Abstract:
A quantizer has a plurality of decision blocks, each coupled from input to output, where each decision blocks output generates a binary value that is an unchanged decision block input if the decision block input is below the threshold input level divided by a power of 2, or the decision block subtracts a threshold divided by the power of 2 and passes this result as the decision block output. The quantizer output is formed from the bits of each comparison from each decision block. The threshold is developed from a channel noise variance which may be multiplied by a scale factor related to coding type and rate. In this manner, a large number of input bits to be quantized may be converted to a smaller number of quantizer output bits, while preserving the dynamic range information required to correctly decode signals passed through a communications channel having multi-path frequency selective fading.

Description:
FIELD OF THE INVENTION 
   The present invention is directed to the field of noise estimators, particularly as used in wireless communications system to estimate the signal quality during a preamble interval. 
   BACKGROUND OF THE INVENTION 
     FIG. 1  shows a prior art OFDM receiver  10 . A baseband signal  12  enters a synchronization function  14 , which serves to identify phase and frequency offsets in the incoming signal  12 , where they are fed back to an NCO (not shown) or a phase rotator (not shown) which removes the offsets and frequency drifts from the synchronized signal. The phase and frequency corrected signal  15  is delivered to an FFT  16  which recovers the combinations of OFDM subcarriers which comprise the transmitted data. FFT outputs  17  are shown as signal  17   a,  comprising linear combinations of FFT output data having real and imaginary components. The FFT output  17  is provided to a channel estimation and equalization function  18 , which produces output  19  compensated for channel phase and magnitude variations. Plot  19   a  shows the output  19  in a frequency vs real and imaginary amplitude view, and plot  19   b  shows the corresponding constellation diagram for 16-QAM, where each position in a 16 QAM constellation diagram represents 4 bits of data after decoding. The output  19  of the channel compensator  18  is fed to the soft constellation de-mapper  24 , which performs the function of converting the constellation into corresponding data values, and this output  23  is fed to the de-interleaver and soft decoder  20 , which performs data decoding resulting in output data  22 . 
     FIG. 2  shows a preamble stream  25  for an OFDM packet. The packet  25  comprises a sequence of preamble tones P 0  through P 15  which form a first preamble  26  followed by a second identical preamble  28 , which is followed by a third preamble  30 , and finally the packet data  32 . During the preamble times corresponding to preambles  26 ,  28 , and  30  of packet  25 , the synchronization function  14  and channel estimation function  18  of  FIG. 1  make estimations of channel frequency offset, phase offset, and channel frequency transfer function, respectively. 
     FIG. 3  shows one implementation of a prior art packet detection and coarse frequency offset synchronizer such as  14  of  FIG. 1 . The synchronizer comprises two parts, a coarse frequency offset part  40 , and a packet detection part  60 . The frequency offset estimator  40  accepts as an input a stream of complex OFDM symbols  92  and a delayed version  42  of the same stream, where the delay is equal to the interval of a single preamble interval  26 . The conjugator  52  has the function of inverting the imaginary part of the incoming stream such that a+jb becomes a−jb. The product of (a+jb)(a−jb) produces the signal power level a 2 +b 2 , since the same-position preamble symbols are identical other than the frequency offset generated phase shift component from the earlier symbol to the later symbol. Consequently, the multiplier  44  output contains an imaginary component corresponding to the amount of phase shift from a first preamble symbol to a second preamble symbol. The Phase Finder  46 , which is implemented as a CORDIC generates an output  47  which represents the phase φ of the incoming multiplier  44  product. The frequency may be then be estimated from change of phase per sample Δφ/Δt. The output of CORDIC  46  is averaged  48  to generate a coarse frequency offset  50 . This value is measured during the preamble interval and fed back to a numerically controlled oscillator (NCO, not shown) or phase rotator (not shown) to remove any frequency offset during the balance of the packet receive time prior to performing the FFT, where such frequency offset would result in an offset in the FFT  16  of  FIG. 1  outputs. 
   The symbol timing may be extracted from the processing shown as packet detection system  60  of  FIG. 3 . The incoming stream of baseband OFDM symbols are delayed  62  by a time equal to a preamble interval, and the preamble stream  92  is multiplied  66  by a delayed preamble  63  and conjugated  64  to produce multiplier  66  output  67 . This output  67  is averaged over an interval equal to the number of symbols in a preamble (shown as 16 symbols) to generate a value Cn  74 , which represents the power level of the signal, as before. During the preamble interval, the multiplication of a current preamble symbol with the same symbol from a previous preamble results in the output  67  of the multiplier  66  representing the correlated signal power. The averager  70  sums the previous preamble values (shown for a 16 symbol preamble) to generate a power value Cn  74  whose value represents the noise plus interference component of the SINR value to be determined. The output  63  of the delay element  62  is multiplied by a conjugate  64  value  65  to produce a product  69 , which is averaged over the same preamble interval by averager  72  to generate a signal plus noise power level  76 . Since there is very little signal correlation from one symbol of a preamble to the next, the output Pn  76  provides an indication of the uncorrelated noise plus interference level, which includes unrelated noise and interference effects such as preamplifier gain in the RF signal processing chain and reflected signal energy, in contrast to the correlated value Cn  74  indicates the correlated power level of the incoming stream during the preamble interval. Cn  74  and Pn  76  are ordinarily used to establish the symbol timing referenced to the preamble, and one such method is to divide  78  the absolute value of Cn  84  by the noise plus signal level Pn  76  to generate a figure of merit μ  85 , and to associate packet detection  90  with μ  85  crossing some predetermined threshold using a comparator  88 . 
     FIG. 4  shows the signals for the prior art packet detection system of  FIG. 3 . The packet preamble is shown as  120 , while signal power  67  is shown as  122  and noise and interference power signal  69  is shown as  124 . Output Cn  74  is shown as signal  126 , and output Pn  76  is shown as signal  128 , which both rise during second preamble time t 2 , which corresponds to interval  28  of  FIG. 2 . The ratio of Cn/Pn is shown on waveform  127 , and when waveform  127  crosses threshold  125 , start of packet  121  is indicated, while end of preamble/start of data/symbol timing may be detected by falling correlated signal waveform  122  edge  123 . 
   The use of existing signals Cn and Pn is known in the prior art for symbol timing and packet detection, and it also known in the prior art to change demodulation method and transmission speed based on error rate at the detector. It is desired to generate a SINR estimate using these signals for use in demodulation, particularly following the soft constellation demapping step, whereby the quantization method performed on the demapped data may be changed in accordance with the value of SINR as determined during the preamble synchronization step. 
   An estimate of the receiver signal quality can be used to improve the performance or reduce the complexity of base-band processing functions. An estimate of the noise variance is a sufficient measure of the signal quality, as the AGC (Automatic Gain Control) function of the RF receiver (not shown) ensures constant input power to a base-band system. Typically, symbol decisions are compared with the received symbol to obtain an error vector. The error vectors can be averaged to obtain an estimate of the noise variance as discussed in U.S. Pat. No. 5,379,324. The symbol decisions can be made at the input to the decoder, or at the decoder output. Using decisions from the output of the decoder provides a better estimate of the noise variance. Both these techniques have significant latency, and it is useful to have an estimate of signal strength established during the preamble interval so that it may be used during the data interval of the same packet. It is desired to have a signal strength estimation for use in an OFDM system which relies on parameters which can be established during the preamble interval. 
   A technique for synchronization based on a training sequence consisting of repeating patterns is described in “Robust Frequency and Timing Synchronization for OFDM”, IEEE Transactions on communications, December 1997. As noted in  FIG. 3  and  FIG. 4 , due to the repeating preamble symbols, a correlation peak is observed at the end of the training sequence. This peak is used to detect a valid reception. The position of the peak also indicates the symbol boundary. 
   The correlation be represented as, 
             C   ⁡     (   n   )       =       ∑       n   -   L     &lt;   k   ≤   n       ⁢       X   ⁡     (   k   )       *       X   ⁡     (     k   -   L     )       *               
The signal energy is computed as,
 
   
     
       
         
           
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                     X 
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                       ( 
                       k 
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   The normalized value used for symbol timing is given by 
   
     
       
         
           
             Y 
             ⁡ 
             
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               n 
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   In an OFDM system, the soft metric values can be weighed by the corresponding channel estimates resulting in a significant improvement in receiver performance. A simple technique to generate soft metrics with channel weighting is discussed in “Simplified Soft-Output Demapper for Binary Interleaved COFDM with application to Hiperlan/2” by Tosato et al. 
   In a frequency selective fading environment, where OFDM is typically employed, the frequency domain channel estimates have large peaks and nulls and therefore a large dynamic range as shown in  148   FIG. 7 . This requires a large soft metric bit-width to accurately represent the reliability information. A large soft-metric bit-width results in a significant increase in area as bit-widths in De-Interleaver and Soft decoder blocks increase. A non-uniform quantization technique could be used but it leads to increased complexity in the soft decoders. 
   A technique for soft metric quantization is discussed in U.S. Pat. No. 5,379,324. This technique uses statistical information from the output of the soft metric quantization to adjust the quantization threshold. This is however an iterative process with inherent latency. The proposed technique provides a simpler technique to determine the quantization threshold for OFDM systems without any latency. 
   U.S. Pat. No. 5,214,675 by Mueller et al. describes a system for compensating for multi-path reflection in a communications system by computing a variance of the signal and providing this signal to a filter which compensates for multipath delay. 
   U.S. Pat. No. 6,792,055 by Hart describes a system for use in QAM whereby the strength of the demodulated signal is fed back to a gain control. In another embodiment, the decoder makes hard and soft decisions according to a variable threshold which is set by the strength of the signal applied to the decoder. 
   U.S. Pat. No. 5,740,203 describes a prior art demapper for QAM and PSK modulation methods which performs the function of block  24  of  FIG. 1  or block  140  of  FIG. 6 . 
   U.S. Pat. No. 5,379,324 by Mueller et al describes a system for computing gain and noise variance of a channel for use in correcting the channel. 
   OBJECTS OF THE INVENTION 
   A first object of the invention is to generate an estimate of SINR using signals from a prior art symbol detection function. 
   A second object of the invention is to generate an estimate of SINR from a preamble symbol stream. 
   A third object of the invention is to generate an estimate of SINR from a preamble stream, a delayed preample stream, a conjugator, and two multipliers. 
   A fourth object of the invention is to generate a threshold value from an SINR value, and use the threshold value to quantize demapped values. 
   SUMMARY OF THE INVENTION 
   An estimate of noise is given by V(n)=(E(n)−C(n))/L, measured when the ratio of En 2 /Cn 2  is at a maximum during a packet interval, where E(n) is derived from the incoming symbol stream  92  X k  where a delay element  62  with a delay interval L equal to a preamble interval generates a delayed incoming stream which is multiplies the incoming symbol stream  92  X k  with the delayed and conjugated copy  65  of the symbol stream to generate a first multiplier  66  output  67  X(k)*X(k−L)*. A second multiplier  68  generates an output  69  X(k)*X(k)* from the product of the delayed symbol stream  63  multiplied by a conjugated copy of the delayed symbol stream  65 . The complex outputs of the first multiplier  66  are summed over a preamble interval L in accumulator  70 , the output of which is fed to phase finder  152  which generates magnitude  156 . The magnitude  156  is scaled by the accumulator interval L by scaler  512 , and this result is subtracted from the second multiplier  68  output  69  summed  72  over two preamble intervals (2L) and scaled by 2L, thereby generating an estimate of noise level  162 . Qualifier  166  generates signal  168 , which indicates when noise estimate  162  is valid, which is optionally at the time when the ratio of En 2 /Cn 2  is at a maximum. 
   As discussed earlier, the frequency-offset estimate is computed from the angle of the correlation output ∠C(n) at the peak of Y(n). 
   The noise estimator is coupled to a constellation soft de-mapper having an adjustable level quantizer. The ability to vary the quantizer threshold according to signal level reduces the complexity of the Viterbi decoder which follows the quantizer. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  shows the block diagram for a prior art 802.11 OFDM receiver. 
       FIG. 2  shows a stream of preamble symbols. 
       FIG. 3  shows the block diagram for a prior art packet detection and frequency offset subsystem. 
       FIG. 4  shows the waveforms of the prior art system of  FIG. 3 . 
       FIG. 5   a  shows the block diagram for an OFDM noise estimator. 
       FIGS. 5   b  and  5   c  show the accumulators of  FIG. 5   a.    
       FIG. 6  shows the block diagram for an OFDM receiver with SINR enhancement. 
       FIG. 7  shows a channel profile and noise spectrum plot. 
       FIG. 8  shows the block diagram for a quantizer using SINR. 
       FIG. 9  shows the block diagram of a 4 bit quantizer. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 5   a  shows a block diagram for the present noise estimator  150 , which may be used as the synchronization function such as  14  of  FIG. 1 . The synchronization technique shown in  FIG. 5   a  yields a signal quality estimate in the form of noise measurement  162  in conjunction with qualifier signal  168 . The reference numbers of  FIG. 5   a  perform similar functions as shown in  FIG. 3 . As was described earlier, incoming preamble stream  92  is conjugated  64 ′ to generate a conjugated output  65 ′ which is multiplied by second multiplier  68  to second multiplier output  69 , which is summed  72  over two preamble intervals 2L and scaled by 2L  514  to form real valued En  76 . The input signal  92  is simultaneously delayed  62  by preamble length L and multiplied by conjugated  64  multiplied by the input stream  92  using first multiplier  66  to generate output  67  as before, which is accumulated over a preamble interval L in accumulator  70 , the output of which is fed to the phase finder CORDIC  152  which provides a phase output  154  as well as a magnitude output  156 , which is scaled by preamble interval L to produce Cn  160 . An estimate of the noise level  162  can be obtained by subtracting the magnitude of the correlation output |C(n)|  160  from the signal energy E(n)  76  when Y(n) reaches its peak value. In the prior art of  FIG. 3 , deriving the magnitude of C(n) from C(n) 2    84  would require a square root computation. However, in  FIG. 5 , the CORDIC processor  152  that is used to compute the angle  154  of C(n) for the frequency offset estimation of  FIG. 3  can also compute the magnitude of C(n)  156 . The CORDIC processor  152 , as known in the prior art, accepts a real and imaginary component as input  67  (a+jb), and generates a phase output  154  corresponding to tan −1  (b/a) and a magnitude output corresponding to √{square root over (a 2 +b 2 )}. In this manner, a noise estimate V(n)  162  for the incoming signal  92  can be generated by subtracting the correlated signal level  160  from the noise plus interference output  76 . Qualifier  166  generates signal  168 , which indicates when noise estimate  162  is valid, which is optionally at the time when the ratio of En 2 /Cn 2  is at a maximum. An AGC function in the RF processing is performed prior to processor  130 , and results in increasing the receiver gain when the incoming signal level is weak, and decreasing the receiver gain when the incoming signal level is strong, thereby optimizing the use of the digitization dynamic range. The AGC function generates a relatively constant En level  76 , which results in an improved noise estimate  162 . It is also possible to scale the noise estimate  162  by the length of the preamble, shown as L=16 for a short preamble, and L=32 for a long preamble. Generally, a longer sample size produces more accurate estimates of noise variance. In this manner, an improved estimator for noise level for use in a communications receiver is described. 
     FIG. 5   b  shows an embodiment for accumulator  70 , which takes an input  67  and sums  504  the input  67  with a one-sample delayed output  506  and subtracts out an L delayed  502  version of the input  67 . In this manner, the output of the first accumulator represents the sum of the previous L samples. 
     FIG. 5   c  similarly shows an embodiment for the accumulator  72 , which receives an input  69  and sums  512  the input  69  with a one-sample delayed output  510  and subtracts out a 2L delayed  508  version of the input  69 . In this manner, the output of the second accumulator represents the sum of the previous 2L samples. 
   A technique is proposed in the present invention that modifies the quantization threshold level T depending on the average noise level n. Any soft metric values greater than the threshold are clipped and uniform quantization is applied within the threshold. Therefore, sub-carriers with a Signal To Noise ratio (SNR) greater than 
             T   2       n   2           
are clipped. Since sub-carriers with large SNR are more reliable, loss of information resulting from clipping for these sub-carriers does not significantly affect the receiver performance. Therefore, the quantization range is limited to sub-carriers with lower SNR, resulting in a lower quantization bit-width. Examining  FIG. 7 , an upper level  148  defined by the noise level  149  forms the boundary limit for such a quantization threshold, and this quantization may be set to a smaller step size when the noise level is low compared to the signal, and the quantization step size may be increased when the noise level is high compared to the signal.
 
   There are two signal conditions and two SNR conditions to consider. In the absence of signal reflections, the frequency fading of the communications channel is flat in frequency, which is to say that all of the subcarriers have the same SNR. Typically in this condition, 4 bits of quantization are sufficient to represent the soft values at low SNR, and at high SNR 1 bit of quantization is sufficient. The utility of the present invention is realized in the case of multipath reflections with frequency selective fading, where all of the subcarrier levels have to be accommodated to maintain sufficient sampling of the lower amplitude carriers, which would require the generation of 12 bit soft values in the prior art. This increased complexity would be carried through to the Viterbi decoder, as described below. In the present invention operating on signals in the presence of multipath reflection with frequency selective fading, a suitable quantization threshold can be found such that 4 bit soft values can be generated and sent for subsequent processing, for example, by a Viterbi decoder. 
   The advantage of using a signal strength dependent quantizer prior to the decoder is reduced system complexity. When the SNR is high, the Viterbi decoder  144  of  FIG. 6  may operate satisfactorily on 4 bits of data, and may continue to operate satisfactorily as the SNR decreases, as long as the channel is flat. In such a case the performance of the receiver is dominated by AWGN. Multi-path reflections in the channel generally increase the number of data bits required for the same level of performance, up to 12 bits as may be used in a decoder operating in an environment with multi-path reflections. In such a case the performance of the receiver is also affected by the frequency selective variations in channel envelope. The use of the quantizer  142  of the present invention as shown in  FIG. 8  in conjunction with a threshold generator operating on data from the synchronizer  134  allows the incoming data to be reduced to a smaller number of data bits, thereby saving power and reducing decoder complexity in the decoder  144 . 
     FIG. 6  shows a modified OFDM receiver with adaptive soft metric quantization. The signal quality estimates  162  derived from the synchronization process are fed to the quantizer  142  after soft-demapping  140 , where the quantizer  142  decision threshold varies based on the signal quality estimate  162 , and other relevant parameters such as modulation order, code rate or signal variance. Comparing to  FIG. 3 , the output of FFT  137  shown as  137   a  contains a plurality of k subcarriers, each having a different magnitude and phase. The phase is channel equalized by equalizer  138  to generate equalized frequency domain output  139 , which may be viewed as a constellation  139   b  with tightly formed real and imaginary responses (shown as small diameter circles) for high SINR and loosely formed responses (shown as larger diameter circles) as shown in  figure 139   c  for poor signal to noise environments. The subcarriers are phase equalized but not amplitude equalized. Therefore the envelope of the signal constellation varies across subcarriers but the noise variance is the same. Hence subcarriers with large SNR will have a larger constellation envelope. This could be shown with  139   b  having a larger envelope as compare to  139   c  with circles of the same diameter as  139   c.    
   It is desired to use the preamble generated noise estimate  162  to change the behavior of quantizer  142 , in particular to simplify the quantization process for high signal to noise environments, and introduce more complex quantization for low signal to noise environments, where the signal to noise is determined during the preamble interval for the same received packet. The channel equalization block  138  includes provision for equalizing each of the FFT outputs as follows:
         Y k  is the symbol at the kth subcarrier.   H k  is the channel coefficient of the kth subcarrier.   X k  be the phase equalized value
 
Then,
 
 X   k   =Y   k *    H   k   
       

   Following the channel equalization  138 , the phase-equalized values are used to generate the reliability metrics known as LLRs (Log Likelihood Ratio) using any available technique, including the one described in U.S. Pat. No. 5,379,324. 
   |H k | 2  is the squared magnitude of the channel coefficient. 
   The LLR metrics L k  for BPSK are given by,
 
 L   k   =Re ( X   k )
 
   The LLR metrics L k  for QPSK are given by,
 
 L   k,1   =Re ( X   k )
 
 L   k,2   =Im ( X   k )
 
   The LLR metrics L k  for 16 QAM are given by,
 
 L   k,1   =Re ( X   k )
 
 L   k,2   =Im ( X   k )
 
 L   k,3   =−|Re ( Xk )|+2*| H   k | 2 
 
 L   k,4   =−|Im ( Xk )|+2*| H   k | 2 .
 
   The LLR metrics L k  for 64 QAM are given by,
 
 L   k,1   =Re ( X   k )
 
 L   k,2   =Im ( X   k )
 
 L   k,3   =−|Re ( Xk )|+4*| H   k | 2 
 
 L   k,4   =−|Im ( Xk )|+4*| H   k | 2 
 
 L   k,5   =−|Re ( Xk )−4*| H   k | 2 |−2*| H   k | 2 
 
 L   k,6   =−|Im ( Xk )−4*| H   k | 2 |−2*| H   k | 2 
 
   Once the above metrics have been computed, the quantization threshold  212  of  FIG. 8  is determined based on the variance, modulation and coding rate. Generally, the quantization threshold  212  increases with modulation order, code rate, and signal variance. Additionally, the quantization threshold  212  is scaled by modulation type and rate, as shown in the table below: 
   
     
       
             
             
             
             
             
             
             
             
             
           
         
             
                 
             
           
           
             
               Modulation 
               64 QAM 
               64 QAM 
               16 QAM 
               16 QAM 
               QPSK 
               QPSK 
               BPSK 
               BPSK 
             
             
               &amp; Rate 
               ¾ 
               ½ 
               ¾ 
               ½ 
               ¾ 
               ½ 
               ¾ 
               ½ 
             
             
               SCALE 
               18.88 
               15 
               8.93 
               5.97 
               3.99 
               3.17 
               2.83 
               2.52 
             
             
                 
             
           
        
       
     
   
   The above values are considered best mode for setting the threshold value. As it is possible to practice the present invention using scale factors which are outside the best mode set forth above, the above scale factors may be considered median values which may vary +100% to −50% from the best mode median values of the above table. 
   As is shown in  FIG. 8 , once the quantization threshold  212  is computed from the above metrics, it is passed to the quantizer  200 , which comprises a plurality of decision blocks  216   a,b, . . . n.  Each decision block operates on the quantization threshold  212  in succession. The quantizer  200  receives an multi-valued input  202  from a soft demapper such as  140  of  FIG. 6 , and each decision block  216   a  compares the magnitude of the multi-valued input  202  with threshold  212 . If input  202  exceeds the threshold  212 , the comparator  218  generates a 1 output for that particular bit  214   a,  or if the values is below the threshold  212   a,  the output bit  214   a  is 0, and the threshold  212  is subtracted  226 , and multiplexer  228  passes the remainder to the next stage  216   b,  which operates analogously on the remainder, applying the next bit position of the threshold  212   b.  In this manner, a quantizer output  214  is generated with one comparator  218  bit from each decision block  216   a,    216   b,  . . . ,  216   n.    
     FIG. 9  shows a best mode 4 bit quantizer according to the present invention where the MSB is generated by the sign bit of the quantizer input  212 , and the magnitude is passed on to the successive decision blocks  216   a,    216   b,    216   c.    
   As is clear to one skilled in the art, the above examples are shown for clarity and explanation of operation, and are not intended to limit the invention to the specific embodiments described herein.