Soft Decision Decoding For Block Codes Using Artificial Neural Networks

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9216686 Wicker The revival of interest in artificial neural networks (ANN's) in the early 1980's has spurred research into their application in many engineering fields, including digital signal processing and communications. The pattern recognition capabilities of ANN's allows them to emulate a variety of functions. This emulation is enhanced through the use of training algorithms, such as back-propagation, that allow some types of ANN's to "learn" desired functions. This research is an investigation into the application of ANN's to the soft decision decoding of block error control codes. In the past two years, experimental studies have demonstrated that certain ANN's can perform error control decoding for some block codes. The experimental results have so far shown that ANN's can perform hard decision decoding, but soft decision decoding results have been extremely limited. In this investigation, the error control problem is first translated into the terms of functional analysis. Decoding is viewed as a mapping from a continuous received signal space onto a discrete information word space. Decoder design is thus translated into a problem of functional approximation. The next step is to use a highly promising class of ANN's, feedforward neural networks (FFNN's), to implement the decoding function in an efficient manner. The operation of FFNN's can be viewed geometrically, with each layer in the network carving up the received signal space into increasingly lower dimensional subspaces, culminating in an output decision layer that provides estimates of the transmitted information bits. The functional approximation approach thus allows for ideal soft decision decoding, while the FFNN design uses the algebro-geometric redundancy within the block code to reduce the decoder complexity. The performance of these decoders will be further enhanced through the use of ANN training techniques that will match input layer decision metrics to channel conditions w hile the decoder is operating. The result will be a series of efficient soft decision decoders for a variety of block codes in a variety of applications. ***