In this article, we study a model for error-correcting codes that comes from spin glass theory and leads to both new codes and a new decoding technique. Using the theory of spin glasses, it has been proven that a simple construction yields a family of binary codes whose performance asymptotically approaches the Shannon bound for the Gaussian channel. The limit is approached as the number of information bits per codeword approaches infinity while the rate of the code approaches zero. Thus, the codes rapidly become impractical. We present simulation results that show the performance of a few manageable examples of these codes. In the correspondence that exists between spin glasses and error-correcting codes, the concept of a thermal average leads to a method of decoding that differs from the standard method of finding the most likely information sequence for a given received codeword. Whereas the standard method corresponds to calculating the thermal average at temperature zero, calculating the thermal average at a certain optimum temperature results instead in the sequence of most likely information bits. Since linear block codes and convolutional codes can be viewed as examples of spin glasses, this new decoding method can be used to decode these codes in a way that minimizes the bit error rate instead of the codeword error rate. We present simulation results that show a small improvement in bit error rate by using the thermal average technique. I
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