A New Error-Correcting Syndrome Decoder with Retransmit Signal Implemented with an Hardlimit Neural Network.
José Barahona da Fonseca · 2014
Abstract. Still today the problem of counting the errors of a noisy received word is an open problem in literature. This means that when we use an error correcting code we cannot control if the number of errors of the received noisy word is greater than the error correction capability of the code of k errors, k=(d-1)/2, where d is the minimum Hamming distance of the code. The main advantage of our proposal results from the introduction of the Retransmit signal when the syndrome decoder detects an ambiguity situation and cannot correct the noisy word. These ambiguity situations occur when happens one more error than the error correction capability of the error correcting code. This property of the error correcting syndrome scheme allows increasing the error correction capability of an error correcting code by one error at a little increment of bandwidth or delay in the transmission. Although there are some proposals of implementation of errorcorrecting decoders with neural networks in literature our work is completely different in what concerns three main aspects. First we propose the implementation of the retransmit signal based on the detection of ambiguity of the minimum