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

Amanda Ly, Yu Dong Yao

Research output: Contribution to journalReview articlepeer-review

55 Scopus citations

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.

Original languageEnglish
Article number9353849
Pages (from-to)396-408
Number of pages13
JournalIEEE Open Journal of the Communications Society
Volume2
DOIs
StatePublished - 2021

Keywords

  • Deep learning (DL)
  • fifth generation (5G)
  • low-density parity-check coding (LDPC)
  • machine learning (ML)
  • massive multiple-input multiple-output (MIMO)
  • non-orthogonal multiple access (NOMA)
  • resource allocation
  • security

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