Robust Beamforming for RIS-Aided Communications: Gradient-Based Manifold Meta Learning

Robust Beamforming for RIS-Aided Communications: Gradient-Based Manifold Meta Learning

IEEE Transactions on Wireless Communications

10.1109/TWC.2024.3435023

https://doi.org/10.1109/twc.2024.3435023

Fenghao Zhu, Xinquan Wang, Chongwen Huang, Zhaohui Yang, Xiaoming Chen, Ahmed Al Hammadi, Zhaoyang Zhang, Chau Yuen, Mérouane Debbah

05 August 2024

Zhu, F., Wang, X., Huang, C., Yang, Z., Chen, X., Al Hammadi, A., Zhang, Z., Yuen, C., & Debbah, M. (2024). Robust Beamforming for RIS-Aided Communications: Gradient-Based Manifold Meta Learning. IEEE Transactions on Wireless Communications, 23(11), 15945–15956. https://doi.org/10.1109/twc.2024.3435023

Reconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable ways. However, a major challenge in RIS-aided communication systems is the simultaneous design of the precoding matrix at the base station (BS) and the phase shifting matrix of the RIS elements. This is mainly attributed to the highly non-convex optimization space of variables at both the BS and the RIS, and the diversity of communication environments. Generally, traditional optimization methods for this problem suffer from the high complexity, while existing deep learning based methods are lacking in robustness in various scenarios. To address these issues, we introduce a gradient-based manifold meta learning method (GMML), which works without pre-training and has strong robustness for RIS-aided communications. Specifically, the proposed method fuses meta learning and manifold learning to improve the overall spectral efficiency, and reduce the overhead of the high-dimensional signal process. Unlike traditional deep learning based methods which directly take channel state information as input, GMML feeds the gradients of the precoding matrix and phase shifting matrix into neural networks. Coherently, we design a differential regulator to constrain the phase shifting matrix of the RIS. Numerical results show that the proposed GMML can improve the spectral efficiency by up to 7.31%, and speed up the convergence by 23 times faster compared to traditional approaches. Moreover, they also demonstrate remarkable robustness and adaptability in dynamic settings.

Publisher Copyright

This research / project is supported by the China National Key Research and Development Program - NA
Grant Reference no. : 2021YFA1000500, 2023YFB2904804

This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 62331023, 62101492, 62394292, U20A20158

This research / project is supported by the Zhejiang Provincial Natural Science Foundation of China - NA
Grant Reference no. : LR22F010002

This research / project is supported by the Zhejiang Provincial Science and Technology Plan Project - NA
Grant Reference no. : 2024C01033

Zhejiang University Global Partnership Fund

This research / project is supported by the Ministry of Education (MOE) - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP50220-0019

This research / project is supported by the Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research) Singapore - Terahertz Reconfigurable Intelligent Metasurfaces for 6G Communications
Grant Reference no. : M22L1b0110

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1558-2248

Institute of Microelectronics