Deep Learning Based Power Allocation Schemes in NOMA Systems: A Review

Zeyad Elsaraf, Faheem Khan, Qasim Ahmed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Achieving significant performance gains both in terms of system throughput and massive connectivity, non-orthogonal multiple access (NOMA) has been considered as very promising candidate for the future wireless communications technologies. It has already received serious consideration for implementation in the fifth generation (5G) and beyond wireless communication systems. This is mainly due to NOMA allowing more than one user to utilise one transmission resource simultaneously at the transmitter side and successive interference cancellation (SIC) at the receiver side. However, in order to take advantage of the benefits NOMA provides in an optimal manner, power allocation needs to be considered to maximise the system throughput.This problem is non-deterministic polynomial-time (NP)-hard which is mainly why the use of deep learning techniques for power allocation is required. In this paper, a state-of-the-art review on cutting edge solutions to the power allocation optimisation problem using deep learning is provided. It is shown that the use of deep learning techniques to obtain effective solutions to the power allocation problem in NOMA is paramount for the future of NOMA based wireless communication systems. Furthermore, several possible research directions based on the the use of deep learning in NOMA systems are presented.
Original languageEnglish
Title of host publication2021 The 26th International Conference on Automation and Computing
Subtitle of host publicationSystem Intelligence through Automation & Computing (ICAC 2021)
EditorsChenguang Yang
PublisherIEEE
Number of pages6
ISBN (Electronic)9781860435577
ISBN (Print)9781665443524
DOIs
Publication statusPublished - 15 Nov 2021
Event26th International Conference on Automation and Computing - University of Portsmouth, Portsmouth, United Kingdom
Duration: 2 Sep 20214 Sep 2021
Conference number: 26
http://www.cacsuk.co.uk/index.php/icac2021

Conference

Conference26th International Conference on Automation and Computing
CountryUnited Kingdom
CityPortsmouth
Period2/09/214/09/21
Internet address

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