IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866913536111804416 |
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| author | Amiriara, Hamid Ashtiani, Farid Mirmohseni, Mahtab Nasiri-Kenari, Masoumeh |
| author_facet | Amiriara, Hamid Ashtiani, Farid Mirmohseni, Mahtab Nasiri-Kenari, Masoumeh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_16667 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches Amiriara, Hamid Ashtiani, Farid Mirmohseni, Mahtab Nasiri-Kenari, Masoumeh Signal Processing Systems and Control 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. |
| title | IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches |
| topic | Signal Processing Systems and Control |
| url | https://arxiv.org/abs/2210.16667 |