Efficient Beamforming Optimization for STAR-RIS-Assisted Communications: A Gradient-Based Meta Learning Approach

• URL: http://arxiv.org/abs/2512.17928v1
• Date: Tue, 09 Dec 2025 16:28:15 GMT
• Title: Efficient Beamforming Optimization for STAR-RIS-Assisted Communications: A Gradient-Based Meta Learning Approach
• Authors: Dongdong Yang, Bin Li, Jiguang He, Yicheng Yan, Xiaoyu Zhang, Chongwen Huang,
• Abstract summary: STAR-RIS is a promising technology to realize full-space coverage and spectral efficiency in next-generation wireless networks.<n>We develop a gradient-based meta learning (GML) that directly optimization feeds into neural networks, thereby removing the need for pre-training and enabling fast adaptation.
• Score: 29.972987396713126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has emerged as a promising technology to realize full-space coverage and boost spectral efficiency in next-generation wireless networks. Yet, the joint design of the base station precoding matrix as well as the STAR-RIS transmission and reflection coefficient matrices leads to a high-dimensional, strongly nonconvex, and NP-hard optimization problem. Conventional alternating optimization (AO) schemes typically involve repeated large-scale matrix inversion operations, resulting in high computational complexity and poor scalability, while existing deep learning approaches often rely on expensive pre-training and large network models. In this paper, we develop a gradient-based meta learning (GML) framework that directly feeds optimization gradients into lightweight neural networks, thereby removing the need for pre-training and enabling fast adaptation. Specifically, we design dedicated GML-based schemes for both independent-phase and coupled-phase STAR-RIS models, effectively handling their respective amplitude and phase constraints while achieving weighted sum-rate performance very close to that of AO-based benchmarks. Extensive simulations demonstrate that, for both phase models, the proposed methods substantially reduce computational overhead, with complexity growing nearly linearly when the number of BS antennas and STAR-RIS elements grows, and yielding up to 10 times runtime speedup over AO, which confirms the scalability and practicality of the proposed GML method for large-scale STAR-RIS-assisted communications.

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