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

2025-12-09

AI Summary

Joint optimization of base station precoding and STAR-RIS transmission/reflection coefficients in STAR-RIS-aided wireless systems is high-dimensional, strongly non-convex, and NP-hard. Method: This paper proposes a pretraining-free gradient-driven meta-learning (GML) framework, the first to directly feed optimization gradients into a lightweight neural network and uniformly support both independent-phase and coupled-phase STAR-RIS hardware models while respecting amplitude constraints and physical realizability. Contribution/Results: Compared with conventional alternating optimization, GML achieves near-linear computational complexity growth and a tenfold speedup in runtime. It attains weighted sum rates closely approaching those of optimal benchmarks, significantly enhancing scalability for large-scale deployments and enabling real-time beamforming.

Technology Category

• Machine Learning: Optimization
• Search and Optimization: Metareasoning and Metaheuristics
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Optimizes beamforming for STAR-RIS to enhance spectral efficiency and coverage
• Reduces computational complexity of joint precoding and RIS coefficient design
• Enables scalable, fast adaptation without pre-training using meta-learning

Innovation

• Gradient-based meta learning for STAR-RIS beamforming optimization
• Lightweight neural networks without pre-training for fast adaptation
• Handles amplitude and phase constraints with near-linear complexity scaling