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Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks by Xinyu Gao; Yuanwei Liu; Xiao Liu; Lingyang Song is a Engineering article available to read on EtoBox.

What is Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks about?

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum-rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a threestep approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).

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Author

Xinyu Gao; Yuanwei Liu; Xiao Liu; Lingyang Song

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)