Deep Learning in Physical Layer Communications

TLDR

Deep learning has the potential to revolutionize communication systems by improving individual block performance or optimizing entire transmitter/receiver chains. The article reviews recent advances in DL-based physical layer communications, categorizes applications into block‑structured and non‑block‑structured systems, and outlines future research directions. The authors illustrate DL’s effectiveness in signal compression and detection for block‑structured systems and discuss recent work on end‑to‑end DL communication systems.

Abstract

DL has shown great potential to revolutionize communication systems. This article provides an overview of the recent advancements in DL-based physical layer communications. DL can improve the performance of each individual block in communication systems or optimize the whole transmitter/receiver. Therefore, we categorize the applications of DL in physical layer communications into systems with and without block structures. For DL-based communication systems with the block structure, we demonstrate the power of DL in signal compression and signal detection. We also discuss the recent endeavors in developing DL-based end-to-end communication systems. Finally, potential research directions are identified to boost intelligent physical layer communications.