Abstract:
In certain embodiments, an equalization device for a wireless communication receiver includes multiple adjusting means coupled between corresponding multiple weighting means and a combining means. The multiple weighting means performs a weight process on a received multipath signal to obtain multiple weight results, respectively, according to a plurality of adjustable weight coefficients. The multipath signal includes a known training sequence and user data that may both experience channel fading. The adjusting means compares the weight results of the known training sequence with respective multiple known reference signals, and respectively adjusts the weight coefficient of each weighting means by utilizing the comparison results so that the weighting means weight the user data by utilizing the adjusted weight coefficient. The combining means combines the weight results of the user data to obtain a combined equalization signal. Certain embodiments of the equalization device may be implemented on a mobile terminal to mitigate the effects of wireless channel fading.

Description:
FIELD OF THE INVENTION 
     The present invention relates to a wireless communication system, and more particularly, to an adaptive multi-user equalization method and device for use in a receiver of the wireless communication system. 
     BACKGROUND OF THE INVENTION 
     Wireless signals are usually blocked by barriers in the path of propagation, which leads to reflection, dispersion, attenuation and the like. In turn, the multipath effect is produced, so that signals received at antenna of a receiver are actually a linear superposition of multipath signals arriving from different paths, and those multipath signals from various paths are difference in delays, amplitudes, phases and frequencies. On the other hand, for a wireless communication system adopting code division multiple access (CDMA) technology, since the wireless interface uses different channellisation codes to differentiate users, multiple users can share the same frequency and slot resources. Theoretically, orthogonal channellisation codes can not only differentiate different users but also minimize the interference among multiple users. However, due to the limitation of multipath effect, spreading gain and system synchronization accuracy, the complete orthogonality between multiple user data is hardly to be ensured, and multiple access interference (MAI) and inter-symbol interference (ISI) are inevitably caused, system capacity and communication quality will be impaired accordingly. Thus, how to suppress MAI and ISI has drawn a wide range of attention from both academic and industrial circles. 
     At present, the main approaches to suppress MAI and ISI are multi-user detection (MUD) and multi-user equalizer (MUE), both of which play important roles in improving bit error rate (BER) performance and increasing system capacity. Unlike a conventional receiver that contains a matched filter only, an MUD or MUE receiver makes full use of the information from all users, even from the interferers, to suppress MAI and ISI. 
     One of the effective methods to eliminate the aforesaid interference is to perform joint detection on the obtained information after a matched filter. The joint detection is an ideal multi-user detection to extract all user data in parallel. 
     In general, the receiver estimates channel parameters using specific training sequences in various communication bursts. Also, the receiver extracts in parallel all user data, which share the same slot and frequency, by using a specific algorithm, removes the interference caused by non-target user data, and lastly obtains a target signal that has a high signal-to-noise ratio. 
     The method of joint detection increases the allowable range of signal fluctuation, and makes it possible to share resources by more users. Consequently, the transmission capacity of each carrier bandwidth is enhanced effectively and the spectrum efficiency is improved. However, the method of joint detection, though having many advantages, encounters a number of limitations in practice because of its high complexity and poor real time performance. 
     As the most universal method with low computational complexity, adaptive multi-user detection is an alternative for the current RAKE receiver in place of joint detection. Since the method does not need to extract all user data in parallel, it is much simpler than the method of joint detection. 
       FIG. 1  illustrates a conventional multi-user equalizer using a least-mean-square (LMS) method. This method obtains adjusted weighting parameters, which are used to weight and despread user data, by training a despread training sequence. The operation process of the equalizer consists of two stages, i.e. a training stage and a despreading stage. Hereinafter, the training stage will be described first. 
     In  FIG. 1 , x[n] denotes a received signal based on the chip rate. In the training stage, the signal is a set of known spread training sequences. Having been transmitted through different paths, the signal x[n] has different delays corresponding to each path respectively. Having been weighted by weighting modules  12 - 1 ,  12 - 2 , . . . ,  12 -K, the received training signal gets bit-rate-based training sequence signals, wherein W k * denotes a weight vector whose dimensionality equals the spreading factor, and specifically, it is a weight vector processing the k th  propagation path. Then, K weight vectors, W k   H  (k=1, 2, . . . , K), form a weight matrix W H =[w 1   H |w 2   H | . . . w K   H ] The weighted training signals are summed by an adder  131  to obtain bit-rate-based despread training sequence d′[m], wherein m denotes the times the weight vector has been adjusted, and d[m] denotes a known reference training signal for a target user. An adder  132  adds d′[m] and −d[m] to obtain their difference e[m]. The difference e[m] is fed back to computing means  133  to adjust the weight coefficient W k *. The adjusted weight coefficient W k * is applied to each of the corresponding weighting modules  12 - 1 ,  12 - 2 , . . . ,  12 -K. After times of feedback adjusting, a proper weight coefficient is obtained, which is used to perform a weight process on user data of multipath transmission. Hereinafter, the above process will be described in conjunction with mathematical expressions. 
     Suppose the received data x[n] is over-sampled with the over-sampling rate SF to obtain the vector X, and then:
 
 X   n   T   =[x   1   T   |x   2   T   | . . . x   K   T ] n   (1)
 
     Here, X n   T  is a chip-rate-based training sequence through channel fading and sampling. 
     Through weighting by weight modules  12 - 1 ,  12 - 2 , . . . ,  12 -K and summing by the adder  131 , a chip-rate-based output signal d′[m] is obtained:
 
d′[m]=W H X m   (2)
 
     The error between the output signal d′[m] and the known reference signal d[m] is:
 
 e[m]=d′[m]−d[m]   (3)
 
     The computing means  133  decides whether to finish the adjustment on the weight coefficient, and computes the adjusted weight coefficient according to the last computation results if the adjustment on the weight coefficient is not finished. There are two methods for judging whether to finish the adjustment on the weight coefficient. One method is to compute the LMS function as shown in equation (4):
 
min w   {E|e[m]   2 }=min w   {E [( d′[m]−d[m] )( d′[m]−d[m] )*]}  (4)
 
     It is determined whether to finish the adjustment on the weight coefficient by deciding whether result of the objective function is smaller than a predetermined threshold. 
     The other method is to, during adjusting the weight coefficient by using a training sequence of a finite length, determine whether to finish the adjustment on the weight coefficient by deciding whether the training sequence is finished. 
     For instance, the weight coefficient is adjusted based on the recursive discipline for the weight vector from step m to step m+1 by equation (5):
 
 W   m+1   H   =W   m   H +2 μe*[m]X   m   =W   m   H +2μ( d′[m]−d[m] )* X   m   (5)
 
     wherein μ denotes the step of the recursive operation. 
     For a more accurate prediction and a faster convergence speed, a normalized least mean square (NMLS) method can be used. This is an improved LMS method, in which the increment of equation (5) is normalized to obtain the following equation: 
     
       
         
           
             
               
                 
                   
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     The above training step is performed recursively until the adjustment is decided to be finished by one of the above two methods. The weight coefficient obtained therefrom can be used to suppress MAI and ISI and recover the original user data successfully. 
     After the training stage is finished, it goes to the despreading stage, in which the despread data are obtained by weight-summing the multipath user data using the weight coefficient obtained in the training stage. Herein, X n   T  of equation (1) is the user data experiencing channel fading, and d′[m] of equation (2) is the despread user data. The despread data can be directly output from the adder  131  for use with a subsequent process in the receiver. 
     Since the above-described training method is a method for adjusting a weight coefficient based on the chip rate and the speed of parameter adjustment is limited by the feedback period, it is not suitable for a communication system that sets much higher requirements on real time. 
     Therefore, there is a need for a new method and device capable of fast adjusting a weight coefficient so as to meet the higher requirements of a system on real time. 
     SUMMARY OF THE INVENTION 
     In order to settle the above-mentioned technical problem, the present invention provides an adaptive multi-user equalization device and method for a receiver of a wireless communication system, and a computer program implementing the method, and in particular, a chip-rate-based adaptive multi-user equalization device and method, and a computer program implementing the method. 
     An object of the present invention is to provide an adaptive multi-user equalization device, method and program capable of fast adjusting weight coefficients of all communication paths. 
     Another object of the present invention is to provide an adaptive multi-user equalization device, method and program capable of obtaining more accurate weight coefficients. 
     In order to achieve part or all of the above objects, according to one aspect of the present invention, there is provided an equalization device for a wireless communication receiver, which comprises: a plurality of weighing means for, according to a plurality of adjustable weight coefficients, respectively performing a weight process on received multipath signals to obtain a plurality of weight results, said multipath signals including known training sequences and user data experiencing channel fading; a plurality of adjusting means for comparing said plurality of weight results of said training sequence, which are output by said plurality of weighting means, with a plurality of known reference signals respectively, and respectively adjusting the weight coefficient of each of the weighting means in accordance with the comparison results so that said weighting means can weight said user data by utilizing the adjusted weight coefficient; and combining means for combining the plurality of weight results of said user data, which are output by said plurality of weighting means, to obtain the combined equalization signal. 
     According to one aspect of the present invention, there is provided an equalization method for using in a wireless communication receiver, which comprising the steps of: a) according to a plurality of adjustable weight coefficients, respectively performing a weight process on known training sequences in received multipath signals experiencing channel fading to obtain a plurality of weight results of the training sequences; b) comparing said plurality of weight results of said training sequence with a plurality of known reference signals respectively, and respectively adjusting each of the weight coefficients in accordance with the comparison results; c) according to the plurality of weight coefficients obtained from adjustment, performing a weight process on user data in said received multipath signals experiencing channel fading to obtain a plurality of weight results of the user data; and d) combining the plurality of weight results of said user data to obtain the combined equalization signal. 
     According to a further aspect of the present invention, there is provided a mobile terminal, which comprises: a reception device for receiving a signal through wireless channel fading; an equalization device for performing a chip-rate-based signal equalization on said received signal to obtain user data for use in despreading and equalization; and a despread device for despreading said equalized user data. 
     Compared with conventional bit-rate-based equalization methods and devices, the present application is able to fast adjust the weight coefficient of each communication path and obtain more accurate weight coefficients. 
     Other objects and effects of the present invention will become more apparent and ready to understand from the following description taken in conjunction with the accompanying drawings, contents in the claims, and with a more comprehensive understanding of the present invention 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Hereinafter, the preferred embodiments of the present invention will be described with reference to the accompanying drawings in which: 
         FIG. 1  illustrates a schematic view of a conventional equalizer; 
         FIG. 2  illustrates a block diagram of a multi-user equalizer according to an embodiment of the present invention; 
         FIG. 3  illustrates a schematic view of a receiver comprising an equalizer according to an embodiment of the present invention; and 
         FIG. 4  illustrates a flow chart of a multi-user equalization method according to an embodiment of the present invention. 
     
    
    
     In all the accompanying drawings, identical numerals represent like or corresponding features or functions. 
     DETAILED DESCRIPTION OF THE INVENTION 
     The basic discipline of the present invention is to adjust weight coefficients based on the chip rate, i.e., the adjustment period of weight coefficients is the chip period. Since a bit is formed by SF (spreading factor) chips and the chip rate is SF times as large as the bit rate, the convergence speed of a method of adjusting weight coefficients based on the chip rate is much faster than that of a conventional bit-rate-based method. 
     The present invention adjusts weight coefficients by directly comparing weighted signals with reference signals, thereby shortening the adjustment period greatly. 
     The principle of the present invention will be described with reference to  FIG. 2  below. 
       FIG. 2  shows a block diagram of a multi-user equalizer according to an embodiment of the present invention. Said equalizer comprises: sampling means  21 , weighting means  22 , adjusting means  26  and summing means  25 . 
     In  FIG. 2 , the operation of the device is divided into two stages, a training stage of weight coefficients and a chip-equalizing stage. First, the training stage of weight coefficients will be described below. 
     During the training stage of weight coefficients, the received signal x[n] is a known training signal. The sampling means  21  performs a multiple sample on the received signal x[n]. The sampled training signal is transferred to and weighted by the weighting means  22  to obtain a training sequence c′ k [m] which represents a training sequence through channel fading and obtained via the k th  transmission path. Then, the adjusting means  26  compares the received training sequence c′ k [m] with a known reference sequence c k [m], adjusts weight coefficients recursively, determines whether to finish the training process based on whether the difference between the received training sequence c′ k [m] and the known training sequence c k [m] meets predetermined conditions or whether the training sequence c′ k [m] is finished, and obtains proper weight coefficients. 
     Upon completion of the training, the chip equalization is performed using the weight coefficients obtained in the training process. Herein, x[n] is the received user data. The sampling means  21  performs a multiple sample on the received user data x[n]. The sampled signal is transferred to the weighting means  22 . The weighting means  22  performs a weight process on the received user data using the proper weight coefficients determined at the training stage. And the summing means  25  sums the user data, which come from all paths and have gone through the weight process, to obtain a multiuser equalized signal c′[m] for use in a subsequent despeading process. 
       FIG. 3  shows a schematic view of a receiver comprising an equalizer according to an embodiment of the present invention. 
     In  FIG. 3 , the receiver comprises an equalizer  20  and a despreader  27 , wherein the equalizer  20  comprises a sampler  21 , a plurality of parallel weighters  22 - 1 ,  22 - 2 , . . . ,  22 -K, a plurality of adders  23 - 1 ,  23 - 2 , . . . ,  23 -K, a plurality of computing means  24 - 1 ,  24 - 2 , . . . ,  24 -K, and an adder  25 . In the equalizer  20 , one adder  23 - k  and one computing means  24 - k  form one adjusting means  26 - k , wherein k=1, 2, . . . , K. K adjusting means form the adjusting means in  FIG. 2 . The weighters  22 - 1 ,  22 - 2 , . . . ,  22 -K in  FIG. 3  correspond to the weighting means  22  in  FIG. 2 . The operation of the receiver shown in  FIG. 3  is also divided into two stages, namely a training stage and a chip-equalizing stage. First, the training stage will be described below. 
     In the training stage, the received signal x[n] is a training sequence. First, the sampler  21  performs an over-sampling on the training sequence x[n], such as a multiple sample to sample plural points of each chip in the received signal. Take a sample multiple equaling i for example. Then, the sampled signal can be expressed as X 1/i =[x 1 , x 2 , . . . , x i ], and an estimate signal component in the k th  path is obtained through weight-summing by the weighters  22 - 1 ,  22 - 2 , . . . ,  22 -K:
 
c k ′=W k   H X 1/i   (7)
 
     Here, Wk is the weight coefficient with dimension of 1 by i for the k th  path. 
     The sampler  21  in  FIG. 3  is not a necessary device, and the object of the present invention can be achieved even if the device is omitted. In addition, the sample multiple i of the sampler  21  may be any integer multiple, such as 2 and 4. The larger the sample multiple is, the higher accuracy of the adjustment is, while the higher complexity is. Thus, it is preferable to adopt the sample multiple of 8. 
     In the adder  25 , the path signal components c 1 ′[m], c 2 ′[m], . . . , c K ′[m] output from the weighters  22 - 1 ,  22 - 2 , . . . ,  22 -K are summed to obtain a gross estimate output, namely a chip-rate-based signal: 
     
       
         
           
             
               
                 
                   
                     
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     At the adders  23 - 1 ,  23 - 2 , . . . ,  23 -K, the estimate value c k ′[m] for each path is compared with the reference signal c k [m] for each path to obtain the difference between c k ′[m] and c k [m]. The above reference signal for each path can be computed through the following equation:
 
 c   k   [m]=P   k ·{Midamble},  k= 1, 2, . . .  K   (9)
 
     In the equation (9), P k  is channel parameters used for each adjusting means and representing different signal paths, and {Midamble} is a known training sequence code. 
     Whether to finish the training stage is decided by one of the following two methods. 
     One method is to input the comparison results output from the adders  23 - 1 ,  23 - 2 , . . . ,  23 -K into the computing means  24 - 1 ,  24 - 2 , . . . ,  24 -K. In each of the computing means  24 - 2 ,  24 - 2 , . . . ,  24 -K, it is decided whether the objective function of the aforesaid difference meets a predetermined standard, for example, it is decided whether it is smaller than a predetermined threshold. If the objective function does not meet the predetermined standard, then the weight coefficient is recursively adjusted in accordance with the adjustment equation until the objective function meets the predetermined standard. For instance, the aforesaid objective function may be the Least-Mean-Square function: 
     
       
         
           
             
               
                 
                   
                     
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     wherein m denotes the times the current weight coefficient is adjusted. 
     The other method of deciding whether to finish the training stage is to decide whether to finish the training stage by deciding whether the training sequence is finished in the computing means  24 - 1 ,  24 - 2 , . . . ,  24 -K. 
     The recursive adjustment equation for the k th  path weight from step m to step m+1 is, for example, described as: 
     
       
         
           
             
               
                 
                   
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     Similar to the weight adjustment equation used in the prior art, the equation (11) can also use other equation, which is capable of converging weight coefficients, to adjust the weight coefficient. Finally, after the training stage of plural recursion (chip), the ultimate weight coefficient W k * is obtained for weighting the user data. 
     After the training stage is finished, the chip equalizing stage is entered. In the adders  22 - 1 ,  22 - 2 , . . . ,  22 -K, a weight process is performed on the received user data by using the obtained ideal weight coefficient, i.e., the received user data are equalize-compensated. Then, each component is added together, and the user data for chip equalization are obtained. 
     Afterwards, a despreader  27  despreads the equalized user data by using the spread code s #  of the target user to obtain the user data for ultimate output. Here, s #  is the channellisation code of the target user. 
     According to the TD-SCDMA standard, the pulse architecture of each service slot is formed by two data symbol areas, a 144-chip training sequence and a 16-chip protection area. In each slot, when receiving a training sequence, the weight vector is adjusted recursively using the multi-user equalizer of the present invention, and the recursive adjustment is performed once using each of the chips of the training sequence. Thus, the recursive adjustment is performed for 144 times in each slot. The weight results are not summed and output to the despreader in the process of recursively adjusting the weight vector; instead, the user data are weighted based on the adjusted weight vector and output to the despreader when it begins to receive the user data. 
     The method of the present invention can be combined with the existing joint detection method, i.e. the joint detection method can be applied to the data d′[m] despread by the despreader  27  to minimize the MAI and ISI. 
     As is clear from the above description, in the prior art, the despreading process is completed in the weighters  12 - 1 ,  12 - 2 , . . . ,  12 -K and the adder  131 , the output signal d′[m] is the despread chip-rate-based data, and a proper weight vector is determined by comparing the despread data with the reference data. However, the present invention separates the process of adjusting the weight vector from the despreading process, carries out the adjustment in the weight vector in the chip-rate-based stage prior to the despreading, and adjusts the weight vector using the normalization and least mean square method, thereby get a faster convergence speed and a higher adjustment accuracy. 
     Referring to  FIG. 4 , the flow of operating the method of the present invention will be described from another aspect. As shown in  FIG. 4 , an input signal training sequence and data user data are over-sampled in step S 302 , for instance, each chip is sampled with i points. Then, a weight process is performed on the sampled training sequence in step S 303 , and the weight coefficient is adjusted by comparing the weighted training sequence with the known reference sequence in step S 304 . Next, whether the weight coefficient obtained in step S 304  meets the predetermined condition or the training sequence used to train the weight coefficient is finished is decided in step S 305 ; if the decision result is “yes”, then it goes to step S 306 , otherwise it goes back to step S 302  to implement the process of adjusting weight parameters from step S 302  to step S 305 . After step S 306 , a weight process is performed on the sampled data signal using the determined weight coefficient. Afterwards, a combination process is performed on the received data signals using the determined weight coefficient in step S 307 , and the combined data signals are despread in step S 308 . And then, the whole process is finished. 
     Besides the embodiments provided above, many modifications could be made to the present invention. Although the technical solution of the present invention has been described with TD-SCDMA systems taken for example, it is obvious that the present invention can still be applied to other CDMA communication systems. A major feature of the present invention lies in the adjusting of each transmission path in the chip-rate-based stage prior to the despreading, rather than the adjusting of weight coefficients in the bit-rate-based stage. Thus, the present invention can be adapted to the case of rapid changes in transmission paths, especially to TDD systems. 
     Various alternations or modifications can be made without departing the concept and scope of the present invention. It is to be understood that the present invention is not limited to specific embodiments, and that the scope of the present invention is defined by the appended claims.