Abstract:
A method and apparatus for reducing the complexity of waveform correlation computations used by a multicode receiver is described herein. One exemplary multicode receiver includes a despreading unit, channel estimator, and waveform correlation calculator. The despreading unit despreads a received multicode signal to generate despread symbols. The channel estimator estimates channel coefficients associated with the despread symbols. The waveform correlation calculator determines waveform correlations between the transmitted symbols in successive processing windows that span two or more symbol periods and that overlap in time. To reduce the computational complexity associated with computing waveform correlations, the calculator may reuse channel coefficients and/or net channel correlations for multiple symbol periods and/or processing windows. The calculator may also reduce complexity by reusing one or more waveform correlations from a previous processing window as waveform correlations for one or more subsequent processing windows and/or by exploiting the Hermitian symmetry of the waveform correlation matrix.

Description:
BACKGROUND 
     Interference due to channel dispersion presents one challenge to obtaining high data transmission rates in Code Division Multiple Access (CDMA) systems, such as Wideband CDMA and IS-2000. Performance in CDMA systems is sensitive to multi-path dispersion when a low spreading factor and/or multicode is used to transmit data. With dispersion, multiple echoes of the transmitted signal arrive at the receiver with different relative delays. These echoes interfere with one another. The interference results in a loss of orthogonality between successive symbols and between symbols sent on different, orthogonal codes. 
     Generalized RAKE (GRAKE) receivers provide one means for suppressing interference. Interference suppression is achieved by treating Intersymbol Interference (ISI) and Multiple Access Interference (MAI) as colored Gaussian noise. The noise correlation across fingers is exploited by adapting the finger delays and combining weights. In this way, the orthogonality between user signals may be partially restored. Recently, further improvements in GRAKE receivers have been proposed for the High Speed Downlink Packet Access (HSDPA) mode of WCDMA that take into account waveform correlations. 
     Multicode detection techniques that rely on waveform correlations provide another technique for suppressing MAI and ISI. Exemplary multicode detectors include a Maximum Likelihood Sequence Estimation (MLSE) detector, a minimum mean squared error (MMSE) detector, and a decorrelating detector. Because the computational complexity of conventional multicode detectors grows exponentially with the number of codes, there remains an interest in reducing the computational complexity associated with multicode detection. 
     SUMMARY 
     The present invention comprises an apparatus for reducing the complexity of waveform correlation computations used by a multicode receiver to process received multicode signals containing a plurality of symbols transmitted over two or more codes. In one exemplary embodiment, the multicode receiver includes a despreading unit, a channel estimator, and a waveform correlation calculator. The despreading unit despreads the received multicode signal to generate despread symbols. The channel estimator estimates channel coefficients associated with the despread symbols in successive symbol periods. The waveform correlation calculator determines waveform correlations between transmitted symbols corresponding to the despread symbols in successive processing windows that span two or more symbol periods and that overlap in time. 
     In one embodiment, the waveform correlation calculator reduces the computational complexity associated with computing the waveform correlations by reusing channel coefficients used to determine waveform correlations in one processing window to determine the waveform correlations in one or more subsequent processing windows. In another embodiment, the waveform correlation calculator reduces the computational complexity by computing net channel correlations based on the channel coefficients, and reusing the computed net channel correlations over multiple processing windows and/or symbol periods to determine the corresponding waveform correlations. In another embodiment, the waveform correlation calculator reduces the computational complexity by reusing one or more waveform correlations computed for a previous processing window as waveform correlations for one or more subsequent processing windows. Computational complexity may be further reduced by exploiting the Hermitian symmetry of the waveform correlation matrix. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an exemplary receiver in a mobile communication system. 
         FIG. 2  shows multiple symbols received over multiple codes during multiple symbol periods relative to a sliding processing window. 
         FIG. 3  shows multiple waveform correlation matrices computed for multiple symbol periods relative to a sliding processing window. 
         FIG. 4  shows one exemplary process for computing waveform correlations. 
         FIG. 5  shows an exemplary multicode detector for jointly detecting symbols transmitted over multiple code channels. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention provides multiple techniques for reducing the computational complexity associated with waveform correlations used in multicode detection.  FIG. 1  illustrates a block diagram of one exemplary multicode receiver  10  that jointly detects signals transmitted on different spreading codes and received as a composite received signal r(t). The receiver  10  may be deployed in any wireless network element, e.g., a base station, mobile terminal, etc. Receiver  10  comprises a RAKE section  12 , a processor  14 , and a multicode detector  16 . RAKE section  12  despreads the composite received signal r(t) and generates a vector of RAKE combined values corresponding to each spreading code based on combining weights and path delays provided by processor  14 . Multicode detector  16  jointly processes the RAKE combined values to generate estimates of the transmitted symbols based on channel coefficients and path delays provided by processor  14 . 
     RAKE section  12  comprises a plurality of RAKE and/or GRAKE receivers  18 . Each RAKE receiver  18  includes a plurality of RAKE fingers  20  and a RAKE combiner  21 . Each RAKE finger  20  comprises a delay element  22  and a despreader or correlator  24  for processing different time shifts or multi-path echoes of the received signal r(t). Delay elements  22  delay the received signal r(t) responsive to a path delay τ selected by processor  14  to time align the multi-path echoes processed by each RAKE finger  20 . Correlators  24  correlate the delayed signals with a spreading code to extract the assigned multi-path echoes from the received signal r(t). 
     RAKE combiner  21  combines the despread values from correlators  24  to generate a RAKE combined value, also referred to as a despread symbol, for each code-multiplexed symbol during each symbol period. Combiner  21  includes weighting elements  26  and summer  28 . Weighting elements  26  weight the despread values output from respective correlators  24  responsive to weighting factors computed by processor  14 . Summer  28  combines the weighted despread values symbol-by-symbol to form the RAKE combined values. Those skilled in the art will appreciate that the combining weights associated with weighting elements  26  may comprise RAKE combining weights that correspond to the channel coefficients, or GRAKE combining weights that correspond to the channel coefficients and a noise correlation matrix. Each RAKE combined value represents a symbol of interest or an interfering symbol. It should be noted that the symbols of interest also interfere with each another. Therefore, when considering a given symbol of interest the other symbols of interest represent interfering symbols. 
     The vector of RAKE combined values, denoted z, output by RAKE section  12  may be expressed as:
 
 z=RAs+n,   (1)
 
where s=(s 0 , . . . , S K−1 ) T  represents a vector of symbols to be considered for joint detection, A=diag (A 0 , . . . , A K−1 ) represents a diagonal matrix with the k th  element corresponding to the received amplitude for s k , R represents a waveform correlation matrix, and n represents a vector of the noise. The vector z includes both symbols of interest and interfering symbols. The elements of R represent the correlations of the effective spreading waveforms of the symbols in s with each other and with themselves. The elements of R may be given by:
 
 R ( u,v )=∫ −∞   ∞   f   u   H ( t ) f   v ( t ) dt,   (2)
 
where f u (t)=[f u,0 (t),f u,1 (t), . . . , f u,Q−1 (t)] T  represents the effective waveform for symbol s u , with each element of the effective waveform corresponding to the q th  receive antenna. The number of receive antennas equals Q. The effective spreading waveform considered is a combination of the transmit waveform and the radio channel impulse response. It can be demonstrated that σ 2 R represents the covariance of the noise vector n, where σ 2  represents the noise variance at the input to RAKE section  12 .
 
     Multiple access interference (MAI) and intersymbol interference (ISI) due to channel dispersion corrupts the RAKE combined values output by the RAKE section  12 . In the case of MAI, the symbols transmitted on different codes interfere with one another. In the case of ISI, channel dispersion causes symbols transmitted on the same code to interfere with one another. It should be noted that multi-path propagation may cause a transmitted symbol to interfere with itself. Multicode detector  16  employs multicode detection techniques to suppress MAI and ISI due to channel dispersion and to generate estimates of the transmitted symbols. The term multicode detector is used rather than multi-user detector because the transmitted symbols may belong to the same user or to different users. 
     Multicode detector  16  computes the symbol estimates according to:
 
 ŝ=Mz.   (3)
 
In one embodiment, multicode detector  16  comprises a decorrelating detector, which computes the symbol estimates by setting M=R −1  so that:
 
 ŝ=R   −1   z.   (4)
 
In another embodiment, detector  16  comprises a Linear Minimum Mean Square Error (LMMSE) detector, which computes the symbol estimates by computing M according to:
 
 M=A   −1   [R+σ   2   A   −2 ] −1   (5)
 
where σ 2  is the noise variance at the input of the RAKE section  12 . It will be appreciated that other multicode detectors may be used, such as a nonlinear Maximum Likelihood Sequence Estimator (MLSE) or other linear multicode detectors, or the multicode detectors discussed in U.S. application Ser. No. 11/739,126 entitled “Robust Multicode Detector for HSDPA” and filed 24 Apr. 2007, which is herein incorporated by reference. For the MLSE detector, s-parameters, which are similar to waveform correlations, need to be computed.
 
     Multicode detector  16  employs a sliding window approach in which combined values received over multiple codes during multiple symbol periods are processed to produce symbol estimates for a current symbol period.  FIG. 2  illustrates a sliding processing window  50  applied to symbols received over three codes during four symbol periods. During each symbol period, the symbols in the processing window  50  are selected and stacked to form the RAKE combined values z used to compute ŝ. In this example, the processing window  50  spans three symbol periods for three codes, is centered on a current symbol period, and extends forward and backward in time one symbol period. During each symbol period, the multicode detector  16  estimates the symbols at the center of the processing window  50  based on the waveform correlation matrix R computed for the symbols in processing window  50 . It will be appreciated that any size processing window may be used. 
     Due to the large number of codes and RAKE fingers  20 , calculating the waveform correlations for R is computationally complex. The present invention reduces the complexity associated with the waveform correlation calculations. One exemplary embodiment reuses channel coefficients for multiple symbol periods to reduce the computational complexity associated with waveform correlations. Elements of the waveform correlation matrix R may be computed according to: 
                     R   ⁡     (     u   ,   v     )       =       ∑     q   =   0       Q   -   1       ⁢       ∑       l   1     =   0       L     (     i   ⁡     (   u   )       )         ⁢       ∑       l   2     =   0       L     (     i   ⁡     (   v   )       )         ⁢       ∑       m   0     =   0         N     k   ⁡     (   u   )         (     i   ⁡     (   u   )       )       -   1       ⁢       ∑       m   1     =   0         N     k   ⁡     (   v   )         (     i   ⁡     (   v   )       )       -   1       ⁢     (             g   q     (     i   ⁡     (   u   )       )       ⁡     (     l   1     )       *     ⁢       g   q     (     i   ⁡     (   v   )       )       ⁡     (     l   2     )       ⁢       (       c       k   ⁡     (   u   )       ,     j   ⁡     (   u   )           (     i   ⁡     (   u   )       )       ⁡     (     m   0     )       )     *     ⁢       c       k   ⁡     (   v   )       ,     j   ⁡     (   v   )           (     i   ⁡     (   v   )       )       ⁡     (     m   1     )       ×       R   p     ⁡     (         (         j   ⁡     (   u   )       ⁢     N     k   ⁡     (   u   )         (     i   ⁡     (   u   )       )         -       j   ⁡     (   v   )       ⁢     N     k   ⁡     (   v   )         (     i   ⁡     (   v   )       )         +     m   0     -     m   1       )     ⁢     T   c       +       τ     (     i   ⁡     (   u   )       )       ⁡     (     l   1     )       -       τ     (     i   ⁡     (   v   )       )       ⁡     (     l   2     )       +     o     (     i   ⁡     (   u   )       )       -     o     (     i   ⁡     (   v   )       )         )         ,                         (   6   )               
where the terms in Equation (6) have the following definitions. The index q indicates the receive antenna and ranges from 0 to Q−1. Indices m 0  and m 1  represent chip indices. Indices l 1  and l 2  represent path delays and L i  represents the number of paths for each transmitter. The term g q   (i) (l) represents the complex channel coefficient for the l th  path for transmitter i and antenna q, and τ (i) (l) represents the path delay for the l th  path. The term o (i)  represents the transmit time offset for transmitter i, and N k   (i)  represents the spreading factor for code k and transmitter i. T c  represents the chip period and c k,j   (i)  is the spreading sequence (scrambled Walsh code) for code k and transmitter i during the j th  symbol period. R p (λ) represents the value of the chip pulse autocorrelation function for argument λ. The transmitter index i(u)=└mod(u,KM)/K┘. The code index k(u)=mod(u/K). The symbol period index j(u)=¥u/(KM)┘. M represents the number of transmitters.
 
     As shown in Equation (6), the waveform correlations R(u,v) depend on the channel coefficients g. Because channel coefficients may change slowly, the computational complexity associated with the waveform correlations may be reduced by reusing channel coefficients for multiple symbol periods. To illustrate, consider the example shown in  FIG. 2 , where symbols s 0 ,s 1 , . . . s 5  share channel coefficients g, while symbols s 6 ,s 7 , . . . s 11 , share channel coefficients g′. For processing window A, R(u,v) where (u,v)ε{0, 1, . . . , 5} is calculated based on g. However, R(u,v) where (u,v)ε{6,7,8} is calculated based on g′, and R(u,v) where uε{0, 1, . . . , 5} and vε{6,7,8} is calculated based on g and g′. As a result, computing the waveform correlations for processing window A using Equation (6) generally requires computing different channel coefficient products based on g and g′(g*g, g*g′, (g′)*g′). By assuming that g≈g′, g may be reused for symbols s 6 ,s 7 ,s 8 . This enables g to be used to compute all of the waveform correlations for processing window A. Thus, it will be appreciated that reusing channel coefficients over multiple symbol periods reduces the complexity of computing waveform correlations for each processing window  50 . Further, it will be appreciated that reusing the channel coefficients for multiple processing windows  50  also reduces the computational complexity associated with waveform correlations. 
     Alternatively or in addition, the computational complexity of the waveform correlation computations may be reduced by computing net channel correlations based on the channel coefficients, and reusing the computed net channel correlations to compute the waveform correlations for multiple symbol periods and/or multiple processing windows  50 . Here, the “net” channel represents the transmit chip pulse shape convolved with the medium response. Equation (6) may be rewritten as: 
                     R   ⁡     (     u   ,   v     )       =         ∑       m   0     =   0         N     k   ⁡     (   u   )         (     i   ⁡     (   u   )       )       -   1       ⁢       ∑       m   1     =   0         N     k   ⁡     (   v   )         (     i   ⁡     (   v   )       )       -   1       ⁢         (       c       k   ⁡     (   u   )       ,     j   ⁡     (   u   )           (     i   ⁡     (   u   )       )       ⁡     (     m   0     )       )     *     ⁢       c       k   ⁡     (   v   )       ,     j   ⁡     (   v   )           (     i   ⁡     (   v   )       )       ⁡     (     m   1     )       ⁢       ∑     q   =   0       Q   -   1       ⁢       ∑       l   1     =   0       L     (     i   ⁡     (   u   )       )         ⁢       ∑       l   2     =   0       L     (     i   ⁡     (   v   )       )         ⁢         (       g   q     (     i   ⁡     (   u   )       )       ⁡     (     l   1     )       )     *     ⁢       g   q     (     i   ⁡     (   v   )       )       ⁡     (     l   2     )       ×       R   p     ⁡     (         (         j   ⁡     (   u   )       ⁢     N     k   ⁡     (   u   )         (     i   ⁡     (   u   )       )         -       j   ⁡     (   v   )       ⁢     N     k   ⁡     (   v   )         (     i   ⁡     (   v   )       )         +     m   0     -     m   1       )     ⁢     T   c       +       τ     (     i   ⁡     (   u   )       )       ⁡     (     l   1     )       -       τ     (     i   ⁡     (   v   )       )       ⁡     (     l   2     )       +     o     (     i   ⁡     (   u   )       )       -     o     (     i   ⁡     (   v   )       )         )                     =       ∑       m   0     =   0         N     k   ⁡     (   u   )         (     i   ⁡     (   u   )       )       -   1       ⁢       ∑       m   1     =   0         N     k   ⁡     (   v   )         (     i   ⁡     (   v   )       )       -   1       ⁢         (       c       k   ⁡     (   u   )       ,     j   ⁡     (   u   )           (     i   ⁡     (   u   )       )       ⁡     (     m   0     )       )     *     ⁢       c       k   ⁡     (   v   )       ,     j   ⁡     (   v   )           (     i   ⁡     (   v   )       )       ⁡     (     m   1     )       ⁢       ξ   ⁡     (         (         j   ⁡     (   u   )       ⁢     N     k   ⁡     (   u   )         (     i   ⁡     (   u   )       )         -       j   ⁡     (     n   1     )       ⁢     N     k   ⁡     (   v   )         (     i   ⁡     (   v   )       )         +     m   0     -     m   1       )     ⁢     T   c       ,     i   ⁡     (   u   )       ,     i   ⁡     (   v   )         )       .                     (   7   )               
Equation (7) shows that a waveform correlation R(u,v) is computed based the product of a spreading sequence correlation (c(m 0 )*c(m 1 )) and a net channel correlation (ζ(t,i 1 ,i 2 )), wherein the net channel correlations ζ(t,i 1 ,i 2 ) may be calculated according to:
 
                     ξ   ⁡     (     t   ,     i   1     ,     i   2       )       =       ∑     q   =   0       Q   -   1       ⁢       ∑       l   1     =   0       L     (     i   1     )         ⁢       ∑       l   2     =   0       L     (     i   2     )         ⁢         (       g   q     (     i   1     )       ⁡     (     l   1     )       )     *     ⁢       g   q     (     i   2     )       ⁡     (     l   2     )       ⁢         R   p     ⁡     (     t   +       τ     (     i   1     )       ⁡     (     l   1     )       -       τ     (     i   2     )       ⁡     (     l   2     )       +     o     (     i   1     )       -     o     (     i   2     )         )       .                     (   8   )               
When i 1 =i 2 , ζ(t,i 1 ,i 2 ) represents the net channel autocorrelation; when i 1 ≠i 2 , ζ(t,i 1 ,i 2 ) represents the net channel cross-correlation.
 
     As shown by Equation (8), the net channel correlations ζ(t,i 1 ,i 2 ) depend on the channel coefficients g and the chip pulse autocorrelation function R p , and do not depend on symbol-dependent spreading codes c. When the channel coefficients for multiple symbol periods and/or multiple codes are assumed to be constant, the net channel correlations ζ(t,i 1 ,i 2 ) will also be constant. Thus, in one embodiment, the net channel correlations ζ(t,i 1 ,i 2 ) used to compute R for the symbols in one processing window  50  may be reused to compute R for the symbols in one or more subsequent processing windows  50 . Alternatively or in addition, the net channel correlations ζ(t,i 1 ,i 2 ) used to compute elements of R for one pair of symbols in a symbol period may be reused to compute elements of R for another pair of symbols in the same symbol period. Further, the net channel correlations ζ(t,i 1 ,i 2 ) used to compute elements of R for one symbol period of a processing window  50  may be reused to compute elements of R for one or more subsequent symbol periods of the sliding processing window  50 . When the net channel correlations ζ(t,i 1 ,i 2 ) are reused for one or more symbol periods and/or one or more processing windows  50 , the process for computing the elements of R only requires the repeated calculation of the spreading sequence correlations (c(m 0 )*c(m 1 )) and their convolution with the reused net channel correlations according to Equation (7). Thus, reusing the net channel correlations ζ(t,i 1 ,i 2 ) over multiple symbol periods and/or multiple processing windows  50  reduces the computational complexity associated with the waveform correlation computations. 
     Eventually, the channel coefficient estimates need to change to track the time-varying channel. Referring to  FIG. 2 , consider the case where g′ is much different than g. For this case, assuming the channel does not vary too quickly, the channel coefficients may be assumed to be constant within a processing window  50 . Thus, g is used when computing waveform correlations for s 3  and s 6  in sliding window A, and g′ is used when computing waveform correlations for s 3  and s 6  in sliding window B. As a result, the waveform correlations may periodically be computed twice to handle transitions in the channel estimates. 
     In another embodiment, one or more previously computed waveform correlations may be reused for multiple processing windows  50  to reduce the computational complexity associated with waveform correlations. To illustrate, consider  FIG. 3 , which shows the waveform correlation matrices R A  and R B  for the processing windows A and B, respectively. The waveform correlation matrices R A  and R B  include the same values when (u,v)ε{3, 4, . . . , 8}.  FIG. 3  illustrates this by showing the waveform correlations computed for processing window A that may be reused for processing window B. Further, if processing window B is advanced one symbol period, the waveform correlations for (u,v)ε{6, 7, . . . , 11} computed for processing windows A and B are the same for the new processing window  50 . Thus, as processing window  50  advances, a sliding effect may be applied to the waveform correlation matrix R that enables multiple waveform correlations to be reused for one or more subsequent processing windows  50 . For a processing window  50  covering three codes and three symbol periods, such reuse decreases the computational complexity by more than 40%. Thus, reusing previously computed waveform correlations provides significant computational complexity savings. 
     In another embodiment, the computational complexity may be reduced by recognizing that R(u,v)=R*(v,u), and therefore, that R is Hermitian symmetrical. Thus, the computational complexity may be reduced by computing the waveform correlations for the upper or lower triangle of R, and using the Hermitian symmetry relationship to provide the remaining waveform correlations. 
     It will be appreciated that while the above describes four complexity reduction techniques, the present invention may combine one or more of the above-described complexity reduction techniques to reduce the complexity associated with computing waveform correlations. 
       FIG. 4  shows one exemplary process  100  for computing the waveform correlation matrix R. Processor  14  provides computed channel estimates g to the multicode detector  16  (block  110 ). Multicode detector  16  computes elements of the waveform correlation matrix R (block  130 ) based on net channel correlations ζ computed based on the channel coefficients g (block  120 ) or based directly on the channel coefficients g. It will be appreciated that the channel estimates g and/or the net channel correlations ζ may be reused for multiple symbol periods within the processing window  50 . After generating the estimates of the transmitted symbols based on R (block  140 ), the multicode detector  16  advances the sliding processing window  50  one symbol period (block  150 ). For the new processing window of RAKE combined values, the multicode detector  16  reuses the previously computed net channel correlations ζ (path  170 ) or computes a new net channel correlation based on g (path  160 ) to determine the elements of R for the new processing window. It will be appreciated that the multicode detector  16  may reuse one or more previously computed waveform correlations R(u,v) and/or exploit the Hermitian symmetry of R to reduce the number of waveform correlations R(u,v) computed for the waveform correlation matrix R in block  130 , and therefore, to reduce the computational complexity associated with R. 
       FIG. 5  illustrates an exemplary multicode detector  16  that computes and utilizes the waveform correlations to generate symbol estimates for demodulation, as described above. Multicode detector  16  comprises a waveform correlation estimator  30 , sliding window selector  32 , symbol estimator  34 , and symbol extractor  36 . In one embodiment, the waveform correlation estimator  30  estimates the effective spreading waveform correlations for the symbols in the sliding processing window  50  and generates the waveform correlation matrix R based on the channel coefficients g and path delays τ provided by processor  14 , as discussed above. In another embodiment detector  16  includes a net channel correlation estimator  38  that estimates the net channel correlations based on the channel coefficients g and path delays τ provided by processor  14 . For this embodiment, the waveform correlation estimator  30  generates the waveform correlation matrix R based on the net channel correlations provided by the net channel correlation estimator  38 , as discussed above. Symbol estimator  34  estimates the symbols, for example according to Equations (3)-(5), in the processing window  50  based on the waveform correlation matrix R provided by estimator  30 . Symbol extractor  36  extracts symbols corresponding to a current or middle symbol period(s) and outputs the extracted symbol estimates for demodulation. For example, when a processing window  50  spans five symbol periods, the symbol extractor  36  may extract symbol estimates for the middle three symbol periods. 
     The present invention may, of course, be carried out in other ways than those specifically set forth herein without departing from essential characteristics of the invention. The present embodiments are to be considered in all respects as illustrative and not restrictive, and all changes coming within the meaning and equivalency range of the appended claims are intended to be embraced therein.