On the opportunities and challenges of NOMA-based fog radio access networks: An overview

Megumi Kaneko*, Itsikiantsoa Randrianantenaina, Hayssam Dahrouj, Hesham Elsawy, Mohamed Slim Alouini

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

Abstract

Future generations of wireless networks are expected to provide new services with an unprecedented level of diverse and stringent requirements. Fog Radio Access Network (FRAN) and Non-Orthogonal Multiple Access (NOMA) have emerged as complimentary enablers to meet such requirements. On the one hand, FRAN architecture is designed to reduce the delay caused by the fronthaul link by pushing control and storage to the network edge. On the other hand, in addition to increasing the spectral and energy efficiency and the number of connected devices, NOMA has the potential to improve network latency. This paper overviews the joint benefits of enabling NOMA schemes in an FRAN architecture, by means of examining the applicability and adequateness of the NOMA-based FRAN features in achieving specific objectives of next generation of mobile networks, mainly those related to enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC). The paper further depicts the challenges and future research directions that must be addressed in order to meet such opportunities.

Original languageEnglish
Pages (from-to)205467-205476
Number of pages10
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keywords

  • Beyond 5G
  • CRAN
  • Deep Reinforcement Learning
  • FRAN
  • Machine learning
  • NOMA
  • OMA

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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