This paper provides a comprehensive survey on recent advances in deep learning (DL) techniques for the channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to the physical layer technologies have been extensively studied in recent years, and 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 the 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 mode-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.

翻译：本文对深度学习（DL）技术在信道编码问题中的最新进展进行了全面综述。受深度学习在多个研究领域近期取得成功的启发，其在物理层技术中的应用近年来得到了广泛研究，并有望成为支持下一代无线通信系统（如6G）新兴用例的潜在突破点。本文专门聚焦于信道编码问题，回顾了将先进深度学习技术融入编码设计和信道解码的现有方法。在简要介绍近期深度学习技术背景后，我们对现代纠错码（如低密度奇偶校验（LDPC）码和极化码）的设计与解码中的多种方法进行了分类和总结，包括无模型和基于模型的深度学习方法，以突出其潜在优势与挑战。最后，本文对信道编码领域的开放性问题及未来研究方向进行了讨论。