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

被引：0
作者：
Masaracchia A. [1 ]
Nguyen M.T. [2 ]
Kortun A. [1 ]
机构：
[1] Department of Electrical Engineering, Thai Nguyen University of Technology, Thai Nguyen
来源：
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems | 2020年 / 7卷 / 25期
关键词：
Channel-State-Information; Neural Network; Reinforcement Learning; user mobility;
D O I：
10.4108/eai.7-1-2021.167841
中图分类号：
学科分类号：
摘要：
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 in presence of users' mobility can be improved, especially when a feed-forward neural network is used as CSI estimator. © 2020. Antonino Masaracchia et al., licensed to EAI. All Rights Reserved.
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页码：1 / 7
页数：6
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