Bello, Mouktar and Chorti, Arsenia and Fijalkow, Inbar and Yu, Wenjuan and Musavian, Leila (2020) 'Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints.' IEEE Open Journal of the Communications Societs, 1. pp. 1691-106. ISSN 2644-125X

Preview

Text 09226621.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

In this article, we study the performance of an uplink non-orthogonal multiple access(NOMA) network under statistical quality of service (QoS) delay constraints, captured through eachuser’s effective capacity (EC). We first propose novel closed-form expressions for the EC in a two-userNOMA network and show that in the high signal-to-noise ratio (SNR) region, the “strong” NOMA user,referred to asU2, has a limited EC, assuming the same delay constraint as the “weak” user, referred to asU1. We demonstrate that for the weak userU1, OMA and NOMA have comparable performance at lowtransmit SNRs, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, forthe strong userU2, NOMA achieves higher EC than OMA at small SNRs, while OMA becomes morebeneficial at high SNRs. Furthermore, we show that at high transmit SNRs, irrespective of whether theapplication is delay tolerant, or not, the performance gains of NOMA over OMA forU1, and OMA overNOMA forU2remain unchanged. When the delay QoS of one user is fixed, the performance gap betweenNOMA and OMA in terms of total EC increases with decreasing statistical delay QoS constraints for theother user. Next, by introducing pairing, we show that NOMA with user-pairing outperforms OMA, interms of total uplink EC. The best pairing strategies are given in the cases of four and six users NOMA,raising once again the importance of power allocation in the optimization of NOMA’s performance.

Item Type:	Article
Uncontrolled Keywords:	Beyond 5G (B5G), effective capacity, low latency, non-orthogonal multiple access(NOMA), quality of service (QoS), user-pairing
Divisions:	Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	24 Nov 2020 11:06
Last Modified:	23 Sep 2022 19:42
URI:	http://repository.essex.ac.uk/id/eprint/28885

Actions (login required)

View Item