Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach

TL;DR

This paper introduces a machine learning framework for optimizing resource allocation in IRS-assisted MISO-NOMA networks, improving sum rate performance through user mobility prediction, clustering, and joint beamforming and power control.

Contribution

It proposes a novel three-step machine learning approach combining LSTM, K-GMM, and DQN for joint optimization in IRS-NOMA systems, which is a new integration of techniques.

Findings

The proposed algorithm outperforms benchmark methods.

IRS-NOMA system achieves better performance than IRS-OMA.

Effective user mobility prediction enhances resource allocation.

Abstract

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 and power allocation coefficient vector, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step 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 performance of IRS-NOMA system is better than IRS-OMA system.