IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 11; pp. 1 - 11
• Main Authors: Amiriara, Hamid, Ashtiani, Farid, Mirmohseni, Mahtab, Nasiri-Kenari, Masoumeh
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Array signal processing; Beamforming; Complexity; Computational complexity; Convex analysis; Convex optimization (CO); Convexity; Downlink; Intelligent reflecting surface (IRS); IRS-user association; Machine learning; Machine learning (ML); MISO communication; Mixed integer; Neural networks; Optimization; Signal to noise ratio; Wireless communications; Wireless networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2023.3282272

This paper concentrates on the problem of associating an intelligent reflecting surface (IRS) to multiple users in a multiple-input single-output (MISO) downlink wireless communication network. The main objective of the paper is to maximize the sum-rate of all users by solving the joint optimization problem of the IRS-user association, IRS reflection, and BS beamforming, formulated as a non-convex mixed-integer optimization problem. The variable separation and relaxation are used to transform the problem into three convex sub-problems, which are alternatively solved through the convex optimization (CO) method. The major drawback of the proposed CO-based algorithm is high computational complexity. Thus, we make use of machine learning (ML) to tackle this problem. To this end, first, we convert the optimization problem into a regression problem. Then, we solve it with feed-forward neural networks (FNNs), trained by CO-based generated data. Simulation results show that the proposed ML-based algorithm has a performance equivalent to the CO-based algorithm, but with less computation complexity due to its offline training procedure.