Title

Random Neural Network Decoder For Error Correcting Codes

Abstract

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 first 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 decker 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.

Publication Date

12-1-1999

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

5

Number of Pages

3241-3245

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0033342818 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0033342818

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