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

Amanda Ly; Yu Dong Yao

doi:10.1109/OJCOMS.2021.3058353

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

Amanda Ly
, Yu Dong Yao

Department of Electrical and Computer Engineering

Stevens Institute of Technology

Research output: Contribution to journal › Review article › peer-review

71 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 language	English
Article number	9353849
Pages (from-to)	396-408
Number of pages	13
Journal	IEEE Open Journal of the Communications Society
Volume	2
DOIs	https://doi.org/10.1109/OJCOMS.2021.3058353
State	Published - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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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10.1109/OJCOMS.2021.3058353

Cite this

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title = "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.",

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",

author = "Amanda Ly and Yao, \{Yu Dong\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2021",

doi = "10.1109/OJCOMS.2021.3058353",

language = "English",

volume = "2",

pages = "396--408",

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AU - Ly, Amanda

AU - Yao, Yu Dong

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Deep learning (DL)

KW - fifth generation (5G)

KW - low-density parity-check coding (LDPC)

KW - machine learning (ML)

KW - massive multiple-input multiple-output (MIMO)

KW - non-orthogonal multiple access (NOMA)

KW - resource allocation

KW - security

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UR - https://www.scopus.com/pages/publications/85113858222#tab=citedBy

U2 - 10.1109/OJCOMS.2021.3058353

DO - 10.1109/OJCOMS.2021.3058353

M3 - Review article

AN - SCOPUS:85113858222

VL - 2

SP - 396

EP - 408

JO - IEEE Open Journal of the Communications Society

JF - IEEE Open Journal of the Communications Society

M1 - 9353849

ER -

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

Abstract

UN SDGs

Keywords

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