Abstract:
Optimal decision metric approximation in bit-soft decisions. The present invention provides for calculation of the decision metrics/branch metrics for determining whether an incoming analog signal should be transformed into a 1 or a 0 in the digital realm. In performing these decisions, there is some probability associated with the decision to map the incoming signal to a value of 1 or 0. These decisions made in extracting bits from a particular symbol are typically referred to as bit-soft decisions. In making these bit-soft decisions, decoders commonly use decision metrics/branch metrics as mentioned above. Whereas prior art approaches typically are very computationally intensive to calculate these values, the present invention provides for a much improved and simplified calculation of decision metrics/branch metrics that may be used in bit-soft decisions. The optimal metric approximation may be implemented using a few mathematical operations and simple comparison logic circuitry.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     1. U.S. Utility patent application Ser. No. 10/112,128, entitled “FREQUENCY DRIFT AND PHASE ERROR COMPENSATION IN A VOFDM RECEIVER,” filed Mar. 30, 2002, pending. 
     2. U.S. Utility patent application Ser. No. 10/112,009, entitled “MODIFIED BRANCH METRICS FOR PROCESSING SOFT DECISIONS TO ACCOUNT FOR PHASE NOISE IMPACT ON CLUSTER VARIANCE,” filed Mar. 30, 2002, pending. 
     3. U.S. Utility patent application Ser. No. 10/112,567, entitled “CHARACTERIZING CHANNEL RESPONSE IN A SINGLE UPSTREAM BURST USING REDUNDANT INFORMATION FROM TRAINING TONES,” filed Mar. 30, 2002, pending. 
     4. U.S. Utility patent application Ser. No. 10/114,023, entitled “VOFDM RECEIVER CORRELATION MATRIX PROCESSING USING FACTORIZATION,” filed Mar. 30, 2002, now U.S. Pat. No. 6,947,715, issued on Sep. 20, 2005. 
     BACKGROUND 
     1. Technical Field 
     The invention relates generally to communication systems; and, more particularly, it relates to system and method that are operable to perform an efficient approximately of decision metrics used in bit-soft decisions. 
     2. Related Art 
     Communication systems transmit digital data through imperfect communication channels. These symbols may undergo some undesirable corruption due to the imperfection of the communication channel. One effort to try to avoid such situations is focused on performing forward error correction (FEC) coding. However, there is typically some difficulty in extracting the information contained within these symbols after they have been undesirably altered within the communication channel. There exist some methods that seek to curb the effect that the communication channel has had on the data; one such method includes employing using Decision Feedback Equalizers (DFEs). However, even after the incoming signal has been equalized, the extraction of the data, that has undergone some alteration due to the channel effects, is still a probabilistic determination. 
     Many communication systems, particularly in a receiver, need to perform the analog to digital transformation of an incoming signal. In doing so, there is oftentimes an uncertainty in whether a sample of the incoming analog signal is properly transformed into a 1 or a 0 in the digital realm; for example, there is not a 100% certainty that an incoming signal is actually a 1 or actually a 0—there is some probability associated with the decision. In higher-level encoding/decoding systems, e.g., QPSK/4 QAM, 16 QAM, 64 QAM, 256 QAM, 1024 QAM etc., there are several bits per symbol that need to be transformed to either a 1 or a 0. In 16 QAM applications, a receiver extracts 4 bits of data from each symbol. In the QAM modulation scheme, each symbol includes an in-phase component and a quadrature component. For the 16 QAM modulation type, the decision block maps the in-phase and quadrature components contained in the symbol to a 16 QAM constellation and decides the values of the four bits that are carried by the symbol. 
     The decisions made in extracting bits from a particular symbol are referred to as “bit-soft decisions.” Bit-soft decisions not only map the symbol into bits but also produce a decision metric/branch metric related to the probability that the decision was correct based upon how well the symbol maps into the constellation. The terminology of “branch metric” is often used interchangeably with “decision metric,” and this convention will be followed in this document. The decoder operates based on the premise that there are only a finite number of possible states of the encoder and that given a certain number of consecutive states, the input bit may be predicted that would have caused a particular state transition. The decoder generates a “branch metric” (or “decision metric”) for each of the possible “state transitions” from one state to another. The coding method maintains a “decision metric” associated with every state which is the sum of the metric at its predecessor state and the metric associated with the branch that caused the state transition. This metric may be termed the cumulative metric, accumulated metric, or path metric, and the decoder generates the cumulative metric for all of the states. The different states and the transition from one state to another can be represented in a diagram, namely, a trellis diagram. For various possible allowable state transition sequences through the trellis (the allowable paths through the trellis), the decision metric associated with the sequence of branches of the trellis diagram are summed together, and the smallest sum is selected as the actual state transition and enables the identification of the best estimate of the decoded data. It is noted that with a sign change on each decision metric, the largest sum identifies the best path. 
     Prior art approaches to calculate decision metrics/branch metrics used in decoding systems typically are either very computationally intensive or deviate noticeably from the optimal metric. The computational intensiveness of the prior art approaches prohibits their implementation into many applications, particularly those that have relatively tight real estate and power consumption budgets. Again, these branch metrics are used to determine whether the mapping of a sample should be to that of a value of 1 or a value of 0. Many higher order coding schemes employ mapping of symbols into a constellation. In such situations, the various bits contained in the symbol, e.g., least significant bit (LSB), . . . most significant bit (MSB), etc. may be considered separately. For example, in the 16 QAM scheme, the in-phase component carries two bits while the quadrature phase component carries two bits. By considering separately each of the bits of the in-phase and quadrature components, the decision metrics may be employed in deciding whether the particular bit is a 1 or a 0, in making these bit-soft decisions. 
     One prior art approach is to employ the maximum a priori (MAP) approach, in which a probability of the source symbol is computed, based on information relating to the received, distorted symbol sequence. The bit-soft decision output of the MAP approach is termed a log-likelihood ratio (LLR) that is the logarithmic ratio of the probability of receiving a 1 divided by the probability of receiving a 0; these probabilities of determining the receipt of either a 1 or a 0 are often performed using Bayes Rule to the problem to determine the probability to reach a certain encoder state after receipt of a certain number of symbols and the probability to get from one encoder state to another with the received symbol sequence. 
     This is where the branch metrics come in; the branch metrics may be viewed as being the probabilities of the transitions from one encoder state to another. The branch metrics are a function of the received symbols and the model of the channel over which the symbols have been communicated. To illustrate the complexity of the prior art approach to calculating the decision metrics/branch metrics (that often involves the calculation of the LLR), a 16 QAM example is illustrated as shown below. One rail, containing 2 bits and 4 levels, is examined. The received voltage on the rail is Vrec. The 4 possible transmit levels are: constp a , constp b , constp c , and constp d . In the following equations, the levels constpt a  and constpt b  correspond to an MSB value of 0, and the variables constpt c  and constpt d  correspond to an MSB value of 1. To begin with, the log-likelihood function of a simple constellation point is as follows:
 
−ln(e −(Vrec−constpt     a     )     2     /2σ     2   )
 
     Then, the log-likelihood function for MSB=0 is as follows:
 
−ln(e −(Vrec−constpt     a     )     2     /2σ     2   +e −(Vrec−constpt     b     )     2     /2σ     2   )
 
     A prior art method of calculating the log likelihood ratio of an optimal bit-soft decision (ODM) for the MSB is as shown below:
 
 ODM ={−ln( e   −(Vrec−constpt     a     )     2     /2σ     2     +e   −(Vrec−constpt     b     )     2     /2σ     2   )}−{−ln( e   −(Vrec−constpt     c     )     2     /2σ     2     +e   −(Vrec−constpt     d     )     2     /2σ     2   )}
 
     The ODM shown above, which is the LLR of the MSB bit-soft decision, is the optimal decision metric for 16 QAM, one rail, for the most significant bit (MSB). Clearly, the LLR may be taken as a logarithm of a ratio or a difference of logarithms; the difference is shown above. The implementation of such non-linear equations can be very complex. To calculate these decision metrics accurately using the above-described methods is typically very difficult (theoretically) and is even more difficult to apply. Therefore, these decision metrics are simply not employed in most communication systems. 
     Further limitations and disadvantages of conventional and traditional systems will become apparent to one of skill in the art through comparison of such systems with the invention as set forth in the remainder of the present application with reference to the drawings. 
     SUMMARY OF THE INVENTION 
     Various aspects of the invention can be found in a receiver that performs approximation of optimal decision metrics/branch metrics (DMs/BMs) that are employed in making bit-soft decisions. Any decoding applications that seek to calculate these metrics may benefit from the present invention. Whereas prior art approaches have sought to implement very complex calculations that are typically very computationally intensive, the present invention is able to provide very accurate approximations using a greatly simplified approach. This new approach may be implemented using a very modest amount of mathematical and comparison logic circuitry. The savings in the receiver, in terms of real estate and power consumption, in addition to the increased performance provided by the very close approximation provided by the present invention, provide improvement over the prior art approaches. When it is considered that many prior art approaches do not even implement the computationally intensive equations of the log likelihood function of an optimal bit-soft decision (ODM), but rather utilize a greatly different bit soft decision than the LLR, the present invention can provide significant improvement in performance. 
     The present invention offers a solution that is also scalable to accommodate coding schemes of varying levels, including QPSK/4 QAM, 16 QAM, 64 QAM, 256 QAM and 1024 QAM, among others. The present invention may be implemented within a wireless communication system that employs the vector orthogonal frequency division multiplexing (VOFDM) portion of the broadband wireless Internet forum (BWIF) standard set. The optimal decision metric/branch metric approximation may be implemented within a wireless modem (WM) receiver within the wireless communication system architecture. 
     The present invention, in performing the approximation of the prior art&#39;s very computationally intensive equations used to generate branch metrics, the may be viewed as partitioning the in phase (I) and quadrature (Q) rails of the square symmetric constellation into various regions, each of which is approximated using linear equations. Across the entirety of the constellation, the various regions are approximated with individual segments that corporately make up a piecewise linear curve that spans the various axes of the constellation. For example, depending on the received voltage for a symbol and depending on the particular bit within the symbol (e.g., MSB, . . . 2SB, LSB), the mapping of the bit will be performed differently using the appropiate segment of the piecewise liner curve. The present invention provides a more efficient method of determining these bit-soft decisions compared to the prior art, in that, an optimal decision metric may be approximated using the appropriate segment of the piecewise linear curve for the various bits. 
     The above-referenced description of the summary of the invention captures some, but not all, of the various aspects of the present invention. The claims are directed to some other of the various other embodiments of the subject matter towards which the present invention is directed. In addition, other aspects, advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A better understanding of the invention can be obtained when the following detailed description of various exemplary embodiments is considered in conjunction with the following drawings. 
         FIG. 1  is a system diagram illustrating an embodiment of a communication system employing optimal decision metric approximation according to the present invention. 
         FIG. 2  is a diagram illustrating an example embodiment of a decision metric according to the present invention for QPSK. 
         FIG. 3  is a diagram illustrating an example embodiment of decision metric approximation according to the present invention for the MSB of 16 QAM. 
         FIG. 4  is a diagram illustrating an example embodiment of decision metric approximation according to the present invention for the LSB of 16 QAM. 
         FIG. 5  is a diagram illustrating an example embodiment of decision metric approximation according to the present invention for the MSB of 64 QAM. 
         FIG. 6  is a diagram illustrating an example embodiment of decision metric approximation according to the present invention for the 2 SB of 64 QAM. 
         FIG. 7  is a diagram illustrating an example embodiment of decision metric approximation according to the present invention for the LSB of 64 QAM. 
         FIG. 8  is a system diagram illustrating an embodiment of an optimal decision metric approximation system that is built according to the present invention. 
         FIG. 9  is a system diagram illustrating an embodiment of an improved wireless communication system that is built according to the present invention. 
         FIG. 10  is a functional block diagram illustrating an embodiment of an improved wireless communication method that is performed according to the present invention. 
         FIG. 11  is a functional block diagram illustrating an embodiment of an optimal decision metric approximation method that is performed according to the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention is operable to perform an approximation of the complicated decision metrics/branch metrics that may be used to perform bit-soft decisions when decoding a received data stream. The prior art complicated optimal decision metrics are tightly approximated via linear solutions. The linear solutions employed in the optimal decision metric approximations closely track the more complicated optimal decision metrics without the significant complexities associated within the prior art implementations of calculating the decision metrics. 
     From certain perspectives, differing slopes are applied as scalar multiples to differences generated from the in-phase (I) and quadrature (Q) components of the incoming symbols. Depending upon the value of the I and Q components with symbol, and depending upon which of the bits (LSB, . . . , and MSB) within the symbol that the decision metric is being computed, these differences will be multiplied by differing scalar values. By applying these varying scalar gains to the raw values of the components of the incoming symbol, the approximated optimal solution to the decision metrics are closely approximated without the complexities associated with the prior art methods of calculating the decision metrics. In one embodiment, only addition or subtraction and one multiplication are needed to generate the tight approximation to the decision metrics (not counting operations that may be required for combining the multiple signals received from multiple receive antenna inputs). In one embodiment of the invention, implementation of the varying scalar factor, together with the multiplication operation, can be replaced with a few addition operations and some simple comparison logic. The present invention may be implemented using significantly reduced logic circuitry when compared to prior art methods of implementation for generating branch metrics. 
       FIG. 1  is a system diagram illustrating an embodiment of a communication system  100  employing optimal decision metric approximation according to the present invention. From one level, the present invention is operable within any communication system that needs to perform decision metrics/branch metrics in performing bit-soft decisions. In the  FIG. 1 , an input signal is provided to encoder circuitry  110  for transforming it into a form for communication via a communication channel  120  to a decoder circuitry  130 . It is noted that the encoder circuitry  110  and the decoder circuitry  130  may include outer code encoders, inner code encoders, interleavers and inner code decoders, outer code decoders, de-interleavers, respectively. 
     The encoder circuitry  110  performs the encoding of symbols using multiple bits (multi-bit/symbol encoding  112 ) and some type of forward error correction (FEC) encoding  114 . The decoder circuitry  130  performs optimal decision metric/branch metric approximation for use in making its bit-soft decisions, as shown in a block  132 . The output signal, provided by the decoder circuitry  130 , represents the best estimate of the input signal that is encoded by the encoder circuitry  110 . The present invention is adaptable to provide for a vastly improved system and method for approximated decision metrics/branch metrics for use in bit-soft decisions within any communication system that employs multi-bit per symbol encoding and some form of forward error correction (FEC). In one implementation, the present invention is able to scale to square-symmetric QAM coding schemes of virtually any size, e.g., QPSK/4 QAM, 16 QAM, 64 QAM, 256 QAM, and 1024 QAM etc. Those persons having skill in the art will appreciate that the principles of efficiently approximating the decision metrics/branch metrics for use in bit-soft decisions may be extended to other types of coding schemes beyond those square-symmetric coding schemes. 
     The computationally intensive method of performing the calculations for the LLR, that is used to approximate the optimal decision metric, for performing bit-soft decisions may be simplified in accordance with the present invention. The exponents of the prior art equations may be multiplied by the metric of 2σ 2 ; this key scaling makes the metric independent of the signal to noise ratio (SNR). For application to time-varying SNR channels, simple multiplicative scaling as a final metric produced via the approximation technique of the present invention will “factor back in” the SNR. 
     Then, for the 16 QAM embodiment, by dividing by the factor of 4, the metric may be made to agree with a distance metric. It is noted that the metric may be normalized without departing from the scope and spirit of the invention as well. The present invention provides for much more simplistic and improved calculations that enable a system to provide for optimal approximation to the decision metrics/branch metrics. As the decoder receives a voltage from across the communication channel, the received voltage is modified/approximated to enable proper decoding and signal processing of the received signal. Again, these metrics are scaled to match the distance metric close to the origin. 
     For the 16 QAM embodiment with Gray mapping with bits onto the symbols, the simplified equations, that may be implemented very straightforwardly using small amounts of mathematical and comparison logic, for approximating the branch metrics/decision metrics are shown below for both the most significant bit (MSB) and the least significant bit (LSB); the metrics are the same for both the in-phase (I) and quadrature (Q) components of the incoming symbols: 
     
       
         
               
               
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 MSB metric 
                 = 
                 V rec  + (V rec  + 2) 
                 for V rec : [−∞, −2) 
               
               
                   
                   
                 = 
                 V rec   
                 for V rec : [−2, 2) 
               
               
                   
                   
                 = 
                 V rec  + (V rec  − 2) 
                 for V rec : [2, ∞] 
               
               
                   
                 LSB metric 
                 = 
                 2 − |V rec | 
                 for V rec : [−∞, ∞] 
               
               
                   
                   
               
             
          
         
       
     
     The present invention is also adaptable to the 64 QAM embodiment (another square symmetric constellation). These simplified equations may also be implemented very straightforwardly using small amounts of mathematical and comparison logic, for approximating the branch metrics/decision metrics. The decision metric of this invention as applied to Gray mapped 64 QAM is shown below for both the most significant bit (MSB), the 2 nd  most significant bit (2 SB), and the least significant bit (LSB); again, the metrics are the same for both the in-phase (I) and quadrature (Q) components of the incoming symbols: 
     
       
         
               
               
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                 MSB metric 
                 = 
                 −(4 · |V rec | − 12) 
                 for V rec : [−∞, −6) 
               
               
                   
                   
                 = 
                 −(3 · |V rec | − 6) 
                 for V rec : [−6,−4) 
               
               
                   
                   
                 = 
                 −(2 · |V rec | − 2) 
                 for V rec : [−4,−2) 
               
               
                   
                   
                 = 
                 −|V rec | 
                 for V rec : [−2, 0) 
               
               
                   
                   
                 = 
                 V rec   
                 for V rec : [0, 2) 
               
               
                   
                   
                 = 
                 2 · V rec  − 2 
                 for V rec : [2, 4) 
               
               
                   
                   
                 = 
                 3 · V rec  − 6 
                 for V rec : [4, 6) 
               
               
                   
                   
                 = 
                 4 · V rec  − 12 
                 for V rec : [6, ∞] 
               
               
                   
                 2SB metric 
                 = 
                 −2 · |V rec | + 10 
                 for V rec : [−∞, −6) 
               
               
                   
                   
                 = 
                 −|V rec | + 4 
                 for V rec : [−6, −2) 
               
               
                   
                   
                 = 
                 −2 · |V rec | + 6 
                 for V rec : [−2, 0) 
               
               
                   
                   
                 = 
                 −2 · V rec  + 6 
                 for V rec : [0, 2) 
               
               
                   
                   
                 = 
                 −V rec  + 4 
                 for V rec : [2, 6] 
               
               
                   
                   
                 = 
                 −2 · V rec  + 10 
                 for V rec : [6, ∞] 
               
               
                   
                 LSB metric 
                 = 
                 V rec  + 6 
                 for V rec : [−∞, −4) 
               
               
                   
                   
                 = 
                 −V rec  − 2 
                 for V rec : [−4, 0) 
               
               
                   
                   
                 = 
                 V rec  − 2 
                 for V rec : [0, 4) 
               
               
                   
                   
                 = 
                 V rec  + 6 
                 for V rec : [4, ∞] 
               
               
                   
                   
               
             
          
         
       
     
     These above-described equations are true for the specific coding embodiments of 16 QAM and 64 QAM, both of which are square symmetric constellations. 
       FIG. 2  is a diagram illustrating an example embodiment  200  of a decision metric according to the present invention for QPSK. The  FIG. 2  graphically depicts an optimal decision metric for one rail of QPSK and  FIGS. 3–7  show the nearly optimal but very low complexity, decision metrics of this invention as applied to Gray mapped 16 QAM and 64 QAM. However, the present invention is readily adaptable and scalable to square symmetric constellations of any order, including 256 QAM, 1024 QAM, etc. Generally speaking, the present invention may be implemented within any constellations. The generic equations for the piece-wise linear approximation that is adaptable to calculate nearly optimal decision metrics to be used in bit-soft decisions are provided as follows: 
     In a general case, the bit assignment (“0” or “1”) of the Nth bit is identified for each constellation point along the voltage axis of the I or Q rail. Half way between the 0 and 1 constellation voltages, the metric is 0, and continues to “run” up the voltage that is bit mapped to “1,” and continues to “−1” at the voltage that is bit mapped to “0.” At all voltages, identify the nearest constellation voltage with a 0 mapping, and the nearest constellation voltage with a 1 mapping. Let the voltage difference between these points be Vdiff=V1−V0. The slope of the metric of this invention is slope=Vdiff/2. By specifying the metric of this invention in at least one voltage, a mid-point of a “0” and “1” constellation voltage, and the slope at all voltages (except a finite number of voltages) mid-way between constellation points, and by specifying the metric to be continuous, the metric is fully and uniquely specified. The particular embodiments for square symmetric constellations have been described above. Those persons having skill in the art will appreciate that piece-wise linear approximation may be performed to assist in bit-soft decisions for other constellation types as well. 
     The  FIGS. 3 ,  4 ,  5 ,  6 , and  7  show other example embodiments  300 ,  400 ,  500 ,  600 , and  700 , respectively, of decision metric. The  FIGS. 3 ,  4 ,  5 ,  6 , and  7 , shows other example embodiments of decision metric approximation according to the present invention. Specifically,  FIG. 3  shows embodiment  300  for the MSB of 16 QAM, and  FIG. 4  shows embodiment  400  for the LSB of 16 QAM. In addition,  FIG. 5  shows embodiment  500  for the MSB of 64 QAM,  30   FIG. 6  shows embodiment  600  for the 2 SB of 64 QAM, and  FIG. 7  shows embodiment  700  for the LSB of 64 QAM. 
       FIG. 8  is a system diagram illustrating an embodiment of an optimal decision metric approximation system  800  that is built according to the present invention. A received voltage (as shown by the input voltage) is input a symbol demodulator  805  of the optimal decision metric approximation system  800 . The symbol demodulator  805  splits the input voltage into the in-phase (I) and quadrature (Q) components, e.g., the I-Rail and the Q-rail voltages, as shown by the functional blocks  810  and  815 , respectively. Then, the optimal decision metric approximation system  800  performs optimal decision metric approximation, as shown in the functional block  820 , to generate soft-decision branch metrics/decision metrics (SD DMs/BMs) that are all provided to a soft decision decoder  841  (that may be a convolutional decoder  840  in certain embodiments), from which the hard bit decisions/best estimate for the data carried via the input voltage that is provided to the symbol demodulator  805 . The convolutional decoder  840  (being one embodiment type of the soft decision decoder  841 ) may be a Viterbi decoder in certain embodiments. 
     Within the optimal decision metric approximation  820 , the I-rail and the Q-rail voltages are dealt with separately. For example, the I-rail voltages are provided to a number of engines to deal with each of the various I-rail voltage bits of the symbol. For example, an I-rail most significant bit (MSB) engine  821 , as well as any intermediary I-rail bit engines . . . and an I-rail least significant bit (LSB) engine  829  generate the SD DM/BM for the I-rail MSB, . . . , and the I-rail LSB, respectively. Similarly, the Q-rail voltages are provided to a number of engines to deal with each of the various Q-rail voltage bits of the symbol. For example, a Q-rail most significant bit (MSB) engine  831 , as well as any intermediary Q-rail bit engines . . . and a Q-rail least significant bit (LSB) engine  839  generate the SD DM/BM for the Q-rail MSB, . . . , and the Q-rail LSB, respectively. The present invention is operable to scale to a variety of symbol sizes, having differing number of bits, without departing from the scope and spirit of the invention. 
     Again, after the soft-decision branch metrics/decision metrics (SD DMs/BMs) have been calculated, using the efficient implementation as provided within the present invention, the soft decision decoder  841  generates the hard bit decisions/best estimate for the data. If desired, the soft decision decoder  841  may be broken down into two separate convolutional decoders (an I convolutional decoder  842  and a Q convolutional decoder  844 ), or the functionality may be performed within a single integrated soft decision decoder, e.g., soft decision decoder  841 , and in certain specific embodiments, the convolutional decoder  840 . 
       FIG. 9  is a system diagram illustrating an embodiment of an improved wireless communication system  900  that is built according to the present invention. The present invention is operable within the improved wireless communication system  900  that employs the vector orthogonal frequency division multiplexing (VOFDM) portion of the broadband wireless Internet forum (BWIF) standard set. The VOFDM standard defines the physical layer and additional layers in which a plurality, e.g., up to 1,024 separate carriers (tones) carry either data (data tones) or training/pilot signals (training/pilot tones). The 1,024 tones are separated in frequency such that they are orthogonal to one another. The VOFDM standard also defines a multiple antennae receive path that combines the signal received via each of the antennae using a combining methodology. In the receiver of a VOFDM device (sometimes referred to as a wireless modem (WM) indoor unit (IDU)), a decision block maps incoming voltage signals corresponding to a particular symbol to a modulation constellation in order to extract bits carried by the symbol. 
     Here, the present invention is operable to provide for improved decoding of the received voltage signal that is provided to a wireless modem (WM) indoor unit (IDU)  945 ; optimal decision metric/branch metric approximation is performed for use in bit-soft decision (as shown in functional block  947 ) within the WM IDU  945 . It is also noted that optimal decision metric/branch metric approximation may be performed for use in bit-soft decision (as shown in functional block  927 ) within the WATS IDU  925  without departing from the scope and spirit of the invention. One or both of the transmit directions may practice the present invention. The functionality offered by the present invention may be performed in both transmit/receive paths without departing from the scope and spirit of the invention. 
     The improved wireless communication system  900  may be viewed in light of the wireless communication system reference architecture of the BWIF; the present invention provides for improved signal processing within the WM IDU  945 . A wide area network  905  communicatively couples to a backbone network  910 . The backbone network  910  in turn communicatively couples to a wireless access termination system (WATS) indoor unit (IDU)  925 . The WATS IDU  925  is serviced via operation system support  915  and a security server  920 . The WATS IDU  925  is operable to communicate with a WATS outdoor unit (ODU) and antenna  930  using one or more signals. The present invention implements vector orthogonal frequency division multiplexing (VOFDM) where the signal is partitioned among a number of frequencies. The WATS ODU  930  communicates with a wireless modem (WM) outdoor unit (ODU) and antenna  940  via wireless communication  935 . If desired, the WM ODU and antenna  940  is implemented in a spatially diversified/separated dual or multi-antennae implementation  942 . The WM ODU and antenna  940  provides the one or more signals to the WM IDU  945  that is operable to optimal decision metric/branch metric approximation  947  in making bit-soft H decisions. The WM IDU  945  communicatively couples to a customer premises equipment (CPE)  950 . The  FIG. 9  shows just one embodiment where a communication system may benefit from the functionality offered by the present invention in optimally making bit-soft decisions. 
     It is noted that the functionality offered by the present invention may be performed in both transmit/receive paths without departing from the scope and spirit of the invention, as shown by the functionality within the functional blocks  947  and  927 . 
       FIG. 10  is a functional block diagram illustrating an embodiment of an improved wireless communication method  1000  that is performed according to the present invention. In a block  1010 , raw data are encoded. The encoding performed in the block  1010  is performed by employing multi-bit/symbol encoding  1012  and employing forward error correction (FEC) encoding  1014 . After the encoding has been performed, then the encoded data is transmitted via a communication channel  1020 . After the encoded data are received at the other end of the communication channel, then the received/encoded data are decoded in a block  1030 . Within the decoding operations in a block  1032 , optimal decision metrics/branch metrics approximation are calculated for use in soft bit decisions; these optimal decision metrics/branch metrics are passed to a decoder that generates the hard bit decisions/best estimate of the data. 
       FIG. 11  is a functional block diagram illustrating an embodiment of an optimal decision metric approximation method  1100  that is performed according to the present invention. In a block  1110 , one or more voltage signal(s) is/are received. If the embodiment employs multiple antennae, then the multiple signals are combined in an optional block  1120 . In a block  1130 , the received voltage is demodulated whereby the I-rail and Q-rail voltages are extracted. In a block  1140 , the I-rail bits for the symbol are extracted, including any most significant bit (MSB), . . . , and any least significant bit (LSB). Then, in a block  1150 , the Q-rail bits for the symbol are extracted, including any most significant bit (MSB), . . . , and any least significant bit (LSB). Then, in a block  1160 , optimal decision metrics/branch metrics are approximated in accordance with the present invention. In a block  1170 , these approximated optimal decision metrics/branch metrics are fed to a soft decision decoder. Then, in a block  1180 , the hard decisions/best estimate of the data are made using the optimal decision metrics/branch metrics that were approximated in the block  1170 . 
     In view of the above detailed description of the invention and associated drawings, other modifications and variations will now become apparent to those skilled in the art. It should also be apparent that such other modifications and variations may be effected without departing from the spirit and scope of the invention.