Machine learning-based reconfigurable intelligent surface-aided MIMO systems

Nguyen, Nhan Thanh; Nguyen, Ly V.; Huynh-The, Thien; Nguyen, Duy H. N.; Swindlehurst, A. Lee; Juntti, Markku (2021-11-12)
 
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https://doi.org/10.1109/SPAWC51858.2021.9593256

Nguyen, Nhan Thanh
Nguyen, Ly V.
Huynh-The, Thien
Nguyen, Duy H. N.
Swindlehurst, A. Lee
Juntti, Markku
Institute of Electrical and Electronics Engineers
12.11.2021

N. T. Nguyen, L. V. Nguyen, T. Huynh-The, D. H. N. Nguyen, A. Lee Swindlehurst and M. Juntti, "Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems," 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Lucca, Italy, 2021, pp. 101-105, doi: 10.1109/SPAWC51858.2021.9593256

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doi:https://doi.org/10.1109/SPAWC51858.2021.9593256
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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 over-come this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural net-work (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 16 × 2 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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