Recent Advances in Deep Learning for Channel Coding: A Survey

Abstract

This paper provides a comprehensive survey of recent advances in deep learning (DL) techniques for channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to physical layer technologies have been extensively studied in recent years, and they are expected to be a potential breakthrough in supporting the emerging use cases of the next generation wireless communication systems such as 6G. In this paper, we focus exclusively on channel coding problems and review existing approaches that incorporate advanced DL techniques into code design and channel decoding. After briefly introducing the background of recent DL techniques, we categorize and summarize a variety of approaches, including model-free and model-based DL, for the design and decoding of modern error-correcting codes, such as low-density parity check (LDPC) codes and polar codes, to highlight their potential advantages and challenges. Finally, the paper concludes with a discussion of open issues and future research directions in channel coding.

Keywords

• Channel coding
• deep learning (DL)
• low-density parity check (LDPC) codes
• machine learning (ML)
• neural network
• polar codes