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

arXiv: https://arxiv.org/abs/2512.17928 · HTML: https://mlsanity.com/paper/2512.17928/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer](https://mlsanity.com/paper/2205.05029/) (2022)
- [Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning](https://mlsanity.com/paper/2402.10626/) (2024)
- [Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning](https://mlsanity.com/paper/2311.06861/) (2023)
- [Robust Beamforming with Gradient-based Liquid Neural Network](https://mlsanity.com/paper/2405.07291/) (2024)

## Abstract (arXiv)

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.