High-order neural models for error-correcting code
Clark D. Jeffries, Peter Protzel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
The decoding and error-correction of data transmitted over a noisy channel is in principle equivalent to the operation of a neural network performing as a content-addressable memory. For a successful application however the neural network has to be capable of storing arbitrary words and it has to be guaranteed that the stored words represent the only stable attractors of the memory. In this paper we present a novel high order neural network architecture that has these characteristics. The analog nature of the network can be used to perform softdecision decoding with any block code. The performance in terms of post-decoding bit error rate versus signal-to-noise ratio is demonstrated for two exemplary block codes. The comparison with a conventional decoding algorithm for a (15 cyclic redundancy code shows for example that the bit error rate at 7dB signal-to-noise ratio can be decreased by two orders of magnitude. 1.