Abstract:
A mobile station is capable of detecting a signal in the wireless communication systems using the Multiple Input Multiple Output (MIMO). The mobile station includes an apparatus that determines a vector of signals received by several receiving antennas. The apparatus estimates a channel between transmitting antenna and receiving antenna; forms a channel matrix; establishes, based on the channel state data, an order for detecting symbols transmitted by different transmitting antennas; calculates weight coefficients for detecting the symbols in the MIMO system; detects the symbols serially in the established order on the basis of the received signal vector; calculates the Euclidean distance between the detected symbols and the symbol constellation points; determines values of the Logarithmic Likelihood Ratio (LLR) for estimating the soft output bit probability, and forms a group of the most probable candidate symbols from the points of the symbol constellation.

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) AND CLAIM OF PRIORITY 
       [0001]    The present application is related to and claims priority to an application entitled “ADVANCED METHOD FOR DECODING IN THE MIMO SYSTEM AND APPARATUS FOR IMPLEMENTING THEREOF” filed in the Russian Patent Office on Jan. 12, 2009 and assigned Serial No. 2009100150, the contents of which are incorporated herein by reference. 
       TECHNICAL FIELD OF THE INVENTION 
       [0002]    The present application relates generally to the radio communication field and particularly to the wireless communication systems using the Multiple Input Multiple Output (MIMO) principle and decoding technique based on the Ordered Successive Interference Cancellation (OSIC). 
       BACKGROUND OF THE INVENTION 
       [0003]    An effective signal detection tool is necessary for a high-quality communication in the MIMO-system. Specifically, a V-BLAST detection scheme, as disclosed in P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel,” in URSI International Symposium on Signals, Systems and Electronics, pp. 295-300, September), the contents of which hereby are incorporated by reference, employs the successive cancellation of the interference component, which does not require great computational resources and demonstrates good result when operating using rigid solutions at the output. However, this scheme includes a significant reduction in its effectiveness due to the maximum likelihood (ML) scheme that provides soft solutions, but is very strict to the computational resources. 
         [0004]    One technical solution is described in the US Patent Application No. 2008/0152032A. This application proposes the method and apparatus that permit to use the signal detection based on the OSIC in the MIMO systems, the signal detection allowing for estimating the output bit probability, thus obtaining soft solutions. 
         [0005]    The MIMO transmission system using m transmitting (Tx) antennas and n receiving (Rx) antennas is described Equation 1: 
         [0000]        y=Hx+v,   [Eqn. 1] 
         [0006]    where H is the channel matrix of size n×m, 
         [0007]    x=[x 1  x 2  . . . x m ] T  is the transmitted signal vector, 
         [0008]    y=[y 1  y 2  . . . y n ] T  is the received signal vector, 
         [0009]    v=[v 1  v 2  . . . v n ] T  is the noise component vector. 
         [0010]    The classification procedure regulating the sequence for determining the transmitted symbols is based on the principle of the maximal norm of the channel coefficient matrix column, which permits primarily to choose the Tx antenna having the maximal value of the channel coefficient vector. 
         [0011]    The detection method provides for estimating all possible transmitted signals layerwise, where the signal transmitted by one Tx antenna is regarded as the layer. Thus, such as in the case of the 16 QAM (quadrature amplitude modulation), sixteen (16) candidates are calculated first in the layer — 1, which is determined as the best according the aforementioned classification procedure. Using the MMSE-OSIC method, symbols belonging to other layers are detected for every symbol from the layer — 1, which results in forming sixteen (16) candidate vectors. K best candidates are separated amongst these sixteen (16) vectors, where K is a parameter which is set as K=3 for the example in the above application. The best candidates are determined in accordance with the minimum Euclidean distance criterion: 
         [0000]        d=∥y−Hx   x ∥ 2 ,  [Eqn. 2] 
         [0012]    where x i  is a candidate vector. Moreover, when calculating the Euclidean distance (2), the Logarithmic Likelihood Ratio (LLR) for the soft solution is determined: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       LLR 
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         [0013]    where i=1, . . . M; M is determined on the basis of the modulation type (M=4 in the case of the 16 QAM), 
         [0014]    S i,0 ={x|b i =0} means symbols, for which the i-th bit is ‘0,’ 
         [0015]    S i,1 ={x|b i =1} means symbols, for which the i-th bit is ‘1.’ 
         [0016]    Thereafter, by means of scanning all possible symbols in the layer 2 when fixing K symbols from the layer — 1, a next group of candidate vectors (consisting of K vectors) is determined. The solution vector for other layers is determined also using the MMSE-OSIC method. Thus, it is necessary to test m*K candidates. Values of the Logarithmic Likelihood Ratio (LLR) for the layer — 2 are calculated by the Equation 3. Additionally, the LLR values for the layer — 1 could be recalculated in the case, if a shorter Euclidean distance is obtained in comparison with the one calculated in the previous layer. Such procedure is similarly applied for all other layers. In order for processing every layer, the respective MMSE filter is determined: 
         [0000]        W   1 =( H   1   H   H   1 +σ 2   I   1 ) −1   H   1   H ,  [Eqn. 4A] 
         [0017]    where H 1 =[h 2  h 3  h m ] is the matrix H after exclusion of the column corresponding to the first layer, 
         [0000]        W   2 =( H   2   H   H   2 +σ 2   I   2 ) −1   H   2   H   H ,  [Eqn. 4B] 
         [0018]    where H 2 =[h 3  . . . h m ] is the matrix H after exclusion of the columns corresponding to the first and second layers. 
         [0000]        W   m-1 =( H   m-1   H   H   m-1 +σ 2   I   m-1 ) −1   H   m-1   H ,  [Eqn. 4C] 
         [0019]    where H m-1 =[h m ] is the last column of the matrix H. 
         [0020]    The disadvantage of the closest analogue consists in that, whereas the complexity degree of such a method is significantly lower than of the ML method, it is still nevertheless very high, especially in the case when the number of the Tx and Rx antennas is great. 
       SUMMARY OF THE INVENTION 
       [0021]    To address the above-discussed deficiencies of the prior art, it is a primary object to provide an improved method for detecting a signal in the MIMO system, which method possesses an accuracy close to the maximum likelihood technique, but less strict to the computational resources, and, second, in developing an apparatus for implementing such a method. 
         [0022]    In some embodiments, provided is a method for detecting a signal in the wireless communication systems using the Multiple Input Multiple Output (MIMO) principle. The method includes determining a vector of signals received by several receiving antennas; estimating a channel between every transmitting antenna Tx and every receiving antenna Rx; forming a channel matrix; establishing, based on the channel state data, an order for detecting symbols transmitted by different antennas Tx; calculating weight coefficients for detecting the symbols in the MIMO system; detecting the symbols serially in the established order on the basis of the received signal vector; calculating the Euclidean distance between the detected symbols and the symbol constellation points; determining values of the Logarithmic Likelihood Ratio (LLR) for estimating the soft output bit probability, and forming a group of the most probable candidate symbols from the points of the symbol constellation, wherein determining, at the stage n, the Euclidean distance by means of the normalized sum of the Euclidean distances 
         [0000]    
       
         
           
             
               
                 d 
                 
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                   , 
                   k 
                 
               
               = 
               
                 
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                   i 
                 
                 + 
                 
                   
                     d 
                     k 
                   
                   
                     σ 
                     n 
                     2 
                   
                 
               
             
             , 
           
         
       
     
         [0000]    where d i  is the Euclidean distance calculated at the previous stage for the candidate having the index i, σ n   2  is an estimation of the error variance in detecting the symbol at the stage n, d k  is the Euclidean distance between the detected symbol and the point k of the signal constellation. This latter distance is calculated for all constellation points and for every candidate symbol from the group of the candidates formed at the previous stage, then forming a new group of the candidate symbols based on the minimum normalized Euclidean sum principle; and thereupon canceling the candidate symbols from the updated received data vector. 
         [0023]    In some embodiments, provided is an apparatus that implements the claimed method for detecting a signal in the wireless communication systems using the Multiple Input Multiple Output (MIMO) principle. The apparatus is operable to employ several receiving antennas Rx. The apparatus includes a signal sorter establishing an order for detecting symbols, every of those symbols being transmitted using one transmitting antenna Tx; a weight calculator determining the weight coefficients of the filter for detecting symbols in the MIMO system; a symbol detector estimating a symbol transmitted by one antenna Tx and determining the estimation error variance; a Euclidean distance calculator and Logarithmic Likelihood Ratio (LLR) estimator determining the Euclidean distance between the detected symbol and the signal constellation points, as well as the Logarithmic Likelihood Ratio (LLR) value on the basis of the Euclidean distances; a best symbol candidate former determining the most probable symbol constellation points transmitted; a candidate canceller subtracting the symbol transmitted by one antenna Tx from the received signal vector. The input of the signal sorter is fed by an information on the parameters of a channel between every antenna Tx and every antenna Rx. The output of the signal sorter is connected with the first input of the weight calculator. The second input of the weight calculator is fed by an information on the signal/noise ratio. The output of the weight calculator is connected to the first input of the symbol detector. The second input of the symbol detector is fed by a received signal vector. The output of the symbol detector is connected with the input of the Euclidean distance calculator. The first output is connected with the second input of the symbol detector via the serially coupled best symbol candidate former and candidate canceller. The latter updates the received data vector without the eliminated candidate symbol. The updated vector is fed to the input of the symbol detector. The symbols transmitted by different antennas Tx are detected serially in accordance with the order determined by the signal sorter. The second output of the Euclidean distance calculator is the source for the data on the Logarithmic Likelihood Ratio (LLR) estimation. The apparatus structure distinguishes in that only the information on the detected symbol and detection error variance estimation is fed to the input of the Euclidean distance calculator. Thus, this module calculates the Euclidean distances to the constellation points and processes the LLR estimation only on the basis of the information on the detected symbol and modulation type. 
         [0024]    Before undertaking the DETAILED DESCRIPTION OF THE INVENTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document: the terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation; the term “or,” is inclusive, meaning and/or; the phrases “associated with” and “associated therewith,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like; and the term “controller” means any device, system or part thereof that controls at least one operation, such a device may be implemented in hardware, firmware or software, or some combination of at least two of the same. It should be noted that the functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. Definitions for certain words and phrases are provided throughout this patent document, those of ordinary skill in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0025]    For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts: 
           [0026]      FIG. 1  illustrates a block diagram of the MIMO receiver; 
           [0027]      FIG. 2  illustrates a block diagram of the MIMO detector in accordance with embodiments of the invention; 
           [0028]      FIG. 3  illustrates a process of the signal detection in accordance with embodiments of the invention; 
           [0029]      FIG. 4  illustrates the receiver operation (bit error probability) according to embodiments of the invention; and 
           [0030]      FIG. 5  illustrates the receiver operation (bit error probability) according to embodiments of the invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0031]      FIGS. 1 through 5 , discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged system. 
         [0032]      FIG. 1  illustrates a block diagram of the MIMO receiver. 
         [0033]    Referring to  FIG. 1 , the MIMO receiver  100  comprises a channel estimator  105 , a MIMO detector  110 , a de-interleaver  115  and an error correction decoder  120 . The channel estimator  105  estimates signals from multiple antennas and provides estimation results to the MIMO detector  110 . The MIMO detector  110  will be explained in  FIG. 2 . The de-interleaver  115  performs de-interleaving data provided from the MIMO detector  110 . The error correction decoder  120  performs decoding data provided from the de-interleaver  115 . 
         [0034]    The block diagram of the MIMO detector apparatus  200  implementing the proposed method is shown in  FIG. 2 . The signal sorter  205  determines an order for detecting symbols in accordance with the criterion of the minimum mean square error (MMSE) of the solution, or (in another embodiment) in accordance with the criterion of the maximal weight of the channel matrix column. This module is similar to a conventional channel sorter ( 312 ). The weight calculator  210  carries out the calculation of the MMSE matrix elements. This module is similar to a conventional homonymous module. The symbol detector  215  calculates the received symbol value by means of the MMSE technique. This module is similar to a conventional symbol detector. The Euclidean distance calculator and LLR estimator  220  performs the calculation of the Euclidean distances between the MMSE solution point and each point of the constellation for the given modulation type. The Euclidean distance calculator and LLR estimator  220  is similar in functionally to a conventional Euclidean calculator, however, it uses technique for calculating the Euclidean distances different from the prior art and uses other input data therefore. Conventional systems calculate the Euclidean distances between the received vector and the estimation of the received vector for the given set of the transmitted symbols. Thus, in convention systems, the received vector Y and the calculated estimation of this vector Hx are fed to the input of this module. 
         [0035]    In embodiments of the present disclosure, however, the MMSE solution (the point on the complex plane) for the layer being processed is fed to the input of the Euclidean distance calculator and LLR estimator  220 , which requires significantly less amount of the input data and simplifies the Euclidean distance calculation. Simultaneously, the Euclidean distance calculator and LLR estimator  220  performs the calculation of the LLR values. The best symbol candidate former  225  forms the group of symbol constellation points that include the least Euclidean distance relative to the point of the MMSE solution. This module is similar functionally to a conventional candidate group selector. The candidate canceller  230  subtracts the candidate symbol from the received signal vector. This module is similar to a conventional symbol substitution unit. 
         [0036]      FIG. 3  illustrates a process of the signal detection in accordance with embodiments of the invention. 
         [0037]    Referring to  FIG. 3 , in order for achieving the claimed result, the detection procedure the following stages 
         [0038]    Stage 0: channel information, SNR information and received vector for y are obtained in step  300 . 
         [0039]    Stage 1: the channel matrix H is estimated using the channel information. 
         [0040]    Stage 2: the MMSE filters W i  are determined using the SNR information in accordance with the Equation 4 in step  301 , where i=0, . . . , m−1, the index 0 corresponds to the full matrix H, and the order (execution queue) of the MMSE-OSIC procedure is determined on the basis of the minimum error variance in the MMSE solution. This order is determined by sorting the moduli of the diagonal elements of the matrix WH, the maximal element corresponding to the first layer, then the sorting procedure is repeated for the reduced matrix H 1  using the diagonal elements W 1 H 1 , and so forth up to the last layer. 
         [0041]    Stage 3: the solutions are obtained using the received vector y using the MMSE filter (weight coefficients) for the layer — 1, where the index “1” means the best layer for the sorting procedure in step  302 : {circumflex over (x)} 1 =wy, where w is a row of the MMSE filter matrix, corresponding to the best layer and symbol is estimated from one TX antenna in step  303 . 
         [0042]    Stage 4: K best candidate symbols are determined for the layer — 1 based on the minimal Euclidean distance between the detected symbol {circumflex over (x)} 1  and signal constellation points in step  304  and step  305 : d=∥{circumflex over (x)} 1 −A k ∥ 2  where A k εS, and the LLR values are determined for the layer — 1: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0043]    where σ 1   2  is the complex estimation of the error variance for the layer — 1. 
         [0044]    Stage 5: when all processes for symbols are not finished in step  306 , the candidate symbols A k  of the layer — 1 are cancelled from the received vector y in step  307 : y 1,k =y−A k h 1 , k=1, K, where h k  is the column of the matrix H corresponding to the layer — 1. Respectively, K updated received vectors y 1  are obtained with the cancelled layer — 1. 
         [0045]    Stage 6: repeating the stages from 3 to 5 for other layers (repeating with next Tx layer) is determined, the Euclidean distance d in the layer k (k&gt;1) being determined in accordance to the equation: 
         [0000]    
       
         
           
             
               
                 
                   
                     d 
                     = 
                     
                       
                         
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         [0046]    where i means the number of the candidate determined at the previous layer, the second index at y and the index at σ mean the number of the layer. For example, d 1,1  means the Euclidean distance determined for the candidate ‘1’ in the layer — 1, d 1,2  means the Euclidean distance determined for the candidate ‘1’ in the layer — 2, and so forth. 
         [0047]    Once the Euclidean distances are determined, that is, when all processes for symbols are finished in step  306  the LLR values are calculated for the layer k in step  308  in accordance with the equation: 
         [0000]    
       
         
           
             
               
                 
                   
                     
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         [0048]    where d p , d t  mean the Euclidean distances between the MMSE solution in the layer k and the constellation point having the proper bit b i . 
         [0049]    All other layers can be verified, excluding the first layer. K*M candidates should be taken into account when deriving the best candidates and calculating the LLR, where K is the number of candidates that are monitored after processing the previous layer, M is the number of points in the constellation. 
         [0050]    Simplification of the proposed algorithm, in comparison with conventional systems, is achieved by means of canceling a series of the calculations therefrom. Particularly, when obtaining the solution for every layer excluding the first layer, K*M variants of the vector y i  should be taken into account, while the solution in accordance with embodiments take into account only K variants. It should be noted that obtaining the MMSE solution for every layer requires m complex multiplications. Moreover, the conventional systems determined the Euclidean distance as a squared norm of the difference between the received vector y and Hx (see Equation 2), where the number of candidates is K*M. Even when taking into account that x belongs to the fixed constellation (components x are integers) and, therefore, the multiplication of Hx could be substituted by summing, the number of calculations remains great. In some embodiments, the Euclidean distance is calculated as the distance between the MMSE solution x, (which is simply a point in the complex space) and signal constellation points A E S. Therefore, in the case of the QAM modulation, the squared norm of the difference between two complex values is calculated rather than between vectors. 
         [0051]    In comparison with conventional systems, certain additional calculations are needed. Particularly, the Euclidean distance d is calculated using the Equation 5, which requires additional multiplications for summing fractions. Moreover, the MMSE filter is calculated for the full matrix H. However, these additional calculations are disparately small in comparison with the aforementioned complexity reduction. Table 1 provides a comparison of the number of the needed multiplications in the claimed algorithm and in conventional systems for the MIMO system 4×4 and the 16 QAM modulation. Four (4) candidates are used in the both algorithms. It should be noted that due to the fact that, in the case of 16 QAM modulation, the signals being transmitted are represented by integers (i.e., ±1, ±3), the multiplication can be substituted in many cases by an addition that requires significantly less hardware resources than the multiplication. This distinctive feature was taken into account when estimating the number of needed multiplications in the conventional systems and in the algorithm according to embodiments of the present disclosure. 
         [0052]    It should be also noted that, since the Euclidean distance in the conventional method is determined in accordance with the Equation 2, denying from multiplications in Hx and Wy i  results in a great number of addition operator, which in any case increases the solution time. In embodiments of the present disclosure, this operation is absent. 
         [0000]    
       
         
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                 New method 
                   
               
               
                   
                   
                 according to 
               
               
                   
                 Conventional 
                 embodiments of the 
               
               
                 Algorithm 
                 Method (4 
                 present disclosure (4 
               
               
                 components 
                 candidates are used) 
                 candidates are used) 
                 Comments 
               
               
                   
               
             
             
               
                 Calculating the 
                 189 
                 472 
                 The new method 
               
               
                 weight coefficient 
                   
                   
                 additionally calculates 
               
               
                 matrix 
                   
                   
                 4×4 MMSE matrix, and 
               
               
                   
                   
                   
                 the conventional 
               
               
                   
                   
                   
                 method uses only 3×3 
               
               
                   
                   
                   
                 and lesser matrices 
               
               
                 Applying the 
                 144 
                 160 
                 Obtaining solutions 
               
               
                 weight coefficient 
                   
                   
                 using the MMSE 
               
               
                 matrix 
                   
                   
                 filtration 
               
               
                 Calculating LLR 
                 128 + 480 * 3 
                 32 + 128 * 3 + 21 
                 In order for obtaining 
               
               
                 for 4 layers 
                   
                   
                 the normalized 
               
               
                   
                   
                   
                 Euclidean distance in 
               
               
                   
                   
                   
                 the claimed method, 21 
               
               
                   
                   
                   
                 additional 
               
               
                   
                   
                   
                 multiplications are 
               
               
                   
                   
                   
                 utilized 
               
               
                 Total 
                 1901  
                 1069  
                 Complexity degree 
               
               
                   
                   
                   
                 ratio is 1.778:1 
               
               
                   
               
             
          
         
       
     
         [0053]      FIG. 4  and  FIG. 5  illustrate operational characteristics of the claimed method in comparison with conventional systems and standard linear MMSE detector. While the claimed method demonstrates some deterioration in comparison with conventional systems, it can be significantly better than the standard MMSE detector. This can be considered as a good compromise between the effectiveness and complexity for applying in specific devices. 
         [0054]      FIG. 4  illustrates the receiver operation (bit error probability) according to embodiments of the invention; and, where the proposed solution is compared with the MMSE MIMO receiver and a conventional system. The simulation was carried out for the 2×2 MIMO V-BLAST system (each stream has the 16 QAM modulation) corresponding to the IEEE 802.16e standard having the convolution encoder and Viterbi decoder. The channel model is 3GPP/25.943/RA-10. The signal/noise ratio (SNR) is determined as the ratio of the signal energy irradiated by all Tx antennas to the noise energy in every Rx antenna. 
         [0055]      FIG. 5  illustrates the receiver operation (bit error probability) according to embodiments of the invention, where the proposed solution is compared with the MMSE MIMO receiver and a conventional system. The simulation was carried out for the 4×4 MIMO V-BLAST system (each stream has the 16 QAM modulation) corresponding to the IEEE 802.16e standard having the convolution encoder and Viterbi decoder. The channel model is 3GPP/25.943/RA-10. The signal/noise ratio (SNR) is determined as the ratio of the signal energy irradiated by all Tx antennas to the noise energy in every Rx antenna. 
         [0056]    It is important for the effective operation of the new method that the detection order is set in accordance with the MMSE-OSIC procedure and, respectively, the symbol detection is performed using the weight matrix MMSE. 
         [0057]    The embodiment of the new method is also possible, wherein the detection order is set in accordance with the Zero-Forcing procedure and, respectively, the symbol detection is performed using the Zero-Forcing weight matrix. 
         [0058]    The embodiment of the new method is also possible, wherein the detection order is set in accordance with the descending sequence of the norms of the matrix columns. 
         [0059]    It is important for the effective operation of the new method that the Euclidean distance and the LLR value are calculated in accordance with the equation: 
         [0000]    
       
         
           
             
               
                 LLR 
                  
                 
                   ( 
                   
                     b 
                     i 
                   
                   ) 
                 
               
               = 
               
                 ( 
                 
                   
                     
                       min 
                       
                         
                           A 
                           p 
                         
                         ∈ 
                         
                           S 
                           
                             i 
                             , 
                             0 
                           
                         
                       
                     
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                       p 
                     
                   
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                      
                     
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                       t 
                     
                   
                 
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             , 
           
         
       
     
         [0000]    where d p , d 1  mean the Euclidean distances to the signal constellation points having the i-th bit  0  (A p εS i,0 ) and to points having the i-th bit  1  (A p εS i,1 ). 
         [0060]    The hardware implementation of the concept is illustrated in  FIG. 1  and  FIG. 2 , and the operation of the method is explained in  FIG. 3 . In so doing, the new algorithm is simplified significantly, therefore it can be readily applied in the MIMO-OFDM systems being developed. 
         [0061]    Although the present disclosure has been described with an exemplary embodiment, various changes and modifications may be suggested to one skilled in the art. It is intended that the present disclosure encompass such changes and modifications as fall within the scope of the appended claims.