Channel Coding with Deep Learning: An Overview

This chapter is devoted to the use of various neural networks and related learning algorithms in the channel coding (encoder and decoder) of wireless communications and networking. Due to its powerful nonlinear mapping and distributed processing capability, neural network-based machine learning technology could offer a more powerful channel coding solution than conventional approaches in many aspects including coding performance, computational complexity, power consumption, and processing latency. The neural networks discussed in the chapter mainly include deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The chapter first focuses on the background information of channel coding and deep learning, together with the motivation for the use of machine learning in channel coding. It introduces the channel coding schemes with DNN, CNN, and RNN networks, respectively, and finally discusses potential coding/decoding performance, computational complexity, power consumption, and processing delay.