Random neural network decoder for error correcting codes

This paper presents a novel Random Neural Network (RNN) based soft decision decoder for block codes. One advantage of the proposed decoder over conventional serial algebraic decoders is that noisy codewords arriving in non binary form can be corrected without rst rounding them to binary form. Another advantage is that the RNN, after being trained, has a simple hardware realization that makes it candidate for implementation as a VLSI chip. This seems to make the neural network decoding inherently more accurate, faster, and more robust than conventional decoding. The proposed decoder is tested on Hamming linear codes and the results are compared with that of the optimum soft decision decoder and the conventional hard decision decoder. Extensive simulations show that the RNN based decoder reduces the error probability to zero in the range of the error correcting capacity of the used code. On the other hand, it is much better than the hard decision decoder for codewords corrupted with more errors.