Title: Performance Evaluation of Channel Decoding With Deep Neural Networks,
Abstract: With the demand of high data rate and low latency in fifth generation (5G),
deep neural network decoder (NND) has become a promising candidate due to its
capability of one-shot decoding and parallel computing. In this paper, three
types of NND, i.e., multi-layer perceptron (MLP), convolution neural network
(CNN) and recurrent neural network (RNN), are proposed with the same parameter
magnitude. The performance of these deep neural networks are evaluated through
extensive simulation. Numerical results show that RNN has the best decoding
performance, yet at the price of the highest computational overhead. Moreover,
we find there exists a saturation length for each type of neural network, which
is caused by their restricted learning abilities.