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IRS-user association in irs-aided miso wireless networks: convex optimization and machine learning approaches

Amiriara, H ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1109/TVT.2023.3282272
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. This article 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. © 1967-2012 IEEE
  6. Keywords:
  7. Beamforming ; Convex optimization (CO) ; Intelligent reflecting surface (IRS) ; IRS-user association ; Machine learning (ML)
  8. Source: IEEE Transactions on Vehicular Technology ; Volume 72, Issue 11 , 2023 , Pages 14305-14316 ; 00189545 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/10143313