Reference : Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/49528
Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks
English
Le []
Nguyen, van Dinh mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Dobre, Octavia A. []
Nguyen []
Zhao []
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Dec-2021
IEEE Transactions on Vehicular Technology
Institute of Electrical and Electronics Engineers
70
12
12872-12887
Yes (verified by ORBilu)
International
0018-9545
United States
[en] Cell-free massive multiple-input multipleoutput ; full-pilot zero-forcing ; k-means ; machine learning ; nonorthogonal multiple access ; power allocation ; user clustering
[en] The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC certainly results in a suboptimal solution while an exhaustive search method comes at the cost of high complexity, especially for systems of medium-to-large size. To address this problem, we develop two efficient unsupervised machine learning based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system. Adopting full-pilot zero-forcing at access points (APs) to comprehensively assess the system performance, we formulate the sum SE optimization problem taking into account power constraints at APs, necessary conditions for implementing successive interference cancellation, and required SE constraints at user equipments. The formulated optimization problem is highly non-convex, and thus, it is difficult to obtain the global optimal solution. Therefore, we develop a simple yet efficient iterative algorithm for its solution. In addition, the performance of collocated massive MIMO-NOMA (COmMIMO-NOMA) system is also characterized. Numerical results are provided to show the superior performance of the proposed UC algorithms compared to baseline schemes. The effectiveness of applying NOMA in CFmMIMO and COmMIMO systems is also validated.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
European Commission - EC ; Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/49528
H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems
FnR ; FNR11632107 > Lei Lei > ROSETTA > Resource Optimization For Integrated Satellite-5g Networks With Non-orthogonal Multiple Access > 01/09/2018 > 31/08/2021 > 2017

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