Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks

• URL: http://arxiv.org/abs/2008.12938v1
• Date: Sat, 29 Aug 2020 08:39:43 GMT
• Title: Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks
• Authors: Shimin Gong, Jiaye Lin, Jinbei Zhang, Dusit Niyato, Dong In Kim, and Mohsen Guizani
• Abstract summary: Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
• Score: 82.33619654835348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent reflecting surface (IRS) has been recently employed to reshape the wireless channels by controlling individual scattering elements' phase shifts, namely, passive beamforming. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information. In this article, we focus on machine learning (ML) approaches for performance maximization in IRS-assisted wireless networks. In general, ML approaches provide enhanced flexibility and robustness against uncertain information and imprecise modeling. Practical challenges still remain mainly due to the demand for a large dataset in offline training and slow convergence in online learning. These observations motivate us to design a novel optimization-driven ML framework for IRS-assisted wireless networks, which takes both advantages of the efficiency in model-based optimization and the robustness in model-free ML approaches. By splitting the decision variables into two parts, one part is obtained by the outer-loop ML approach, while the other part is optimized efficiently by solving an approximate problem. Numerical results verify that the optimization-driven ML approach can improve both the convergence and the reward performance compared to conventional model-free learning approaches.

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