Abstract:
For digital TVs, transmission parameter signaling (TPS) data are normally required to be decoded and checked in every signal frame. In the Chinese DTV-T standard, these TPS data are transmitted over subcarriers in a contiguous frequency band of width 72 kHz, with the result that the SNR for these subcarriers may drop to a very low value due to lack of frequency diversity. The TPS data decoding error rate may rise significantly, severely impacting the DTV performance. A reliability detector is used to provide a reliability indication of the decoded TPS data. If this indication indicates that the decoded TPS data are likely to be incorrect, the receiver may discard the presently decoded TPS data and use the previously decoded ones (obtained when the reliability measure was high) or may take other appropriate actions. The reliability detector may include an SNR estimator, a comparator, and possibly storage. The SNR estimator estimates the SNR based on the present set of intermediate results obtained through the TPS data decoder and possibly sets of intermediate results obtained at earlier times.

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) AND CLAIM OF PRIORITY 
       [0001]    This application claims the benefit under 35 U.S.C. § 119(a) to a Chinese patent application filed in the State Intellectual Property Office of the People&#39;s Republic of China on Nov. 30, 2007 and assigned Serial No. 200710195513.2, the entire disclosure of which is hereby incorporated by reference. 
       TECHNICAL FIELD OF THE INVENTION 
       [0002]    The invention relates generally to digital television systems and, more particularly, to providing a reliability indicator at the receiver in a digital television system. 
       BACKGROUND OF THE INVENTION 
       [0003]    In August 2006, the standard for terrestrial digital television (DTV) broadcasting, hereinafter referred to as the DTV-T standard, was issued in China. The DTV-T standard contains a multi-carrier (MC) option and a single-carrier (SC) option. In the MC option, multiple subcarriers are used and data are transmitted on these subcarriers. The DTV-T standard states that 3780 subcarriers are used (generally regarded as the multicarrier option), of which 36 subcarriers are reserved for transmission parameter signaling (TPS) data. 
         [0004]    A TPS data set is composed of seven bits of information. These seven bits can be partitioned into two sets, hereinafter referred to as the α-set and the β-set. The α-set consists of six bits indicating the modulation format used, the code rate used, the interleaving option used, etc. The β-set consists of only one bit, which is used to indicate which option (the MC option or the SC option) is used. The six β-set data are represented by a selected one of a set of 64 biorthogonal length-32 Walsh codes, which is then scrambled with a pseudo random noise (PN) sequence. The one β-set bit is duplicated four times. In total, the seven bits of TPS data are represented by a 36-bit sequence. Frequency-domain interleaving is then applied to the 36 bits in such a way that they are not transmitted on the subcarriers in a sequential order. These data are transmitted on subcarriers with subcarrier numbers 0 to 17 and 3762 to 3779. That is, the 36 subcarriers transmitting the TPS data are within the band of subcarriers from 0 to 17 and from 3762 to 3779. Due to the cyclic property of the Discrete Fourier Transform (DFT), these 36 subcarriers essentially fall into a contiguous band of width 36×2 kHz=72 kHz. 
         [0005]    If the band that carries the TPS data is under deep fading, the subcarrier symbol energy-to-noise ratio may drop to a very low value. The TPS data decoding error rate for the α-set may be significantly increased. If the receiver finds that the α-set data are not consistent with the expected ones, it may discard the signal frame. Since the DTV-T standard employs very deep interleaving, a mistaken discard of one signal frame can affect the data of many signal frames, and may even lead to a loss of several image frames in the TV video. Therefore, the consequence of a significant increase in the TPS data decoding error rate caused by deep fading is severe. 
       SUMMARY OF THE INVENTION 
       [0006]    A reliability indicator is provided for TPS data that are decoded at the receiver in a digital television system. This indicator indicates whether the decoded α-set TPS data are likely to be incorrect. If that is the case, the receiver may discard the presently decoded α-set data and use the previously decoded ones (obtained when the reliability measure was high) or may take other appropriate actions. Note that if the subcarriers carrying TPS data are not in deep fade, the reliability indicator is likely to indicate that the decoded α-set data are reliable, so that the receiver can distinguish the α-set data if the currently received signal frame is for the desired DTV service or for other services. In the case where the subcarriers are in deep fade and the reliability indicator gives an indication that the decoded α-set data are likely to be erroneous, the receiver can reduce the likelihood of a mistaken discard of a signal frame. The ability of the receiver to distinguish different services by means of the α-set data is sacrificed, but distinguishing different services based on possibly erroneous α-set TPS data is always problematic. Using the present reliability indicator therefore improves viewing quality of TV programs if the TV signal contains only one service (TV program broadcasting). 
         [0007]    Other features and advantages will be understood upon reading and understanding the detailed description of exemplary embodiments, found herein below, in conjunction with reference to the drawings, a brief description of which is provided below. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  is a plot of error probability in α-set TPS data decoding on an additive white Gaussian noise (AWGN) channel versus the subcarrier symbol energy-to-noise ratio. 
           [0009]      FIG. 2  is a block diagram of a TPS data decoder model. 
           [0010]      FIG. 3  is a block diagram of a TPS data reliability detector. 
       
    
    
       [0011]    There follows a more detailed description of the present invention. Those skilled in the art will realize that the following detailed description is illustrative only and is not intended to be in any way limiting. Other embodiments of the present invention will readily suggest themselves to such skilled persons having the benefit of this disclosure. Reference will now be made in detail to embodiments of the present invention as illustrated in the accompanying drawings. The same reference indicators will be used throughout the drawings and the following detailed description to refer to the same or like parts. 
       DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
       [0012]      FIG. 1  plots the error probability in decoding the six bits in the α-set, P α , against the subcarrier symbol energy to noise ratio, E s /N 0 , for AWGN channels. It is apparent that E s /N 0  equal to about 2.2 dB gives a P α  value of 10 −11 , corresponding to a commonly used view-quality criterion of one error event every one hour of TV viewing. Although the required E s /N 0  value is very low, the received E s /N 0  value can drop significantly when the received signal is in deep fade. A commonly used technique to prevent this significant drop in E s /N 0  is to use frequency diversity. This diversity can be utilized if the frequency band used to transmit the signal is wider than the coherence bandwidth of the channel. Although a DTV-T signal occupies a bandwidth of about 8 MHz, the subcarriers that carry the TPS data are clustered within a bandwidth of only 72 kHz. Utilizing frequency diversity is not always possible. 
         [0013]    The receiver may not need to check the bit in the β-set for each signal frame because, in practice, the broadcaster does not change the transmission option used during broadcasting. (A signal frame is the basic unit for carrying data.) However, the α-set bits are normally required to be decoded and checked for every signal frame. This is because some α-set bits are reserved for future use, and this feature enables TV broadcasters to embed other services into TV signals. If the band that carries the TPS data is under deep fading, E s /N 0  may drop to a very low value. The TPS data decoding error rate for the α-set may be significantly increased. If the receiver finds that the α-set data are not consistent with the expected ones, it may discard the signal frame. Since the DTV-T standard employs very deep interleaving, a mistaken discard of one signal frame can affect the data of many signal frames, and may even lead to a loss of several image frames in the TV video. Therefore, the consequence of a significant increase in the TPS data decoding error rate caused by deep fading is severe. 
         [0014]    Let b 0 b 1 b 2 b 3 b 4 b 5  denote the α-set TPS data vector, where b i ε{+1,−1}, i=0, 1, 2, 3, 4, 5. First, b 0 b 1 b 2 b 3 b 4  is mapped to a length-32 Walsh sequence s 0 s 1  . . . s M−1  where M=32 is the length of the Walsh sequence, and s k ε{+1,−1}, k=0, 1, . . . , M−1. The Walsh chips are then scrambled to give: 
         [0000]      s 0 c 0 , s 1 c 1 , . . . , s M−1 c M−1 , 
         [0000]    where c k ε{+1,−1} is the kth chip of the PN sequence specified in the DTV-T standard. The remaining bit b 5  is used to modulate the scrambled Walsh sequence, giving: 
         [0000]      b 5 s 0 c 0 , b 5 s 1 c 1 , . . . , b 5 s M−1 c M−1 . 
         [0000]    The kth chip is transmitted on the ξ(k)th subcarrier where ξ(·) is the interleaving function. Consider that an Orthogonal Frequency Division Multiplexing (OFDM) symbol carrying only α-set TPS data is transmitted. The complex envelope of the transmitted signal, s(t), is given by: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       s 
                        
                       
                         ( 
                         t 
                         ) 
                       
                     
                     = 
                     
                       
                         
                           2 
                            
                           
                               
                           
                            
                           
                             P 
                             s 
                           
                         
                       
                        
                       
                         
                           ∑ 
                           
                             k 
                             = 
                             0 
                           
                           
                             M 
                             - 
                             1 
                           
                         
                          
                         
                           
                             ∑ 
                             
                               n 
                               = 
                               0 
                             
                             
                               N 
                               - 
                               1 
                             
                           
                            
                           
                             
                               b 
                               5 
                             
                              
                             
                               s 
                               k 
                             
                              
                             
                               c 
                               k 
                             
                              
                             
                                
                               
                                 j 
                                  
                                 
                                     
                                 
                                  
                                 2 
                                  
                                 
                                     
                                 
                                  
                                 π 
                                  
                                 
                                     
                                 
                                  
                                 n 
                                  
                                 
                                     
                                 
                                  
                                 
                                   
                                     ξ 
                                      
                                     
                                       ( 
                                       k 
                                       ) 
                                     
                                   
                                   / 
                                   N 
                                 
                               
                             
                              
                             
                               ψ 
                                
                               
                                 ( 
                                 
                                   t 
                                   - 
                                   
                                     nT 
                                     c 
                                   
                                 
                                 ) 
                               
                             
                           
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where N=3780 is the total number of subcarriers used in DTV-T systems, P s  is the per-subcarrier transmitted power, 1/T c  is the chip rate (equal to the sampling rate, i.e., 7.56M samples/s), and ψ(t) is the square-root raised cosine pulse satisfying ∫ −∞   ∞ |ψ(t)| 2 dt=T c . Note that the OFDM symbol duration, T s , is given by T s =NT c . 
         [0015]    The complex envelope of the received signal is given by: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       r 
                        
                       
                         ( 
                         t 
                         ) 
                       
                     
                     = 
                     
                       
                         n 
                          
                         
                           ( 
                           t 
                           ) 
                         
                       
                       + 
                       
                         
                           
                             2 
                              
                             
                               P 
                               s 
                             
                           
                         
                          
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               0 
                             
                             
                               M 
                               - 
                               1 
                             
                           
                            
                           
                             
                               ∑ 
                               
                                 n 
                                 = 
                                 0 
                               
                               
                                 N 
                                 - 
                                 1 
                               
                             
                              
                             
                               
                                 b 
                                 5 
                               
                                
                               
                                 s 
                                 k 
                               
                                
                               
                                 c 
                                 k 
                               
                                
                               
                                 g 
                                 
                                   ξ 
                                    
                                   
                                     ( 
                                     k 
                                     ) 
                                   
                                 
                               
                                
                               
                                  
                                 
                                   j 
                                    
                                   
                                       
                                   
                                    
                                   2 
                                    
                                   
                                       
                                   
                                    
                                   π 
                                    
                                   
                                       
                                   
                                    
                                   n 
                                    
                                   
                                       
                                   
                                    
                                   
                                     
                                       ξ 
                                        
                                       
                                         ( 
                                         k 
                                         ) 
                                       
                                     
                                     / 
                                     N 
                                   
                                 
                               
                                
                               
                                 ψ 
                                  
                                 
                                   ( 
                                   
                                     t 
                                     - 
                                     
                                       nT 
                                       c 
                                     
                                   
                                   ) 
                                 
                               
                             
                           
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where g i  is the complex-valued channel gain of the ith subcarrier, and n(t) is the baseband-equivalent AWGN satisfying ½E{n(t)n*(t+Δt)}=N 0 δ(Δt), with N 0  being the one-sided noise power spectral density. For the present description, it is sufficient to assume that the channel gains are the same for the subcarriers on which TPS data are transmitted, so that g i  is independent of i and G=|g i | 2 . The data transmitted on the ξ(k)th subcarrier is extracted by first performing matched filtering (under the assumption of perfect timing and frequency synchronization) followed by a DFT operation. Let 
         [0000]        R   m =∫ −∞   ∞   r ( t )ψ*( t−mT   c ) dt  for 0 ≦m≦N− 1.  (3) 
         [0000]    Substituting (2) into (3) yields: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       R 
                       m 
                     
                     = 
                     
                       
                         
                           N 
                           ~ 
                         
                         m 
                       
                       + 
                       
                         
                           
                             2 
                              
                             
                               P 
                               s 
                             
                           
                         
                          
                         
                           T 
                           c 
                         
                          
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               0 
                             
                             
                               M 
                               - 
                               1 
                             
                           
                            
                           
                             
                               b 
                               5 
                             
                              
                             
                               s 
                               k 
                             
                              
                             
                               c 
                               k 
                             
                              
                             
                               g 
                               
                                 ξ 
                                  
                                 
                                   ( 
                                   k 
                                   ) 
                                 
                               
                             
                              
                             
                                
                               
                                 j 
                                  
                                 
                                     
                                 
                                  
                                 2 
                                  
                                 
                                     
                                 
                                  
                                 π 
                                  
                                 
                                     
                                 
                                  
                                 m 
                                  
                                 
                                     
                                 
                                  
                                 
                                   
                                     ξ 
                                      
                                     
                                       ( 
                                       k 
                                       ) 
                                     
                                   
                                   / 
                                   N 
                                 
                               
                             
                           
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where Ñ m =∫ −∞   ∞ n(t)ψ*(t−mT c )dt follows a complex Gaussian distribution with zero mean and variance 2N 0 T c . Then compute 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       γ 
                       k 
                     
                     = 
                     
                       
                         
                           ∑ 
                           
                             m 
                             = 
                             0 
                           
                           
                             N 
                             - 
                             1 
                           
                         
                          
                         
                           
                             R 
                             m 
                           
                            
                           
                              
                             
                               
                                 - 
                                 j 
                               
                                
                               
                                   
                               
                                
                               2 
                                
                               
                                   
                               
                                
                               π 
                                
                               
                                   
                               
                                
                               m 
                                
                               
                                   
                               
                                
                               
                                 
                                   ζ 
                                    
                                   
                                     ( 
                                     k 
                                     ) 
                                   
                                 
                                 / 
                                 N 
                               
                             
                           
                            
                           
                               
                           
                            
                           for 
                            
                           
                               
                           
                            
                           k 
                         
                       
                       = 
                       0 
                     
                   
                   , 
                   1 
                   , 
                   … 
                    
                   
                       
                   
                   , 
                   
                     M 
                     - 
                     1. 
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Let E s =P s T s  be the energy of a symbol transmitted on a subcarrier. Substituting (4) into (5), one obtains: 
         [0000]      γ k   =N   k +√{square root over (2 P   s )} T   s   b   5   s   k   c   k   g   ξ(k) ,  (6) 
         [0000]    where N k  is a zero-mean complex Gaussian random variable with variance 2N 0 T s . 
         [0016]    A decoder model used to process γ k , k=0, 1, . . . , M−1, is depicted in  FIG. 2 . The TPS data decoder receives input data  110  and decodes the input data to produce decoded TPS data  120 . The received signal  101  is first processed by a matched filter  103 . The four subcarriers  104  that carry the β-set TPS data are then extracted by performing a DFT operation  105  on the matched filter outputs and applied to first decoder part  210  of a TPS data decoder  200 . The 32 subcarriers  132  that carry the α-set TPS data are also extracted by the DFT operation  105  and applied to a second decoder part  250 . Other data  107  of other subcarriers is routed to another portion (not shown) of the receiver. 
         [0017]    Within the first decoder part  210 , the data of each of the four subcarriers is multiplied in a multiplier  211  by a respective conjugate channel gain factor. Respective real portions of the resulting signals are taken in block  213 , and resulting real values are summed in an adder  215  to obtain a value U. The sign function (block  217 ) is applied to U to obtain the output bit {circumflex over (β)}. 
         [0018]    Within the second decoder part  250 , the data of each of the 32 subcarriers is multiplied in a multiplier  251  by a respective conjugate channel gain factor and multiplied again in a subsequent multiplier  253  by a respective conjugate PN sequence value. The resulting data for each of the respective  32  subcarriers is then processed in a corresponding branch of a circuit  255  having 32 identical branches  255   a - 255   gg , of which only the branch  255   a  will be described in detail. 
         [0019]    In each of the respective branches, a correlation is performed between the subcarrier data and a respective one of the 32 possible Walsh sequences. In the branch  255   a , a correlator  260  is formed by a multiplier  261 , to which Walsh chips of the corresponding Walsh sequence are applied, and an adder  263 . The correlator  260  produces a correlation result S 0 , of which the real portion is taken in block  271  to form a value Y 0 . The absolute value of Y 0  is taken in block  273 . 
         [0020]    In a remaining portion of the second decoder part  250 , block  275  selects the Y value having the largest absolute value and outputs the corresponding 5-bit index as {circumflex over (b)} 0 {circumflex over (b)} 1 {circumflex over (b)} 2 {circumflex over (b)} 3 {circumflex over (b)} 4 . The index is applied to a selector  277 , which selects the corresponding Y value. The sign function (block  279 ) is applied to the selected Y value to obtain the output bit {circumflex over (b)} 5 . 
         [0021]    More particularly, let a 0   (i) a 1   (i)  . . . a M−1   (i) , where a k   (i) ε{+1,−1}, k=0, 1, . . . , M−1, denote the ith sequence from the set of length-M Walsh sequences. Assume that a perfect knowledge of channel estimation is available. Compute the correlation results 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       S 
                       i 
                     
                     = 
                     
                       
                         
                           ∑ 
                           
                             k 
                             = 
                             0 
                           
                           
                             M 
                             - 
                             1 
                           
                         
                          
                         
                           
                             γ 
                             k 
                           
                            
                           
                             g 
                             
                               ξ 
                                
                               
                                 ( 
                                 k 
                                 ) 
                               
                             
                             * 
                           
                            
                           
                             c 
                             k 
                             * 
                           
                            
                           
                             a 
                             k 
                             
                               
                                 ( 
                                 i 
                                 ) 
                               
                               * 
                             
                           
                            
                           
                               
                           
                            
                           for 
                            
                           
                               
                           
                            
                           i 
                         
                       
                       = 
                       0 
                     
                   
                   , 
                   1 
                   , 
                   … 
                    
                   
                       
                   
                   , 
                   
                     M 
                     - 
                     1. 
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Substituting (6) into (7) gives: 
         [0000]    
       
         
           
             
               
                 
                   
                     S 
                     i 
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           0 
                         
                         
                           M 
                           - 
                           1 
                         
                       
                        
                       
                         
                           N 
                           k 
                         
                          
                         
                           g 
                           
                             ξ 
                              
                             
                               ( 
                               k 
                               ) 
                             
                           
                           * 
                         
                          
                         
                           c 
                           k 
                           * 
                         
                          
                         
                           a 
                           k 
                           
                             
                               ( 
                               i 
                               ) 
                             
                             * 
                           
                         
                       
                     
                     + 
                     
                       
                         
                           2 
                            
                           
                               
                           
                            
                           
                             P 
                             s 
                           
                         
                       
                        
                       
                         T 
                         s 
                       
                        
                       
                         b 
                         5 
                       
                        
                       G 
                        
                       
                         
                           ∑ 
                           
                             k 
                             = 
                             0 
                           
                           
                             M 
                             - 
                             1 
                           
                         
                          
                         
                           
                             s 
                             k 
                           
                            
                           
                             
                               a 
                               k 
                               
                                 
                                   ( 
                                   i 
                                   ) 
                                 
                                 * 
                               
                             
                             . 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Note that the noise term in S i  is complex-Gaussian distributed with zero mean and variance 2N 0 T s MG, and that the noise terms of S i  and S i′  are statistically uncorrelated if i≠i′. Denote the estimated b 0 b 1 b 2 b 3 b 4  as î={circumflex over (b)} 0 {circumflex over (b)} 1 {circumflex over (b)} 2 {circumflex over (b)} 3 {circumflex over (b)} 4  and the estimated b 5  as {circumflex over (b)} 5 . Let 
         [0000]        Y   i   =Re ( S   i ), i=0, 1 , . . . , M− 1.  (9) 
       Then 
       [0022]    
       
         
           
             
               
                 
                   
                     
                       i 
                       ^ 
                     
                     = 
                     
                       
                         argmax 
                         
                           i 
                           ∈ 
                           
                             { 
                             
                               0 
                               , 
                               1 
                               , 
                               
                                   
                               
                                
                               … 
                                
                               
                                   
                               
                               , 
                               
                                 M 
                                 - 
                                 1 
                               
                             
                             } 
                           
                         
                       
                        
                       
                          
                         
                           Y 
                           i 
                         
                          
                       
                     
                   
                    
                   
                     
 
                   
                    
                   and 
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       b 
                       ^ 
                     
                     5 
                   
                   = 
                   
                     
                       sgn 
                        
                       
                         ( 
                         
                           Y 
                           
                             i 
                             ^ 
                           
                         
                         ) 
                       
                     
                     . 
                   
                 
               
               
                 
                   ( 
                   11 
                   ) 
                 
               
             
           
         
       
     
         [0023]    Let β be the TPS bit in the β-set, where ββ{+1,−1}. This bit is duplicated four times for transmission. Let M B  be the number of subcarriers for transmitting the P-set data, i.e., M B =4. 
         [0024]    Referring to (6), one can see that the receiver obtains: 
         [0000]      γ k   (β)   =N   k   (β) +√{square root over (2 P   s )} T   s   β·g   ξ(k;β)   , k 0, 1, . . . ,  M   β −1,  (12) 
         [0000]    where γ k   (β)  is the output after the DFT operation, N k   (β)  is a zero-mean complex Gaussian random variable with variance 2N 0 T s , and ξ(k;β) is the frequency-interleaving function indicating the subcarrier number for transmitting the kth data. Note that g i  is the channel gain for the ith subcarrier and, again, G=|g i | 2 . The estimated β, denoted as {circumflex over (β)}, is given by: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       β 
                       ^ 
                     
                     = 
                     
                       sgn 
                        
                       
                         ( 
                         U 
                         ) 
                       
                     
                   
                   , 
                   
                     
 
                   
                    
                   where 
                 
               
               
                 
                   ( 
                   13 
                   ) 
                 
               
             
             
               
                 
                   U 
                   = 
                   
                     
                       ∑ 
                       
                         k 
                         = 
                         0 
                       
                       
                         
                           M 
                           B 
                         
                         - 
                         1 
                       
                     
                      
                     
                       
                         Re 
                          
                         
                           ( 
                           
                             
                               γ 
                               k 
                               
                                 ( 
                                 β 
                                 ) 
                               
                             
                              
                             
                               g 
                               
                                 ξ 
                                  
                                 
                                   ( 
                                   
                                     k 
                                     ; 
                                     β 
                                   
                                   ) 
                                 
                               
                               * 
                             
                           
                           ) 
                         
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
         [0025]    It can be shown that the α-set TPS data decoding error rate is determined by G(E s /N 0 ). It follows that if the receiver has a knowledge of G(E s /N 0 ), the reliability of the decoded α-set TPS data can be determined. Estimating this figure (or a more general signal-to-noise ratio (SNR)) at the receiver is the main task to be performed. 
         [0026]    From (8), it is apparent that 
         [0000]        E ( Y   i   |s   k   ≡a   k   (i)   , k= 0, 1 , . . . , M− 1)=√{square root over (2 P   s )} T   s   b   5   MG   (15) 
         [0000]      and 
         [0000]        var ( Y   i   |s   k   ≠a   k   (i)   , k= 0, 1 , . . . , M− 1)= N   0   T   s   MG,   (16) 
         [0000]    where the expectation and variance are ensemble-averaged values. It follows that 
         [0000]    
       
         
           
             
               
                 
                   
                     G 
                      
                     
                       
                         E 
                         s 
                       
                       
                         N 
                         0 
                       
                     
                   
                   = 
                   
                     
                       1 
                       
                         2 
                          
                         M 
                       
                     
                     · 
                     
                       
                         
                           
                             [ 
                             
                               E 
                                
                               
                                 ( 
                                 
                                   
                                     
                                       Y 
                                       i 
                                     
                                     | 
                                     
                                       
                                         s 
                                         k 
                                       
                                       ≡ 
                                       
                                         a 
                                         k 
                                         
                                           ( 
                                           i 
                                           ) 
                                         
                                       
                                     
                                   
                                   , 
                                   
                                     k 
                                     = 
                                     0 
                                   
                                   , 
                                   1 
                                   , 
                                   … 
                                    
                                   
                                       
                                   
                                   , 
                                   
                                     M 
                                     - 
                                     1 
                                   
                                 
                                 ) 
                               
                             
                             ] 
                           
                           2 
                         
                         
                           var 
                            
                           
                             ( 
                             
                               
                                 
                                   Y 
                                   i 
                                 
                                 | 
                                 
                                   
                                     s 
                                     k 
                                   
                                   ≠ 
                                   
                                     a 
                                     k 
                                     
                                       ( 
                                       i 
                                       ) 
                                     
                                   
                                 
                               
                               , 
                               
                                 k 
                                 = 
                                 0 
                               
                               , 
                               1 
                               , 
                               … 
                                
                               
                                   
                               
                               , 
                               
                                 M 
                                 - 
                                 1 
                               
                             
                             ) 
                           
                         
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   17 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Alternatively, G(E s /N 0 ) can be obtained by observing U. From (12) and (14), it is noticed that 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       E 
                        
                       
                         ( 
                         U 
                         ) 
                       
                     
                     = 
                     
                       
                         
                           2 
                            
                           
                             P 
                             s 
                           
                         
                       
                        
                       
                         T 
                         s 
                       
                        
                       β 
                        
                       
                           
                       
                        
                       
                         GM 
                         β 
                       
                     
                   
                    
                   
                     
 
                   
                    
                   and 
                 
               
               
                 
                   ( 
                   18 
                   ) 
                 
               
             
             
               
                 
                   
                     var 
                      
                     
                       ( 
                       U 
                       ) 
                     
                   
                   = 
                   
                     
                       M 
                       β 
                     
                      
                     
                       GN 
                       0 
                     
                      
                     
                       
                         T 
                         s 
                       
                       . 
                       
                         
 
                       
                        
                       Hence 
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   19 
                   ) 
                 
               
             
             
               
                 
                   
                     G 
                      
                     
                       
                         E 
                         s 
                       
                       
                         N 
                         0 
                       
                     
                   
                   = 
                   
                     
                       1 
                       
                         2 
                          
                         
                           M 
                           β 
                         
                       
                     
                     · 
                     
                       
                         
                           
                             [ 
                             
                               E 
                                
                               
                                 ( 
                                 U 
                                 ) 
                               
                             
                             ] 
                           
                           2 
                         
                         
                           var 
                            
                           
                             ( 
                             U 
                             ) 
                           
                         
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   20 
                   ) 
                 
               
             
           
         
       
     
         [0027]    From (17) and (20), it is apparent that one may estimate G(E s /N 0 ) based on the following 33 intermediate results: Y i , Y i  for i≠i′ (31 values), and U. Furthermore, the estimation can be made more accurately by using more than one set of intermediate results, i.e., by including sets obtained at previous time instants. Let N ob  be the number of sets of intermediate results used to compute the estimated SNR value. That is, N ob −1 sets of previous ones are involved. For convenience, let Y i (n) be the ith real-valued correlation value (Y i ) generated at time n, wherein i=0, 1, . . . , M−1, and n=0, 1, . . . , N ob −1; let î(n) be the index indicating the correlation result having the largest magnitude among all correlation values obtained at time n; and let U(n) be the value of U obtained at time n. Without loss of generality, it is assumed that the present set of intermediate results is obtained at time N ob −1. Based on the N ob  sets of intermediate results, compute 
         [0000]    
       
         
           
             
               
                 
                   
                     Ξ 
                     = 
                     
                       
                         1 
                         
                           2 
                            
                           
                               
                           
                            
                           
                             N 
                             ob 
                           
                         
                       
                        
                       
                         
                           ∑ 
                           
                             n 
                             = 
                             0 
                           
                           
                             
                               N 
                               ob 
                             
                             - 
                             1 
                           
                         
                          
                         
                           [ 
                           
                             
                                
                               
                                 
                                   Y 
                                   
                                     
                                       i 
                                       ^ 
                                     
                                      
                                     
                                       ( 
                                       n 
                                       ) 
                                     
                                   
                                 
                                  
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                                
                             
                             + 
                             
                               
                                 M 
                                 
                                   M 
                                   β 
                                 
                               
                                
                               
                                  
                                 
                                   U 
                                    
                                   
                                     ( 
                                     n 
                                     ) 
                                   
                                 
                                  
                               
                             
                           
                           ] 
                         
                       
                     
                   
                    
                   
                     
 
                   
                    
                   and 
                 
               
               
                 
                   ( 
                   21 
                   ) 
                 
               
             
             
               
                 
                   Ω 
                   = 
                   
                     
                       1 
                       
                         
                           N 
                           ob 
                         
                          
                         
                           ( 
                           
                             M 
                             - 
                             1 
                           
                           ) 
                         
                       
                     
                      
                     
                       
                         ∑ 
                         
                           n 
                           = 
                           0 
                         
                         
                           
                             N 
                             ob 
                           
                           - 
                           1 
                         
                       
                        
                       
                         
                           ∑ 
                           
                             
                               m 
                               = 
                               0 
                             
                             , 
                             
                               m 
                               ≠ 
                               
                                 
                                   i 
                                   ^ 
                                 
                                  
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                             
                           
                           
                             M 
                             - 
                             1 
                           
                         
                          
                         
                           
                             
                               [ 
                               
                                 
                                   Y 
                                   m 
                                 
                                  
                                 
                                   ( 
                                   n 
                                   ) 
                                 
                               
                               ] 
                             
                             2 
                           
                           . 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   22 
                   ) 
                 
               
             
           
         
       
     
         [0000]    In the two expressions, Ξ and Ω are estimates of |E(Y i |s k ≡a k   (i) , k=0, 1, . . . , M−1)| and var(Y i |s k ≠a k   (i) , k=0, 1, . . . , M−1), respectively. The information provided by U is used to help improve the accuracy in the computation of Ξ and, therefore, is not reused again in the computation of Ω in (22). In the derivation of (21), use is made of the relationship {circumflex over (b)} 5 Y î =|Y î |. That is, the removal of the effect of {circumflex over (b)} 5  from Y î  is equivalent to computing the absolute value of Y î . Similarly, the relationship of {circumflex over (β)}U=|U| is employed in deriving (21). The estimated G(E s /N 0 ) value is then computed by: 
         [0000]    
       
         
           
             
               
                 
                   
                     Estimated 
                      
                     
                         
                     
                      
                     G 
                      
                     
                       
                         E 
                         s 
                       
                       
                         N 
                         0 
                       
                     
                   
                   = 
                   
                     
                       1 
                       
                         2 
                          
                         M 
                       
                     
                     · 
                     
                       
                         
                           Ξ 
                           2 
                         
                         Ω 
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   23 
                   ) 
                 
               
             
           
         
       
     
         [0028]      FIG. 3  is a schematic diagram of a reliability detector  300 . This reliability detector comprises an SNR estimator  301 , a comparator  303 , and a possible storage means  305 . The SNR estimator  301  receives the values î, U, and Y 0 , Y 1 , . . . , Y 31  and uses these values  130  to estimate the value of SNR ( 302 ) for the received TPS data. The comparator  303  compares the estimated SNR with a threshold value to produce a reliability indicator  307 . Storage  305  may optionally be provided to store one or more sets of previous intermediate data to be used together with a current set of intermediate data in estimating the SNR. 
         [0029]    More particularly, the SNR estimator estimates the value of SNR for the received TPS data, with N ob  sets of intermediate results (N ob ≧1) obtained at the present time and at N ob −1 earlier time instants, wherein each set comprises: î; Y i , i=0, 1, . . . , M−1; and U. That is, each set of intermediate results comprises: from the α-set TPS data decoder, (a) the 32 real-valued correlation results after correlating the sequence of 32 demodulated data carried in the desired subcarriers with the 32 scrambled Walsh sequences and (b) the index indicating the correlation result that has the largest magnitude among all correlation results; and from the β-set TPS data decoder, the result after summing the contribution from individual subcarriers. 
         [0030]    In the above description, the expression G(E s /N 0 ) is meaningful only if the channel for TPS data is frequency-nonselective. The SNR estimator is intended to work not only for frequency-nonselective channels but also for a general radio channel. In the latter case, the estimated SNR value may be a generic SNR value, not indicating a value specifically targeted to be an estimate of G(E s /N 0 ). 
         [0031]    The output of the SNR estimator is produced at a rate equal to the rate of the incoming sets of intermediate results. The estimation of SNR can be based on the presently obtained set of intermediate results, or include previous ones. If it is desired to compute the estimated SNR value based on one or more sets of previous intermediate results, a storage means is required to store these previous sets. After the estimated SNR is obtained, this value is fed to the comparator to compare with a threshold value. If the estimated SNR exceeds the threshold value, the receiver can consider that the decoded TPS data are reliable; otherwise, they are considered unreliable. 
         [0032]    The operation of the SNR estimator is detailed as follows. Values of Ξ and Ω are computed by (21) and (22), respectively, based on the N ob  sets of intermediate results. Then the SNR is estimated by: 
         [0000]    
       
         
           
             
               
                 
                   
                     Estimated 
                      
                     
                         
                     
                      
                     S 
                      
                     
                         
                     
                      
                     N 
                      
                     
                         
                     
                      
                     R 
                   
                   = 
                   
                     
                       1 
                       
                         2 
                          
                         M 
                       
                     
                     · 
                     
                       
                         
                           Ξ 
                           2 
                         
                         Ω 
                       
                       . 
                     
                   
                 
               
               
                 
                   ( 
                   24 
                   ) 
                 
               
             
           
         
       
     
         [0033]    After the computation of the estimated SNR value, the storage means is updated to store the set of intermediate results obtained at time N ob −1 and discard the set obtained at time 0, in order to enable estimation of the SNR value for the next time instant. 
         [0034]    It is noted that instead of storing Y m (n), n=0, 1, . . . , N ob −2 and m=0, 1, . . . , M−1, in the storage means, the receiver can save some storage space by storing only |Y î(n) (n)| and Σ m=0,m≠î(n)   M−1 [Y m (n)] 2 , n=0, 1, . . . , N ob −2. 
         [0035]    Although embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions and alternations can be made without departing from the spirit and scope of the inventions as defined by the appended claims.