         [0034]    With reference to FIG. 3, a diversity combiner  30 -I in accordance with a second preferred embodiment of the present invention performs the combining in frequency domain, while it uses a frequency domain training sequence T for computing the combiner weight vector. Diversity combiner  30 -I can be used for multi-user communication. In this case, there is one frequency domain training sequence T I  for each user I, I=1,2, . . . K. In any case, for simplicity the training sequence will be denoted with T. A frequency domain transforming means  301  is coupled to receive as input the sequence of M-element samples from the antenna elements and produce a sequence of M-element frequency domain samples. A switch-A means  302  controls a data input X, while a switch-B means  312  controls a decision input of the least squares means  303 . X is a matrix of size N×M and the decision input is a vector length N, where N is the length of the known training sequence. The M-element frequency domain samples respective to the training sequence and the known training sequence levels T are fed to the least squares means  303  through switch-A  302  and switch-B  312  respectively. The least squares means minimizes the quantity ||Xw−T|| 2  with respect to the M-element combiner weight vector w. The resulting vector w is stored in memory means  304 . Further, the N×M matrix X and the vector T of length N are also fed to a channel estimation means  306  that produces an estimate of the frequency domain channel responses arranged in the matrix H I  of size N×M for each user I=1,2, . . . K. For simplicity, the channel response will b denoted with H. A combiner means  305 -A is coupled to receive as input the sequence of the M-element frequency domain channel responses along with the combiner weight vector w resumed from the weight memory means  304  and produces a scalar weighted channel frequency response for use by the equalization means  307  operating on the information data. The M-element frequency domain samples respective to the information data sequence along with the combiner weight vector resumed from the weight memory means  304  are fed to the combiner means  305 -B in order to produce a scalar sequence of weighted data samples. Note that the combiner means  305 -A and  305 -B are identical. A channel equalization means  307  is coupled to receive as input the said weighted channel frequency response and the sequence of the weighted data samples and produce as output a sequence of equalized data. A decision making means  308  is coupled to receive as input the sequence of equalized data and produce as output a sequence of estimated logic levels Data-I. An array multiplier means  309  multiplies the sequence of estimated logic levels with the M×N channel matrix H and produces as output a sequence of M-element reconstructed input samples Reconstr-I. Also, the sequence of the estimated logic levels is fed to the switch-B means  312 . A delay means  310  is coupled to receive as input the sequence of frequency domain samples and delay them properly to align them in time with the sequence of reconstructed input samples. A subtraction means  311  subtracts the sum Reconstr-Sum of the reconstructed input samples produced by all diversity combiners in the receiver excluding  30 -I from the delayed frequency samples to produce a sequence of modified samples. The produced sequence is fed to the switch-A means  302 . The switch-A means  302  and switch-B means  312  function as gating circuits for the sequence of modified samples and the estimated logic levels respectively and they feed the least squares means  303  with periodical fragments of data. On the basis of each fragment of input data, the least squares means  303  produces a new updated value of the combiner weight vector w and stores it in the weight memory means  304 . When the frequency domain diversity combiner  30 -I is used for multi-carrier signals the said periodical fragments of data can be periodical multi-carrier symbols.  
         [0035]    With reference to FIG. 4, a diversity combiner  40 -I in accordance with a third preferred embodiment of the present invention performs the combining in time domain while it uses a frequency domain training sequence T for computing the combiner weight vector. The diversity combiner  40 -I also can be used for multi-user communication. A training sequence pre-processing means  401  is coupled to get as input the received sequence R of M-element samples from the antenna elements and estimate the time responses H of the M channels respective to the antennas, where H is a matrix of size N×M and N is the length of the training sequence T. For example, H can be computed as follows: 
           H=B·R   (1) 
         [0036]    where B is the inverse (or, in case of singularity, the pseudo-inverse) of the matrix 
           A=D   I ·diag{ T}·D   F   (2) 
         [0037]    with D I ,D F  being the inverse and forward transform domain conversion matrices of size N×N and diag{T} is an N×N diagonal matrix having the elements of the frequency domain training sequence T in its diagonal. For example, if  40 -I is used in relation with orthogonal frequency domain multiplexing (OFDM) signaling N can be equal to the OFDM symbol length and D I ,D F  will represent the inverse and forward Fourier transform matrices respectively. Equivalently, matrix B can be computed as follows:  
             B   =       ∑     n   ∈   S                                 1     T   n            d   n          d   n     *   T                   (   3   )                               
 
         [0038]    where T n  denotes the n th  sample of the training sequence, S is the set of indices corresponding to non-zero training samples, “T” denotes transposition, “*” denotes complex conjugation, and d n  denotes the n th  column of the matrix D I . The training sequence pre-processing means  410  computes also a vector t of combining samples on the basis of H using for example the formula:  
               t   =       [       t   1          t   2                   ⋯                   t   N       ]     T       ,       where                   t   n       =       ∑     m   =   1     M                            h   nm          2         ,     n   =   1     ,   …              ,   N           (   4   )                               
 
         [0039]    and h nm , n=1, . . . , N, m=1, . . . , M, are the elements of matrix H. The least squares means  403  receives H and t through switch-A  402  and switch-B  414  respectively and after normalizing H it produces the combiner weight vector w that minimizes the quantity ||Xw−t|| 2 . X is the result of normalizing H using for example: 
           X=Γ·H   (5) 
         [0040]    where Γ is a diagonal matrix Γ=diag[γγ 2  . . . γ N ], γ n ={square root}{square root over (maxt/t n )}, n=1, . . . , N, with maxt being the maximum of t n , n=1, . . . , N. Alternatively, the training pre-processing means  401  can be configured to compute a vector of combining samples ν according to 
         ν= A·[ 1/γ 1  1/γ 2  . . . 1/γ N ] T   (6) 
         [0041]    while the switch-A  402  is coupled to get as input the received samples R and the least squares means  403  is configured to minimize the quantity ||Rw−ν|| 2 . The combiner weight vector produced by the means  403  is stored in the weight memory means  404 . Further, each of the M channel responses of H is fed to a frequency domain transforming means  406 -A, the output of which is fed along with the combiner weight vector w resumed from the weight memory means  404  to a combiner means  405 -A. Means  405 -A produces a weighted channel frequency response to be used by equalization means  407 . The sequence of M-element received samples respective to the information data sequence along with the combiner weight vector resumed from the weight memory means  404  are fed to the combiner means  405 -B in order to produce a scalar sequence of weighted data samples. Note that combiner means  405 -A and  405 -B are identical. The frequency domain transforming means  406 -B transforms the sequence of weighted samples to a sequence of frequency domain samples. Note that the frequency domain transforming means  406 -A and  406 -B are identical. A channel equalization means  407  is coupled to receive as input the said weighted channel frequency response and the sequence of the weighted data samples and produce as output a sequence of equalized data. A decision making means  408  is coupled to receive as input the sequence of equalized data and produce as output a sequence of estimated logic levels Data-I. An array multiplier means  409  multiplies the logic levels Data-I with the channel response matrix Hi and produces as output a sequence of M-element reconstructed input samples in the time domain. An inverse frequency domain transforming means  410  transforms the frequency domain reconstructed input samples to the time domain reconstructed input samples Reconstr-I. A delay means  411  is coupled to get as input the sequence of received samples and delay them properly to align them in time with the sequence of reconstructed input samples. A subtraction means  412  subtracts the sum Reconstr-Sum of the reconstructed input samples produced by all diversity combiners in the receiver excluding  40 -I from the delayed samples to produce a sequence of modified samples. The produced sequence along with the sequence of the estimated logic levels are fed to the data pre-processing means  413  where the channel time responses and a combining vector are computed following the process described in respect with means  401  with the only difference of using the estimated logic levels instead of the known training sequence levels. The produced channel time responses and the combining vector are fed to the switch-A means  402  and the switch-B means  414  respectively. The switch-A means  402  and switch-B means  414  function as gating circuits and they feed the least squares means  403  with periodical fragments of data. Note that the data pre-processing means  413  may produce only the data fragments that are necessary for the operation of the least squares means  403 . On the basis of each fragment of input data, the least squares means  403  produces a new updated value of the combiner weight vector w and stores it in the weight memory means  404 . When the frequency domain diversity combiner  40 -I is used for multi-carrier signals the said periodical fragments of data will be multi-carrier symbols periodically sampled. When  40 -I is used for single carrier signals the means  406  and  410  will be omitted, while the channel estimation and equalization functions will take place in time domain.  
         [0042]    With reference to FIG. 5, a diversity combiner  50 -I in accordance with a fourth preferred embodiment of the present invention performs the combining in time domain while it uses a time domain training sequence t for computing the combiner weight vector. The diversity combiner  50 -I also can be used for multi-user communication. A channel estimator and channel length estimator means  503  is coupled to receive as input the sequence R of M-element samples r n,m , m=1, . . . , M, n=1, . . . , N, respective to the known training sequence of length N from the antenna elements and produce estimates of the channel time responses H of the M channels respective to the antennas by employing a time-domain channel estimation technique based e.g. on the zero forcing or the minimum mean squared error criterion [5], as well as coarse estimates of the lengths l m , m=1, . . . , M of these M channels. A coarse estimate refers to the large components of each channel time response that are summing up for example to the 70% of the total channel energy and in practical cases the resulting length does not exceed the number 5. A running average means  502  is coupled to get as input the received sequence R through a switch-A  501  and the coarse channel length estimates and produces a running average sequence X based on the formula:  
                 x     n   ,   m       =       ∑     j   =   1       l   m                       r       n   +   j     ,   m           ,     m   =   1     ,   …              ,   M   ,     n   =   0     ,   …              ,     N   -     l   m               (   7   )                               
 
         [0043]    where x n,m  denote the elements of X and l is the maximum of l m , m=1, . . . , M. A least squares means  504  is coupled to receive as input the known training sequence t through a switch-B means  515  and the sequence X and it produces a combiner weight vector w by minimizing the quantity ||Xw−t|| 2 . The resulting vector w is stored in a memory means  505 . Further, the M channel responses of H are fed along with the combiner weight vector w resumed from the weight memory means  505  to a combiner means  506 -A that produces a weighted channel time response. The weighted channel time response is subsequently fed to a frequency domain transforming means  507 -A that produces a weighted channel frequency response to be used later by the equalization means  508 . The sequence of M-element received samples respective to the information data sequence along with the combiner weight vector resumed from the weight memory means  505  are fed to the combiner means  506 -B in order to produce a scalar sequence of weighted data samples. Note that the combiner means  506 -A and  506 -B are identical. The frequency domain transforming means  507 -B transforms the sequence of weighted samples to a sequence of frequency domain samples. Note also that the frequency domain transforming means  507 -A and  507 -B are identical. A channel equalization means  508  is coupled to receive as input the said weighted channel frequency response and the sequence of the weighted data samples and produce as output a sequence of equalized data. A decision making means  509  is coupled to receive as input the sequence of equalized data and produces as output a sequence of estimated logic levels Data-I. The logic levels Data-I are fed to an inverse frequency domain transforming means  510  that produces a time domain estimated data sequence. An array convolution means  511  applies the convolution of the said time domain estimated data sequence with the channel time response matrix H and produces as output a sequence of M-element reconstructed input samples Reconstr-I. A delay means  512  is coupled to get as input the sequence of received samples and delay them properly to align them in time with the sequence of reconstructed input samples. A subtraction means  513  subtracts the sum Reconstr-Sum of the reconstructed input samples produced by all diversity combiners in the receiver excluding  50 -I from the delayed received samples to produce a sequence of modified samples. The produced sequence is fed to the switch-A means  501  and subsequently to the running average means  502 . Means  502  also receives as input the coarse length estimates of the time domain channel responses and produces a running average that is fed to the least squares means  504 . The switch-A means  501  and switch-B means  514  function as gating circuits for the sequence of modified samples and the said time domain estimated data sequence coming from means  511 , respectively, and they feed means  502  and  504  with periodical fragments of data. On the basis of each fragment of input data, the least squares means  504  produces a new updated value of the combiner weight vector w and stores it in the weight memory means  505 . When the frequency domain diversity combiner  50 -I is used for multi-carrier signals the said periodical fragments of data will be multi-carrier symbols periodically sampled. When  50 -I is used for single carrier signals the means  507  and  510  will be omitted and the channel estimation and equalization functions will take place in time domain.  
         [0044]    With reference to FIG. 6, a diversity combiner  60  in accordance with a fifth preferred embodiment of the present invention is appropriate for co-channel interference (CCI) in a wireless communication system comprising a single transmitter and a receiving device. The diversity combiner  60  is a reduced version of the diversity combiner  30 -I described with reference to FIG. 3. In particular, the array multiplier means  309  and the subtraction means  311  may be omitted.  
         [0045]    With reference to FIG. 7, a diversity combiner  70  in accordance with a sixth preferred embodiment of the present invention is appropriate for co-channel interference (CCI) in a wireless communication system comprising a single transmitter and a receiving device. The diversity combiner  70  is a reduced version of the diversity combiner  40 -I described with reference to FIG. 4. In particular, the array multiplier means  409 , the inverse frequency transforming means  410  and the subtraction means  412  may be omitted.  
         [0046]    With reference to FIG. 8, a diversity combiner  80  in accordance with a seventh preferred embodiment of the present invention is appropriate for co-channel interference (CCI) in a wireless communication system comprising a single transmitter and a receiving device. The diversity combiner  80  is a reduced version of the diversity combiner  50 -I described with reference to FIG. 5. In particular, the array convolution means  511  and the subtraction means  513  may be omitted.  
         [0047]    With reference to FIG. 9, a diversity combiner  90  in accordance with an eighth preferred embodiment of the present invention computes a combining weight vector on the basis of the received training sequence samples only. This diversity combiner is appropriate for communication systems where the directionality and other communication parameters do not change substantially within a frame. In this case, the diversity combiner  90  is appropriate for suppressing co-channel interference (CCI) in a single user wireless communication system, as well as for supporting multi-user wireless communication. The diversity combiner  90  is a reduced version of the diversity combiner  30 -I described with reference to FIG. 3. In particular, the means  309  and  311  that are related to the input reconstructed signals, as well as the means  302 ,  310  and  312  that are related to the update of the combining weight vector based on the information data signals may be omitted.  
         [0048]    With reference to FIG. 10, a diversity combiner  100  in accordance with a ninth preferred embodiment of the present invention computes a combining weight vector on the basis of the received training sequence samples only. This diversity combiner too is appropriate for communication systems where the directionality and other communication parameters do not change substantially within a frame. In this case,  100  is appropriate for suppressing co-channel interference (CCI) in a single user wireless communication system, as well as for supporting multi-user wireless communication. The diversity combiner  100  is a reduced version of the diversity combiner  40 -I described with reference to FIG. 4. In particular, the means  409 ,  410  and  412  that are related to the input reconstructed signals, as well as the means  402 ,  411  and  414  that are related to the update of the combining weight vector based on the information data signals may be omitted.  
         [0049]    With reference to FIG. 11, a diversity combiner  110  in accordance with preferred embodiment of the present invention computes a combining weight vector on the basis of the received training sequence samples only. This diversity combiner too is appropriate for communication systems where the directionality and other communication parameters do not change substantially within a frame. In this case, the diversity combiner  110  is appropriate for suppressing co-channel interference (CCI) in a single user wireless communication system, as well as for supporting multi-user wireless communication. The diversity combiner  110  is a reduced version of the diversity combiner  50 -I described with reference to FIG. 5. In particular, the means  511  and  513  that are related to the input reconstructed signals, as well as the means  501 ,  510 ,  512  and  514  that are related to the update of the combining weight vector based on the information data signals may be omitted.  
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