Stochastic approximation to an optimal performance o fthe neural convolutional decoders

신경회로망 콘볼루션 복호기의 최적 성능에 대한 확률적 근사화

It is well known that the viterbi algorithm proposed as a mthod of decoding convolutional codes is in fact maximum likelihood (ML) and therefore optimal. But, because hardware complexity grows exponentially with the constraint length, there will be severe constraints on the implementation of the viterbi decoders. In this paper, the three-layered backpropagation neural networks are proposed as an alternative in order to get sufficiently useful performance and deal successfully with the problems of the viterbi decoder. This paper shows that the neural convolutional decoder (NCD) can make a decision in the point of ML in decoding and describes simulation results. The cause of the difference between stochastic results and simulation results is discussed, and then thefuture prospect of the NCD is described on the basis of the characteristic of the transfer function.