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arXiv · 2105.00347

Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems

Nhan Thanh Nguyen·Ly V. Nguyen·Thien Huynh-The·Duy H. N. Nguyen·A. Lee Swindlehurst·Markku Juntti
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Abstract

Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To overcome this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural network (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16x2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity.

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Nhan Thanh Nguyen, Ly V. Nguyen, Thien Huynh-The, Duy H. N. Nguyen, A. Lee Swindlehurst, Markku Juntti. 2021-05-01. Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems. https://arxiv.org/abs/2105.00347

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