A generalised framework for convolutional decoding using a recurrent neural network
Philip Seeker, Stevan Mirko Berber, Zoran A. Salcic · 2004
This paper introduces a model of the conventional convolutional coding system based on representing encoder outputs as n-dimensional vectors in Euclidean space. Previously, it has been shown that the gradient descent algorithm can be used for bit decoding at the receiver, and can be implemented using a recurrent neural network (RNN). In this paper we generalise the mathematical framework for the general rate 1/n encoder. Our simulation results confirm that the RNN decoder is capable of performing very close to the Viterbi decoder, and has been found here to work extremely well for some simple convolutional codes.