Open Access

ARTICLE

Lowest-Opportunities User First-Based Subcarrier Allocation Algorithm for Downlink NOMA Systems

Mohammed Abd-Elnaby^*, Sameer Alsharif, Hesham Alhumyani, Fahad Alraddady

Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

* Corresponding Author: Mohammed Abd-Elnaby. Email:

(This article belongs to the Special Issue: Recent Advances in Intelligent Systems and Communication)

Intelligent Automation & Soft Computing 2021, 30(3), 1033-1048. https://doi.org/10.32604/iasc.2021.019341

Received 10 April 2021; Accepted 19 May 2021; Issue published 20 August 2021

Abstract

Non-orthogonal multiple access (NOMA) is one of the promising 5G technologies to improve spectral efficiency massive connectivity and cell-edge throughput. The performance of NOMA systems mainly depends on the efficiency of the subcarrier allocation algorithm. This paper aims to jointly optimize spectral efficiency (SE), outage probability, and fairness among users with respect to the subcarrier allocation for downlink NOMA systems. We propose a low-complexity greedy-based subcarrier allocation algorithm based on the lowest-opportunities user’s first precept. This precept is based on computing the number of opportunities for each user to select a subcarrier with good channel gain by counting the number of available subcarriers with channel gains higher than a particular threshold value. So, the proposed algorithm allows the users with low opportunities to select their desired subcarriers first and hence improves their achieved data rates. Simulation results demonstrate that compared to orthogonal multiple access (OMA), and traditional NOMA algorithms, the proposed subcarrier allocation algorithm attains significantly superior spectral efficiency, fairness performance, user data rate, and outage probability. In addition, the proposed algorithm’s performance metrics improve as the number of users in the system increases, contrary to traditional NOMA algorithms.

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Keywords

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