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A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion
Advances in Artificial Intelligence
A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). Thedoi:10.1155/2009/356120 fatcat:ox76rctwivgdvayrs2573e4utu