Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks

TL;DR

This paper introduces a comprehensive machine learning-based framework for optimizing resource allocation in IRS-aided MISO-NOMA networks, significantly improving throughput and efficiency through joint passive beamforming, user clustering, and power control.

Contribution

It presents a novel integrated approach combining LSTM, K-GMM, and DQN algorithms for dynamic resource management in IRS-aided NOMA networks, which is a new contribution.

Findings

Achieves up to 35% throughput gain over OMA.

Outperforms benchmark algorithms in simulations.

Effectively predicts user mobility and optimizes 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, power allocation coefficient vector and number of clusters, 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 throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).