inis 21(25): e5

Research Article

User mobility into NOMA assisted communication: Analysis and a Reinforcement Learning with Neural Network based approach

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  • @ARTICLE{10.4108/eai.7-1-2021.167841,
        author={Antonino Masaracchia and Minh T. Nguyen and Ayse Kortun},
        title={User mobility into NOMA assisted communication: Analysis and a Reinforcement Learning with Neural Network based approach},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={7},
        number={25},
        publisher={EAI},
        journal_a={INIS},
        year={2021},
        month={1},
        keywords={Channel-State-Information, Neural Network, Reinforcement Learning, user mobility},
        doi={10.4108/eai.7-1-2021.167841}
    }
    
  • Antonino Masaracchia
    Minh T. Nguyen
    Ayse Kortun
    Year: 2021
    User mobility into NOMA assisted communication: Analysis and a Reinforcement Learning with Neural Network based approach
    INIS
    EAI
    DOI: 10.4108/eai.7-1-2021.167841
Antonino Masaracchia1,*, Minh T. Nguyen2, Ayse Kortun1
  • 1: School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT7
  • 2: Department of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen 24000, Vietnam
*Contact email: A.MAsaracchia@qub.ac.uk

Abstract

This article proposes a performance analysis of a non-orthogonal multiple access (NOMA) transmission system in the presence of user mobility. The main objective is to illustrate how the users’ mobility can affect the system performance in terms of downlink aggregated throughput, downlink network fairness, and percentage of quality-of-service requirement guaranteed. The idea behind is to highlight the importance to take into account user mobility in designing power allocation policies for NOMA systems. It is shown how the communication technologies are mainly dependent from channel state information (CSI) which in turns depends on users’ mobility. In addition a reinforcement learning (RL) to tackle with user mobility is proposed. Performance investigations regarding the proposed framework have shown how the network performances inpresence of users’ mobility can be improved, especially when a feed-forward neural network is used as CSI estimator.