Abstract:
Disclosed is a soft decision decoder, and a log likelihood ratio calculator for soft decision decoding and a method thereof, for realizing a generalized log likelihood ratio algorithm in consideration of channel estimation errors for performing soft decision decoding on QAM signals, which comprises multipliers for multiplying reference signals and channel estimation signals, subtractors for subtracting a receive signal of a receiver from output signals of the multipliers, first and second square calculators for squaring the subtractors&#39; output signals and reference signals, dividers for dividing output signals of the first and second square calculators by addition values, and a comparator for comparing output signals of the dividers.

Description:
CROSS REFERENCE TO RELATED APPLICATION  
         [0001]    This application claims priority to and the benefit of Korea Patent Application No. 2002-55302 filed on Sep. 12, 2002 in the Korean Intellectual Property Office, the content of which is incorporated herein by reference.  
         BACKGROUND OF THE INVENTION  
         [0002]    1. (a) Field of the Invention  
           [0003]    The present invention relates to a soft decision decoder, and a log likelihood ratio calculator and method thereof for soft decision decoding. More specifically, the present invention relates to a soft decision decoder for realizing a generalized log likelihood ratio algorithm in consideration of channel estimation errors for better performance in soft decision decoding of QAM (quadrature amplitude modulation) signals, and a log likelihood ratio calculator and method thereof for soft decision decoding.  
           [0004]    2. (b) Description of the Related Art  
           [0005]    As information communication techniques have evolved to mobilization and multimedia stages, the QAM method has become the most effective modulation method from among the currently used modulation schemes for realizing high-speed multimedia mobile communications using restricted frequency resources.  
           [0006]    Also, it is required to use error correction codes such as turbo codes so as to perform reliable multimedia communications under the poor mobile communication channel environments.  
           [0007]    However, since the turbo codes need soft decision decoding, and the QAM signals carry information through phases and amplitudes, a soft decision decoding algorithm in consideration of channel estimation errors is necessary.  
           [0008]    A conventional log likelihood ratio algorithm for soft decision decoding will now be described in detail.  
           [0009]    A symbol x of the QAM signals in the M-ary QAM has one of M signal symbols {x 1 , x 2 , . . . , x M }, and each symbol x i  is constituted by k bits of {c 1 , c 2 , . . . , c k } assuming that M=2 k , the bit c i  configuring the respective symbols has one of values +1 and −1, and a generation probability of +1 and −1 is respectively 1/2.  
           [0010]    In general, when a QAM transmit signal x is passed through a channel state a and has noise n added thereto, and is received as y at a receiver, the receive signal y is given as Equation 1.  
             y=αx+n   Equation 1  
           [0011]    Since α is a channel gain from Equation 1, α has a constant value for a symbol duration of the QAM signals, and n is AWGN (additive white Gaussian noise).  
           [0012]    When a channel estimator of the receiver estimates the channel, a channel estimation value {circumflex over (α)} is given as Equation 2.  
           {circumflex over (α)}=α+ e   Equation 2  
           [0013]    From Equation 2, e is assumed to have a Gaussian distribution in consideration of channel estimation errors.  
           [0014]    When not considering the channel estimation errors, that is, if e=0 so {circumflex over (α)}=α, a log likelihood ratio for bit decision in this case is given as Equation 3.  
                     γ        (     c   i     )       =              ln          ∑       x   +     ∈     {       x   :     c   i       =     +   1       }              exp        (     -              y   -       a   ^          x   +              2       σ   n   2         )           -                            ln          ∑       x   -     ∈     {       x   :     c   i       =     -   1       }              exp        (     -              y   -       a   ^          x   -              2       σ   n   2         )                ≷     -   1       +   1          1                   Equation                 3                               
 
           [0015]    From Equation 3, a generalized log likelihood ratio algorithm for soft decision decoding with no consideration of the channel estimation errors is given as Equation 4.  
                       γ   ~          (     c   i     )       =                min       x   -     ∈     {       x   :     c   i       =     -   1       }                     y   -       a   ^          x   -              2       -                              min       x   +     ∈     {       x   :     c   i       =     +   1       }                     y   -       a   ^          x   +              2            ≷     -   1       +   1          0                   Equation                 4                               
 
           [0016]    where the reference signal x +  is a symbol x including the case of c i =+1 from among the bits configuring the symbol x, and the reference signal x −  is a symbol x including the case of c i =−1 from among the bits configuring the symbol x.  
           [0017]    [0017]FIG. 1 shows a conventional configuration of a soft decision decoder of QAM signals.  
           [0018]    As shown in FIG. 1, the soft decision decoder comprises log likelihood ratio calculators  10 , a subtractor  20 , and a comparator  30 .  
           [0019]    The log likelihood ratio calculator  10  calculates log likelihood ratios of (−) and (+) signals as given in Equations 3 and 4. The subtractor  20  calculates a difference of the log likelihood ratios calculated by using the (+) and (−) signals. The comparator  30  receives calculates results on the difference of the log likelihood ratios from the subtractor  20 , and determines a soft decision value of the QAM signal as (+) or (−) according to comparison results of a positive number and a negative number of the difference of the log likelihood ratios.  
           [0020]    [0020]FIG. 2 shows a block diagram of a conventional log likelihood ratio calculator for soft decision decoding.  
           [0021]    As shown in FIG. 2, the log likelihood ratio calculator comprises a multiplier  11 , a subtractor  12 , a square calculator  13 , and a comparator  14 .  
           [0022]    The multiplier  11  multiplies a reference signal and a channel state a with no consideration of channel estimation errors, the subtractor  12  subtracts an output signal of the multiplier  11  from a receive signal, and the square calculator  13  squares an output signal of the subtractor  12 . The comparator  14  compares output signals of the square calculator  13 .  
           [0023]    [0023]FIG. 2 is a configuration of the log likelihood ratio calculator corresponding to a first term or a second term of Equation 4.  
           [0024]    Therefore, as shown in FIG. 1, the whole configuration for soft decision decoding includes two log likelihood ratio calculators of FIG. 2, the subtractor  20  performs log subtraction, and the comparator  30  determines a soft decision value of the QAM signal as 1 when a subtraction result by the subtractor  20  is greater than 0, and determines the soft decision value of the QAM signal as −1 when a subtraction result by the subtractor  20  is less than 0.  
           [0025]    However, since the conventional log likelihood ratio calculator for soft decision decoding does not consider channel estimation errors, the conventional log likelihood ratio calculator recovers signals while failing to completely reflect the actual channel estimation errors.  
           [0026]    Therefore, the modulation method for the QAM signals having information be loaded to the amplitude thereof needs decoding in consideration of the channel estimation errors for the optimized signal recovery. However, since the conventional log likelihood ratio calculator does not completely reflect the channel estimation errors and performs decoding, a soft decision decoding performance on the receive QAM signals is lowered.  
         SUMMARY OF THE INVENTION  
         [0027]    It is an advantage of the present invention to provide a log likelihood ratio calculator for soft decision decoding, and a method thereof, for realizing a log likelihood ratio algorithm in consideration of channel estimation errors to perform optimal signal recovery and improve decoding performance of receive signals.  
           [0028]    In one aspect of the present invention, a soft decision decoder comprises:  
           [0029]    a plurality of log likelihood ratio calculators for using a receive signal y with noise input from a receiver so as to perform soft decision decoding on a QAM signal, reflecting of channel estimation errors, and calculating of a log likelihood ratio of a positive number and a negative number;  
           [0030]    a subtractor for determining a difference between the positive signal and the negative signal output by the log likelihood ratio calculators; and  
           [0031]    a comparator for receiving a calculation result on the difference of the log likelihood ratio of the subtractor, and determining the QAM signal to be positive or negative according to a positive/negative state of the calculation result.  
           [0032]    The log likelihood ratio calculator comprises:  
           [0033]    M multipliers for receiving a channel estimate {circumflex over (α)} estimated by the receiver, and receiving M reference signals x i  from a transmitter to respectively multiply them;  
           [0034]    M subtractors for receiving M multiplication values multiplied by the multipliers to subtract them from a receive signal y received from the receiver;  
           [0035]    M first square calculators for respectively squaring M subtraction values subtracted by the subtractors;  
           [0036]    M second square calculators for receiving the reference signals x i  to respectively square them;  
           [0037]    M adders for respectively adding M square values of the reference signals input by the second square calculators and a ratio ρ of a symbol noise bandwidth of a QAM signal and a channel estimation filter noise bandwidth;  
           [0038]    M dividers for dividing M square values input by the first square calculators by the M addition values input by the adders  122 ; and  
           [0039]    a comparator for selecting the minimum value from among the M division values input by the dividers  123 , and outputting a log likelihood ratio.  
           [0040]    In another aspect of the present invention, a method for calculating a log likelihood ratio for soft decision decoding, comprises:  
           [0041]    (a) receiving a channel estimate {circumflex over (α)} estimated by a receiver, receiving M reference signals x i  from a transmitter to respectively multiply them, and receiving multiplication values to subtract them from a receive signal y received from the receiver;  
           [0042]    (b) respectively squaring subtraction values and the reference signals x i  in (a);  
           [0043]    (c) respectively adding square values of the reference signals input in (b) and a ratio ρ of a symbol noise bandwidth of a QAM signal and a channel estimation filter noise bandwidth;  
           [0044]    (d) dividing square values of the subtraction values input in (b) by the addition values added in (c); and  
           [0045]    (e) selecting the minimum value from among the values input in (d), and outputting a log likelihood ratio for soft decision decoding in consideration of channel estimation errors.  
           [0046]    Outputting a log likelihood ratio in (e) follows Equation 15.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0047]    The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, serve to explain the principles of the invention:  
         [0048]    [0048]FIG. 1 shows a general soft decision decoder for QAM signals;  
         [0049]    [0049]FIG. 2 shows a block diagram of a conventional log likelihood ratio calculator for soft decision decoding;  
         [0050]    [0050]FIG. 3 shows a log likelihood ratio calculator for soft decision decoding according to a preferred embodiment of the present invention;  
         [0051]    [0051]FIG. 4 shows a graph for a bit error probability performance for a 64-QAM signal decoded by the log likelihood ratio calculator realized in FIG. 2; and  
         [0052]    [0052]FIG. 5 shows a graph for a bit error probability performance for a 64-QAM signal decoded by the log likelihood ratio calculator for soft decision decoding according to a preferred embodiment of the present invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0053]    In the following detailed description, only the preferred embodiment of the invention has been shown and described, simply by way of illustration of the best mode contemplated by the inventor(s) of carrying out the invention. As will be realized, the invention is capable of modification in various obvious respects, all without departing from the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not restrictive.  
         [0054]    [0054]FIG. 3 shows a log likelihood ratio calculator for soft decision decoding according to a preferred embodiment of the present invention.  
         [0055]    As shown in FIG. 3, the log likelihood ratio calculator  100  comprises M multipliers  111 , M subtractors  112 , first and second square calculators  113  and  121 , M adders  122 , M dividers  123 , and a comparator  114 .  
         [0056]    The M multipliers  111  receive a channel estimation value {circumflex over (α)} estimated by a receiver, and receive M reference signals x i  from a transmitter to respectively multiply them. The M subtractors  112  receive M multiplication values multiplied by the multipliers  111  to subtract them from a receive signal y received from the receiver.  
         [0057]    The M first square calculators  113  respectively square M subtraction values subtracted by the subtractors  112 , and M second square calculators  121  receive the reference signals x i  to square them.  
         [0058]    The M adders  122  respectively add M square values of the reference signals input by the second square calculators  121  and a ratio ρ of a symbol noise bandwidth of a QAM signal and a channel estimation filter noise bandwidth.  
         [0059]    The M dividers  123  divide M square values input by the first square calculators  113  by the M addition values input by the adders  122 . The comparator  114  selects the minimum value from among the M division values input by the dividers  123 , and outputs a log likelihood ratio for soft decision decoding in consideration of channel estimation errors.  
         [0060]    As to the soft decision decoder having the above-configured two log likelihood ratio calculators, the subtractor determines the difference of the log likelihood ratio of the positive (+) signal and the negative (−) signal output from the respective log likelihood ratio calculators  100  referring to FIG. 1, and the comparator  114  receives calculation results on the log likelihood ratio difference of the subtractors  112  to determine the QAM signals to be positive (+) or negative (−) according to positive and negative number states of the calculation results.  
         [0061]    The soft decision decoder receives the receive signal y with noise from the receiver, receives a channel estimation value {circumflex over (α)} estimated by the channel estimator of the receiver, receives the reference signals x i , receives the ratio ρ of the symbol noise bandwidth and the channel estimation filter noise bandwidth of the QAM signal, and outputs performance-improved soft decision decoding signals.  
         [0062]    The above-configured soft decision decoder and the log likelihood ratio calculator for soft decision decoding will be described using equations.  
         [0063]    When the channel estimation errors are provided in Equation 2, an evaluation thereof is required. Since the AWGN n and the channel estimation errors e are independent of each other in Equations 1 and 2, and they are to be considered with respect to the I and Q channels, respectively, the AWGN n and the channel estimation errors e respectively have 2-dimensional distributions.  
         [0064]    Therefore, the receive signal y and the channel estimation value {circumflex over (α)} have a joint PDF (probability density function) as given in Equation 5.  
                     p        (     y   ,       a   ^        x       )       =       1     πσ   n   2            exp        (     -              y   -   ax          2       σ   n   2         )            1     πσ   e   2            exp        (     -                a   ^     -   a          2       σ   e   2         )                     =       1       π   2          σ   n   2          σ   e   2              exp        [     -     (                y   -   ax          2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]                       Equation                 5                               
 
         [0065]    A log likelihood ratio on the i-th bit of the M-ary QAM signal symbol is found using Equation 5, as given in Equation 6.  
                     γ        (     c   i     )       =     ln          p        (     y   ,         a   ^     |     c   i       =     +   1         )         p        (     y   ,         a   ^     |     c   i       =     -   1         )                       =     ln              ∑               c   1                     ⋯                  ∑       c     i   -   1                           ∑               c     i   +   1            ⋯                  ∑       c   k            p        (     y   ,       a   ^     |     c   1       ,   ⋯              ,       c   i     =     +   1       ,   ⋯              ,     c   k       )               ∑               c   1          ⋯                     ∑               c     i   -   1                ∑               c     i   +   1                       ⋯                     ∑     c   k            p        (     y   ,       a   ^     |     c   1       ,   ⋯              ,       c   i     =     -   1       ,   ⋯              ,     c   k       )                           =       ln            ∑       x   +     ∈     {       x   :     c   i       =     +   1       }              p        (     y   ,       a   ^     |     x   +         )             ∑       x   -     ∈     {       x   :     c   i       =     -   1       }              p        (     y   ,       a   ^     |     x   -         )             ≷   1                   Equation                 6                               
 
         [0066]    Recalculation of Equation 6 by using Equation 5 gives rise to Equation 7.  
               γ        (     c   i     )       =       ln            ∑       x   +     ∈     {       x   :     c   i       =     +   1       }              exp        [     -     (                y   -     ax   +            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]             ∑       x   -     ∈     {       x   :     c   i       =     -   1       }              exp        [     -     (                y   -     ax   -            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]             ≷   1             Equation                 7                               
 
         [0067]    It is needed to introduce a generalized likelihood ratio concept so as to find a threshold value from Equation 7.  
         [0068]    The generalized likelihood ratio is found from Equation 8 maximized in the channel state of α.  
                 γ   ~          (     c   i     )       =       ln            max   a          {       ∑       x   +     ∈     {       x   :     c   i       =     +   1       }              exp        [     -     (                y   -     ax   +            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]         }           max   a          {       ∑       x   -     ∈     {       x   :     c   i       =     -   1       }              exp        [     -     (                y   -     ax   -            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]         }           ≷   1             Equation                 8                               
 
         [0069]    where since the exponential function is an increasing function, the largest exponent in Equation 8 becomes dominant, and Equation 8 can be rewritten as Equation 9.  
                   max   a          {       ∑       x   ±     ∈     {       x   :     c   i       =     ±   1       }              exp        [     -     (                y   -     ax   ±            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]         }            
                ≈       max   a          {     exp        [     -     (                y   -     a          x   ~          (   a   )                2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]       }              
                =         max   a          {       max       x   ±     ∈     {       x   :     c   i       =     ±   1       }              {     exp        [     -     (                y   -     ax   ±            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]       }       }            
                =       max       x   ±     ∈     {       x   :     c   i       =     ±   1       }              {         max                a          {     exp        [     -     (                y   -     ax   ±            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]       }       }                 Equation                 9                               
 
         [0070]    where  
           x   ~          (   a   )       =         arg                 max         x   ±     ∈     {       x   :     c   i       =     ±   1       }              {     exp        [     -     (                y   -     ax   ±            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]       }                             
 
         [0071]    and {tilde over (x)}(α) is a symbol corresponding to the dominant term in Equation 9.  
         [0072]    To find  
         max   a          {     exp        [     -     (                y   -   ax          2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]       }                           
 
         [0073]    from Equation 9 is to maximize Equation 10.  
                 H     y   ,     a   ^              (   a   )       =     -     (                y   -   ax          2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )               Equation                 10                               
 
         [0074]    The α for maximizing Equation 10 is found from Equation 11 by partially differentiating Equation 10 with respect to α.  
                 ∂       H     y   ,     a   ^              (   a   )           ∂   a       =       -     (           (       a   ^     -   a     )     *     (     -   1     )         σ   e   2       +         (     y   -     a                 x       )     *     (     -   x     )         σ   n   2         )       =   0             Equation  11                               
 
         [0075]    Equation 11 results in Equation 12.  
               a   ML     =         x   *   y                   σ   e   2       +       a   ^          σ   n   2                    x        2          σ   e   2       +     σ   n   2                 Equation  12                               
 
                 max   a          {     exp              [     -     (                y   -     a                 x            2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]     }       =     exp        (     -              y   -       a   ^                   x            2                x        2          σ   e   2       +     σ   n   2           )               Equation  13                               
 
         [0076]    Equation 13 is found from Equation 12, and recalculation of Equation 9 by using Equation 13 becomes Equation 14.  
                 max   a          {       max       x   ±     ∈     {       x   :                c   i       =     ±   1       }              {     exp        [     -     (                y   -     a                   x   ±              2       σ   n   2       +                a   ^     -   a          2       σ   e   2         )       ]       }       }       =       max       x   ±     ∈     {       x   :                c   i       =     ±   1       }              {     exp        [     -              y   -       a   ^                     x   ±              2                  x   ±          2          σ   e   2       +     σ   n   2           ]       }               Equation  14                               
 
         [0077]    Finally, Equation 8 becomes Equation 15 as a generalized log likelihood ratio for soft decision decoding in consideration of the channel estimation errors by using Equation 14.  
                         γ   ~          (     c   i     )       ≈            ln            max       x   +     ∈     {       x   :                c   i       =     +   1       }              {     exp        (     -              y   -       a   ^                     x   +              2                  x   +          2          σ   e   2       +     σ   n   2           )       }           max       x   -     ∈     {       x   :                c   i       =     -   1       }              {     exp        (     -              y   -       a   ^                     x   -              2                  x   -          2          σ   e   2       +     σ   n   2           )       }           ≷   1                            =                max       x   +     ∈     {       x   :                c   i       =     +   1       }              {     -              y   -       a   ^                     x   +              2         (              x   +          2     +   ρ     )          σ   e   2           }       -                              max       x   -     ∈     {       x   :                c   i       =     -   1       }              {     -              y   -       a   ^                     x   -              2         (              x   -          2     +   ρ     )          σ   e   2           }       ≷   0                 =                min       x   -     ∈     {       x   :                c   i       =     -   1       }              {              y   -       a   ^                     x   -              2                x   -          2     +   ρ       }       -                              min       x   +     ∈     {       x   :                c   i       =     +   1       }              {              y   -       a   ^                     x   +              2                x   +          2     +   ρ       }              ≷     +   1         -   1          0                   Equation  15                               
 
         [0078]    where  
         ρ   =       σ   n   2       σ   e   2         ,                         
 
         [0079]    which can be found from Equation 16.  
             ρ   =         σ   n   2       σ   e   2       =       BW   n       BW   e                 Equation  16                               
 
         [0080]    where BW n  is a QAM signal symbol noise bandwidth, and BW e  is a channel estimation filter noise bandwidth.  
         [0081]    As to the final log likelihood ratio given as Equation 15, the second square calculators  121  square the reference signal x i , and the adders  122  add the square values of the second square calculators  121  and the ratio ρ of the QAM signal symbol noise bandwidth and the channel estimation filter noise bandwidth.  
         [0082]    After this, the dividers  123  divide the signals output by the first square calculators  113  by the signals output by the adders  122 , and the comparator  114  compares output signals of the dividers  123  to output a log likelihood ratio in consideration of the channel estimation errors through the optimized signal recovery.  
         [0083]    As a result, the log likelihood ratio calculator shown in FIG. 3 realizes the log likelihood ratio for soft decision decoding of the QAM signal given as Equation 15.  
         [0084]    In this instance, the reference signals x +  are symbols x including the case of c i =+1 from among the bits configuring the symbols x, and the reference signals x −  are symbols x including the case of c i =−1 from among the bits configuring the symbols x.  
         [0085]    [0085]FIG. 4 shows simulation results for a decoding bit error probability performance for a turbo-coded 64-QAM signal by the conventional log likelihood ratio calculator realized in FIG. 2, and FIG. 5 shows simulation results for a decoding bit error probability performance for a turbo-coded 64-QAM signal by the log likelihood ratio calculator for soft decision decoding according to a preferred embodiment of the present invention.  
         [0086]    Assuming that ρ=10 and 13 dB, and comparing the graphs of FIGS. 4 and 5, the log likelihood ratio calculator shown in FIG. 5 has a signal power gain substantially greater than 1 dB so as to obtain the same bit error probability performance (e.g., 10 −5 ) as that of the conventional log likelihood ratio calculator, thereby improving the soft decision decoding performance.  
         [0087]    The improvement of the soft decision decoding performance is generated by the channel estimation error considered terms in the denominator of Equation 15 when comparing the Equations 4 and 15.  
         [0088]    Therefore, decoding is executed without completely reflecting the actual channel estimation errors because the channel estimation errors are not considered in Equation 4, but the decoding with completely reflected channel estimation errors is executed in Equation 15 to thereby enable performance improvements for signal recovery and improve the SNR (signal to noise ratio).  
         [0089]    As described, since the soft decision decoder and the log likelihood ratio calculator and method for soft decision decoding use a generalized log likelihood ratio to perform soft decision signal decoding with reflection of the channel estimation errors, decoding performance on the receive signals are improved through the optimized signal recovery, and the SNR is improved.  
         [0090]    While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.