University of Oulu

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

Machine learning-based reconfigurable intelligent surface-aided MIMO systems

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Author: Nguyen, Nhan Thanh1; Nguyen, Ly V.2; Huynh-The, Thien3;
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
2Computational Science Research Center, San Diego State University, CA, USA
3ICT Convergence Research Center, Kumoh National Institute of Technology, Gyeongsangbuk-do, Korea
4Department of Electrical and Computer Engineering, San Diego State University, CA, USA
5Department of Electrical Engineering and Computer Science, University of California, Irvine, CA, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2023-04-04


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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Series: SPAWC
ISSN: 1948-3244
ISSN-E: 1948-3252
ISSN-L: 1948-3244
ISBN: 978-1-6654-2851-4
ISBN Print: 978-1-6654-2852-1
Pages: 101 - 105
DOI: 10.1109/SPAWC51858.2021.9593256
Host publication: 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Conference: IEEE International Workshop on Signal Processing Advances in Wireless Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been supported in part by Academy of Finland under 6Genesis Flagship (grant 318927) and EERA Project (grant 332362).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
332362 (Academy of Finland Funding decision)
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