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

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 in presence of users’ mobility can be improved, especially when a feed-forward neural network is used as CSI estimator. Received on 10 December 2020; accepted on 19 December 2020; published on 07 January 2021