A review of deep learning in 5G research: Channel coding, massive MIMO, multiple access, resource allocation, and network security

Abstract

The current development of 5G technology is flourishing with widespread deployment across the world at a rapid pace. However, there is still a demand concerning 5G research for service and performance improvement. Research tasks include but are not limited to quality-of-service (QoS), energy efficiency, massive connectivity, reliable communications, and security. Due to the advancement of deep learning, numerous such research has utilized this technique. This article provides a comprehensive review of 5G communications research using deep learning. Specifically, we address the issues of low-density parity-check (LDPC) coding, massive multiple-input multiple-output (MIMO), non-orthogonal multiple access (NOMA), resource allocation, and security.