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inis 19(20): e2

Research Article

On the optimal user grouping in NOMA system technology

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  • @ARTICLE{10.4108/eai.13-7-2018.159802,
        author={Antonino  Masaracchia and Dac-Binh  Ha and Ngoc-Phuc  Le},
        title={On the optimal user grouping in NOMA system technology},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={6},
        number={20},
        publisher={EAI},
        journal_a={INIS},
        year={2019},
        month={8},
        keywords={Algorithms and Protocols, Communication networks, Intelligent systems, Optimization, Survey and review},
        doi={10.4108/eai.13-7-2018.159802}
    }
    
  • Antonino Masaracchia
    Dac-Binh Ha
    Ngoc-Phuc Le
    Year: 2019
    On the optimal user grouping in NOMA system technology
    INIS
    EAI
    DOI: 10.4108/eai.13-7-2018.159802
Antonino Masaracchia1,*, Dac-Binh Ha2, Ngoc-Phuc Le2
  • 1: Queens University Belfast, Belfast BT7 1NN, UK
  • 2: Duy Tan University, Da Nang, Vietnam
*Contact email: A.Masaracchia@qub.ac.uk

Abstract

This paper provides a state-of-art analysis of the most relevant studies on optimal user-aggregation strategies for non-orthogonal multiple access (NOMA) technology. The main ideas behind are i) to highlight how, in addition to the adoption of an optimal power allocation scheme, an optimal user-aggregation strategy represents an important key factor for improving NOMA system performance, and ii) to provide an exhaustive survey of the most relevant studies which can serve as useful starting point for the definition of new channel state-aware user-aggregation strategies for NOMA systems which, at the time of writing, represents a research field that still remains to be investigated more in depth. A detailed and complete analysis, which permits to point out the need to guarantee a certain relationship between users’ channel gain, is provided for each cited work.

Keywords
Algorithms and Protocols, Communication networks, Intelligent systems, Optimization, Survey and review
Received
2019-06-27
Accepted
2019-07-14
Published
2019-08-07
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-7-2018.159802

Copyright © 2019 Antonino Masaracchia et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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