Applications of neural networks to digital communications: a survey
Signal Processing - Special issue on emerging techniques for communication terminals
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Novel Use of Channel Information in a Neural Convolutional Decoder
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Neural networks, error-correcting codes, and polynomials over the binary n-cube
IEEE Transactions on Information Theory
Analog decoding using a gradient-type neural network
IEEE Transactions on Neural Networks
A general rate K/N convolutional decoder based on neural networks with stopping criterion
Advances in Artificial Intelligence
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The paper investigates principles of work and BER characteristics of a new soft decision algorithm for decoding convolutional codes that is based on neural network applications. The novelty of the algorithm is in its capability to generate soft output estimates of the message bits encoded. For this purpose the noise energy function, which is defined and used for the neural network decoding of convolutional codes, has been related to the well known log-likelihood function and the soft decision decoding rule has been defined and derived. The BER curves are obtained for this novel algorithm and then compared to the curve obtained by the Viterbi algorithm and the gradient descent algorithm. Based on the theoretical model a simulator of coding communication system has been developed and used to confirm theoretically expected results. It was found that the performances of the proposed soft decision decoder are comparable or better than the performances of the recurrent neural network decoder and decoders based on the Viterbi algorithm.