An improved neural network-based decoder scheme for systematic convolutional code

Abstract

This thesis explores the bit error rate (BER) characteristics of a convolutional decoder constructed of a backpropagation neural network (NN) using a newly proposed input shifting window. Due to the fact that each NN is independent and unique, multiple NNs are placed in parallel and utilize the majority rule to further improve BER performance. NNs are efficient in complex statistical systems because of their inherently fast parallel-processing speed and pattern-recognition abilities. It was found that with a code rate of ½ and a constraint length of K = 3, a NN with the optimal convolutional code generator polynomial, which is non-systematic, has poor performance and a NN with a systematic convolutional code generator polynomial shows comparable performance to a conventional hard-decision Viterbi decoder using the optimal convolutional code generator polynomial.