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1796-2021 (Online); 2374-4367 (Print)
Prof. Maode Ma
Prof. Jalel Ben-Othman, Prof. Nobuo Funabiki
Prof. Jason Z. Kang
or comments to
Prof. Maode Ma
College of Engineering, Qatar University, Doha, Qatar
I'm very happy and honored to take on the position of editor-in-chief of JCM, which is a high-quality journal with potential and I'll try my every effort to bring JCM to a next level...
Volume 17, No. 7 has been published online!
Welcome Prof. Sherali Zeadally from USA to join the Editorial board of JCM.
Volume 17, No. 4-5 has been indexed by Scopus.
Volume 17, No. 6, June 2022
Beamspace NOMA Using User Clustering and Throughput Optimisation Algorithms for Massive MIMO
Haitham Al Fatli, Khairun Nidzam Ramli, and Elfarizanis Baharudin
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
—Massive Multiple Input Multiple Output (MIMO) using millimetre wave transmissions received significant attention due to its significance of high data rate. However, achieving energy and spectrum efficient millimetre wave communications is challenging due to the dedicated Radio Frequency (RF) chain. Non-Orthogonal Multiple Access (NOMA) is used in beamspace MIMO (BS MIMO) significantly overcome such challenges. This paper proposes the enhanced approach of beamspace MIMO NOMA using a simple yet effective clustering solution with a C-NOMA throughput optimisation algorithm. This proposal involves the lightweight user clustering, lens antenna, and clustering-based iterative power allocation algorithm to enhance each cluster's spectral and energy efficiency performance. After cluster formation, the throughput optimisation function applies. Iterative power optimisation method is proposed to allocate power to each user in each cluster dynamically. Therefore, compared to recent clustering and NOMA methods, the proposed BS MIMO C-NOMA improves Energy Efficiency (EE) and Spectral Efficiency (SE) with minimum computational overhead. Results demonstrate that high EE and SE, respectively, as compared with the percentage of improvement of 26% and 37% in the existing BS MIMO NOMA and improvement of 16.47 % and 27.72 % in User Clustering based on Channel Gain (UCCG) MIMO NOMA among 50 and 100 users.
—SE, EE, mmWave, Beamspace MIMO, clustering, channel gain
Cite: Haitham Al Fatli, Khairun Nidzam Ramli, and Elfarizanis Baharudin, "Beamspace NOMA Using User Clustering and Throughput Optimisation Algorithms for Massive MIMO," Journal of Communications vol. 17, no. 6, pp. 463-471, June 2022. Doi: 10.12720/jcm.17.6.463-471
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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