Abstract:
A receiver including a signal reception unit, for receiving a signal from a dynamically fading channel, a demodulator, connected to the signal reception unit, for demodulating the received signal, thereby producing a demodulated signal therefrom, a quantizing processor, connected to the demodulator and to the signal reception unit, for analyzing the received signal and for quantizing the demodulated signal, thereby producing a quantized signal, and a decoder, connected to the quantizing processor, for decoding the quantized signal, wherein the quantizing processor normalizes the demodulated signal according to the estimated fading of the received signal.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of allowed U.S. patent application Ser. No. 09/103,683 now U.S. Pat. No. 6,047,035 filed Jun. 15, 1998, which is incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to a method and a device for quantizing the input to a soft decoder and to a method and a device for quantizing the input to a Viterbi decoder, operating over fading channels, in particular. 
     BACKGROUND OF THE INVENTION 
     The classical problem of quantizing an analog signal into some set of a-priori chosen discrete-alphabet values, was extensively studied following the pioneering work of Shannon on rate distortion theory published in 1948 by C. E. Shannon, “A mathematical theory of communication” Bell System Technical Journal, 27, 1948. 
     Various quantization methods are known to those skilled in the art. In general, each of these methods utilizes a specific cost function. The object of a quantizer is to minimize the respective quantization cost. 
     In digital communication applications, digital information is modulated onto a carrier signal which is then, transmitted over an analog channel. The output of the channel is sampled, quantized and processed by the receiver in order to recover the transmitted digital information. 
     The natural cost function which is used in this case is the probability of error. The objective of the quantization strategy is to minimize the probability of incorrectly receiving the transmitted information. 
     Unfortunately, analytically minimizing this cost function is mathematically intractable even for relatively simple scenarios (see e.g. J. Salz and E. Zehavi, “ Decoding under integer metric constraints” , IEEE Transactions on Communications, vol. 43, pp. 307-317, 1995). 
     Reference is now made to  FIG. 1 , which is a schematic illustration of a digital communication receiver, generally referenced  10 , known in the art. 
     System  10  includes an analog to digital (A/D) converter  12 , a demodulator  14 , an automatic-gain-control (AGC) unit  15 , a quantizer  16 , and a decoder  18 . 
     The transmitted signal is picked-up by the receiver&#39;s antenna and is then amplified and filtered at the receiver&#39;s front-end (not shown in FIG.  1 ). The resulting signal is fed into system  10  at the input of the A/D  12 . 
     The A/D  12  converts the signal to digital samples and provides them to the demodulator  14 . The demodulator  14  processes the digitized samples and produces a demodulated signal Y[n]. The AGC unit  15  normalizes the demodulated signal Y[n], to fit into the dynamic range of the quantizer  16 , as follows
 
 {tilde over (Y)}[n]=AGC   —   Gain·Y[n]   Equation 1 
 
where AGC_Gain may vary from sample to sample.
 
     The quantizer  16  processes the normalized samples {tilde over (Y)}[n], thereby producing the quantized samples Q({tilde over (Y)}[n]) such that each sample is represented by B bits. In most cases, Q({tilde over (Y)}[n]) is simply the nearest element to {tilde over (Y)}[n] in the set of 2 B  possible quantization levels. The quantized samples are provided to the decoder  18 , which in turn attempts to recover the transmitted information. 
     It is noted that system  10  is a mere example to systems which are known in the art. Those skilled in the art are familiar with several other configurations. For example, in a spread-spectrum CDMA (Code Division Multiple Access) environment operating on a multi-path fading channel, the demodulator is replaced by a rake demodulator. A rake demodulator includes a plurality of demodulating fingers, each of which attempts to detect and demodulate a different replica of the transmitted signal. 
     According to another example, an analog demodulator may be utilized. In this case, an A/D converter is placed after the demodulator, sometimes also serving as a quantizer. 
     However, regardless of the specific receiver type and structure, its complexity, or more particularly, the complexity of the decoder, increases with B—the number of bits used to represent each quantized sample Q({tilde over (Y)}[n]). Therefore, it is desirable to choose a quantization strategy that minimizes B. 
     The minimal possible value for B is B=1, which is called “Hard Decision”. In this case the numbers produced by the quantizer are restricted to have only two possible values “one” and “zero”. All other situations are called “Soft Decision” and correspond to the case where B&gt;1. 
     When hard-decision is used, only the sign of {tilde over (Y)}[n] is fed into the decoder, thus completely ignoring any information conveyed by its magnitude. Therefore, hard-decision decoding, although very simple to implement, can lead to a significant degradation in performance. 
     On the other hand, when B is very large, the full potential of the code is utilized. It will be noted however, that in this case, the decoder complexity is high. It is therefore desirable to come-up with an efficient quantization strategy that allows good tradeoff between decoder complexity and quantization loss. 
     Methods for quantizing the input to a soft decoder operating over a static AWGN channel are described in Onyszchuk et. al. In this case, the demodulated signal can be represented by
 
 Y[n]=h·S[n]+W[n]   Equation 2 
 
where S[n] is the desired (information bearing) signal that needs to be decoded, h is the complex valued channel gain, and W[n] is an additive white Gaussian noise term.
 
     The conventional quantization strategy for such channels is based on first normalizing the RMS (Root Mean Square) value of Y[n] to a pre-determined value denoted by Desired_RMS, and then applying a uniform quantizer e.g. a conventional A/D converter. The normalization operation is performed by the AGC according to Equation 1, by setting 
             AGC_Gain   =     Desired_RMS   Estimated_RMS             Equation   ⁢           ⁢   3             
         where the Estimated_RMS may be computed in a variety of ways, e.g. 
             Estimated_RMS   =         1   N     ·       ∑     n   =   1     N     ⁢           ⁢            Y   ⁡     [   n   ]            2                   Equation   ⁢           ⁢   4             
       

     This quantization strategy performs well when the channel is static, (i.e. the model in Equation 2 holds). 
     However, when implemented for non-static channels, this approach can lead to a significant degradation in performance. In order to clarify this, we now consider a simple generalization of Equation 2, in which
 
 Y[n]=h[n]·S[n]+W[n]   Equation 5 
 
where, as before, Y[n] is the demodulated signal; S[n] is the information bearing signal; W[n] is the additive white Gaussian noise term; and h[n] is the complex valued channel gain which is now allowed to be time varying.
 
     Reference is now made to  FIGS. 2A ,  2 B,  2 C and  2 D. 
       FIG. 2A  is an illustration of a frame of a transmitted signal, generally referenced  140 A. The signal is divided into a plurality of sections  150 A,  152 A,  154 A,  156 A,  158 A and  160 A, each including a plurality of symbols represented by dots. For example, section  150 A includes five symbols. The first three symbols and the fifth symbol are of a value of +1, while the fourth symbol is of a value of −1. 
       FIG. 2B  is an illustration of a dynamically fading channel where we plotted only its magnitude |h[n]|, generally referenced  142 . Each of the dots along the line represents the gain of the channel at a point in time which is respective to a symbol of signal  140 A (FIG.  2 A). 
       FIG. 2C  is an illustration of the demodulated signal Y[n] of the received frame in the absence of noise according to the simple model of Equation 5, generally referenced  140 B. Each of the samples in the demodulated signal  140 B is, in general, a multiplication of a selected transmitted symbol of signal  140 A ( FIG. 2A ) and the respective fading value of the channel  142  (FIG.  2 B). 
       FIG. 2D  is an illustration of the quantized signal Q({tilde over (Y)}[n]), produced from signal  140 B, when AGC_Gain is set to unity and the following five level uniform quantizer utilized, 
               Q   ⁢           ⁢     (       Y   ~     ⁡     [   n   ]       )       =     {         1       if             Y   ~     ⁡     [   n   ]       &gt;   0.75             0.5       if         0.75   ≥       Y   ~     ⁡     [   n   ]       &gt;   0.25             0       if         0.25   ≥       Y   ~     ⁡     [   n   ]       &gt;     -   0.25                 -   0.5         if           -   0.25     ≥       Y   ~     ⁡     [   n   ]       &gt;     -   0.75                 -   1         if             Y   ~     ⁡     [   n   ]       ≤     -   0.75                       Equation   ⁢           ⁢   6               
     As can be seen from Equation 5, Equation 6 and  FIG. 2D , all samples for which the fade magnitude is smaller than 0.25, such as the samples in section  158 B (FIG.  2 C), are mapped by the quantizer to the value “0” (section  158 C). 
     These are called erasures, since they contain no information on the actual transmitted bit—it can equally likely be a “1” or a “−1”. 
     It will be appreciated by those skilled in the art (see for example: G. C. Clark Jr and J. Bibb Cain “Error —Correction Coding for Digital Communications” Chapter 5) that if the number of erasures is larger than a certain threshold related to the minimum distance of the code, then even an optimal decoder is likely to be in error. 
     Thus, whenever a deep channel fade occur for a sufficiently long period, a decoding error will occurs due to the quantization of the sampled data during the fade into erasures. This phenomenon happens regardless of the specific decoding method and/or decoding structure. Furthermore, even if erasures do not occur, decoding errors are still most likely to occur during channel fades, since the SNR (Signal-to-Noise Ratio) is low in these periods. 
     It is therefore clear that in a fading environment it is the quantization of the samples corresponding to low channel gain that attribute the most to the quantization loss. 
     One simple way to reduce the quantization loss is by using a larger value of Desired_RMS in Equation 3. With this approach, the signal is amplified so that its low magnitude portion is better mapped on the dynamic range of the quantizer. The price is of course worsening the mapping of the large magnitude portion of the signal that leads to clipping effects. Such clipping effects have a negligible effect on the overall performance, since they occur when the SNR is relatively high. Thus, overall an improvement in performance is achieved. However, if the channel happens to be static, the Desired_RMS value will no longer correspond to its optimal value, resulting in an increase in quantization loss. Furthermore, even with fading channels, different Desired_RMS values are required for different fading characteristics. The approach presented below circumvents these issues. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings in which: 
         FIG. 1  is a schematic illustration of a signal decoding system, which is known in the art; 
         FIG. 2A  is an illustration of a frame of a transmitted signal; 
         FIG. 2B  is an illustration of a dynamically fading channel; 
         FIG. 2C  is an illustration of a frame of a received signal, after traveling through the fading channel of  FIG. 2B ; 
         FIG. 2D  is an illustration of a quantized frame, produced from the frame of received signal of  FIG. 2C ; 
         FIG. 3  is a schematic illustration of a receiver, constructed and operative in accordance with an embodiment of the present invention; 
         FIG. 4  is a schematic illustration of a method for operating the receiver of  FIG. 3 , operative in accordance with an embodiment of the present invention; 
         FIG. 5  is a schematic illustration of a method, operative in accordance with an embodiment of the present invention; 
         FIG. 6  is a schematic illustration of a receiver, in which there is installed a quantizer, constructed and operative in accordance with a further embodiment of the present invention; and 
         FIG. 7  is a schematic illustration of a receiver, constructed and operative in accordance with another embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     The present invention overcomes the disadvantages of the prior art by providing a novel method which dynamically detects the characteristics of the transmission channel, and accordingly quantizes the received signal into a pre-selected set of alphabet values. 
     The method according to the present invention, estimates the dynamics of the transmission channel within the received frame, and accordingly provides the quantization strategy. 
     Reference is now made to  FIG. 5 , which is a schematic illustration of a method, operative in accordance with an embodiment of the present invention. The quantizer operates on blocks of N samples. Each of these blocks is processed according to the following steps: 
     In step  250 , the RMS of the received signal is estimated, e.g. according to Equation 4. 
     In step  252 , the quantities Θ min  and Θ max  are computed, where:
 
Θ max ≡Max n {Θ[n]} and Θ max ≡Min n {Θ[n]}  Equation 7 
         for 1≦n≦N, and Θ[n] is given by
 
 Θ[n]≡|real{ĥ[n]}|Imag{ĥ[n]}|   Equation 8 
   where ĥ[n] denotes an estimate of the channel tap value h[n].       

     In step  254 , the Desired_RMS value is determined by
 
 Desired   —   RMS   —   Fade=F (Θ min , Θ max )  Equation 9 
         where F( , ) is some function whose purpose is to have Desired_RMS_Fade equal to the Desired_RMS value used for static channels whenever Θ min  and Θ max  are close, and to increase the Desired_RMS value when Θ min  and Θ max  differ.       

     In step  256 , the received samples are normalized according to 
                 Y   ~     ⁡     [   n   ]       =         Desired_RMS   ⁢   _Fade     Estimated_RMS     ·     Y   ⁡     [   n   ]                 Equation   ⁢           ⁢   10             
 
     In step  258 , the normalized samples are quantized by setting Q({tilde over (Y)}[n]) as the closest value to {tilde over (Y)}[n] in the pre-determined quantizer alphabet. 
     It will be noted that for static channels (ignoring estimation errors) Θ min =Θ max . Therefore, the above procedure is reduced to the conventional quantization strategy described hereinabove. 
     However, if channel gain variations occurred within the received frame, then Θ min ±Θ max  and a larger value of Desired_RMS will be utilized, thus emphasizing the fade region as is indeed desirable. 
     Altogether, the method of the invention provides improved quantization for fading channels without increasing the quantization loss for static channels. 
     The difference Θ min −Θ max  can serve as an easily computable measure for the fade variability within the received frame, and in step  254  F(Θ min , Θ max ) can be implemented simply by means of a look-up table having Θ min −Θ max  at its input and Desired_RMS_Fade at its output. 
     Different tables may be used when the receiver has to cope with different codes, as is the case for example in IS-95 Rate-set 2 situations where the code properties (puncturing level) may vary from frame to frame depending on the data rate. 
     The quantization method of the present invention, provides code-dependent channel-dependent quantization, which can be tuned to the specific codes and channel conditions by properly adjusting the look-up table values, so that low quantization loss is achieved over a wide variety of practical scenarios. 
     According to another aspect of the invention, more complicated functionals can be used to detect the channel fading characteristic. An example for such a functional is given by Θ[n]≡|ĥ[n]| that should replace the functional in Equation 8. 
     This functional is more difficult to calculate but it provides better estimation of the fade variability. According to a further aspect of the invention, the fade duration is measured and incorporated in F(Θ min , Θ max ). 
     In another embodiment, the demodulator is replaced by the rake receiver. The above quantization procedure remains unchanged, except to the definitions of Θ[n] in Equation 8, that should now be: 
               Θ   ⁡     [   n   ]       ≡         ∑     k   =   1     F     ⁢           ⁢          Real   ⁢     {         h   ^     k     ⁡     [   n   ]       }              +          Imag   ⁢     {         h   ^     k     ⁡     [   n   ]       }                      Equation   ⁢           ⁢   11             
         where F denotes the number of active fingers, and where ĥ k [n] denotes the channel tap estimator of the k&#39;th finger.       

     In another embodiment, the data block may be divided into sub-blocks of size N 1 , N 2  . . . N k  such that 
       N   =       ∑     i   =   1     k     ⁢           ⁢     N   i           
 
     Then, the maximization and minimization in Equation 7 may be performed for each of K sub-blocks, yielding up-to K different values of Desired_RMS_Fade for a given data frame, K is a design parameter. In this situation, the quantizer should provide information regarding the different gains used within the data block to the decoder, thus enabling the decoder to compensate these gain variations during the decoding process. 
     Reference is now made to  FIG. 3 , which is a schematic illustration of a receiver, generally referenced  100 , constructed and operative in accordance with an embodiment of the present invention. 
     Receiver  100  includes a demodulator  102 , a frame buffer  104 , an analog to digital (A/D) converter  106 , a decoder  108  and a channel processor  110 . The frame buffer  104  is connected to the A/D converter  106  and to the demodulator  102 . The A/D converter  106  is further connected to the channel processor  110  and to the decoder  108 . 
     The receiver  100  receives a signal from an unknown dynamic channel. The demodulator  102  demodulates the received signal and stores the demodulated signal in the frame buffer  104 . At the same time, the channel processor  110  analyzes the received signal, thereby detecting the fading characteristics thereof and provides them to the A/D converter  106 . 
     The A/D converter  106  retrieves the demodulated signal and quantizes it according to the fading characteristics. For example, on the one hand, when the fading characteristics indicate that the signal was diminished by the fading channel, then the A/D converter  106  enhances the demodulated signal before or during the quantization procedure. On the other hand, when the fading characteristics indicate a static (i.e. non fading) channel, then the A/D converter  106  follows the conventional quantization strategy. 
     Finally, the A/D converter  106  provides the quantized signal to the decoder  108 , which in turn decodes it and provides a decoded signal at its output. 
     Reference is now made to  FIG. 4 , which is a schematic illustration of a method for operating the receiver  100  of FIG.  3 . 
     In step  170 , the receiver receives a portion of a signal from an unknown channel. The channel may impose either a diminishing or amplifying effect of the signal, thereby deforming it. 
     In step  172 , the receiver stores the received portion either in the received format or in a demodulated format. 
     In step  174 , the receiver analyzes the received signal, thereby detecting its channel characteristics. 
     In step  176 , the receiver determines from the channel characteristics, if the channel through which the signal traveled, is problematic. If so, then the receiver proceeds to step  178 . Otherwise, the receiver proceeds to step  180 . 
     In step  178 , the receiver estimates a correction action according to the detected channel characteristics. Then, the receiver proceeds to step  180 . 
     In step  180 , the receiver processes the received signal according to the estimated correction action. It will be noted that when the receiver determined that the channel is not problematic, then, the correction action is null. 
     It will be noted that the present invention can be implemented in many ways. For example, in accordance with a further embodiment of the present invention, there is provided a novel channel quantizer which replaces a conventional quantizer between the demodulator and the decoder. 
     Reference is now made to  FIG. 6 , which is a schematic illustration of a receiver, generally referenced  200 , in which there is installed a quantizer, generally referenced  216 , constructed and operative in accordance with a further embodiment of the present invention. The quantizer  216  is connected between a demodulator  202  and a decoder  208 . In the present example, the decoder  208  is a Viterbi decoder. 
     Quantizer  216  includes a channel estimator  210 , a controller  212 , a frame buffer  204  and a quantizing unit  206 . The controller  212  is connected between the quantizing unit  206  and the channel estimator  210 . The quantizing unit is also connected to the frame buffer  204 . 
     The channel estimator  210  is further connected to the source of the received signal (e.g. an antenna —not shown) which is also fed into the demodulator  202 . The frame buffer  204  is further connected to the demodulator  202 . The quantizing unit  206  is further connected to the decoder  208 . 
     The channel estimator  210  detects channel characteristics of a portion of the received signal and provides them to the controller  212 . The controller  212  analyses these characteristics thereby determining a set of correction parameters. At the same time, the demodulator  202  demodulates the portion of the received signal and provides the demodulated signal to the channel dependent quantizer  216 , where it is stored in the frame buffer  204 . 
     When the quantizing unit  206  receives the set of correction parameters from the controller  212 , it retrieves the respective demodulated signal from the frame buffer  204 . Then, the quantizing unit  206  quantizes the demodulated signal according to the set of correction parameters and provides the quantized signal to the decoder  208 . 
     According to another aspect of the present invention, the information regarding the channel characteristics is also used in the decoding stage. 
     Reference is now made to  FIG. 7 , which is a schematic illustration of a receiver, generally referenced  300 , constructed and operative in accordance with a further embodiment of the present invention. 
     Receiver  300  includes a demodulator  302 , a frame buffer  304 , a quantizer  306 , a decoder  308  and a channel tap estimator  312 . 
     The frame buffer  304  is connected to the demodulator  302  and to the quantizer  306 . The Viterbi decoder  308  is connected to the quantizer  306  and to the channel tap estimator  312 . 
     The demodulator  302  and the channel tap estimator  312  receive a portion of a received signal which was transmitted via an unknown dynamic channel. The demodulator  302  demodulates the received signal and stores the demodulated signal in frame buffer  304 . The channel tap estimator  312  analyses the received signal, produces a set of correction parameters and provides them to the quantizer  306  and to the decoder  308 . 
     The quantizer  306  retrieves the demodulated signal from the frame buffer  304  and quantizes it according to the set of correction parameters received from the channel tap estimator  312 , thereby producing a quantized signal. Then, the quantizer  306  provides the quantized signal to the decoder  308 . 
     The decoder  308  decodes the quantized signal in view of the set of correction parameters received from the channel tap estimator  312 . 
     It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather the scope of the present invention is defined only by the claims which follow.