用几何感知元学习优化智能表面的相位和预编码，提升速率并降低能耗。
Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS
- 基于复数神经网络与黎曼流形几何，联合优化基站预编码和智能表面相位。
- 相比现有方法快近100轮收敛，速率提升0.7 bps，功率节省1.8 dB。
- 适合通信系统设计者在高能效、快速优化场景中参考应用。
在可重构智能表面（RIS）辅助系统中，基站预编码矩阵与RIS单元相位偏移的联合优化具有较高复杂度。本文提出一种复数域、几何感知的元学习神经网络，旨在最大化多用户多输入单输出系统的加权和速率。通过利用相位偏移的复圆周几何结构和预编码的球面几何结构，优化过程在黎曼流形上进行，实现更快收敛。采用复数神经网络处理相位，基于欧拉思想更新预编码网络。所提方法优于现有基于神经网络的算法，在加权和速率、功耗及收敛速度方面均有显著提升：相比已有工作，收敛速度加快约100轮，加权和速率提高0.7 bps，功率增益达1.8 dB；且相较当前最优交替优化算法，速率提升0.86 bps，功率节省2.6 dB。
原文摘要 · Abstract (English)
In reconfigurable intelligent surface (RIS) aided systems, the joint optimization of the precoder matrix at the base station and the phase shifts of the RIS elements involves significant complexity. In this paper, we propose a complex-valued, geometry aware meta-learning neural network that maximizes the weighted sum rate in a multi-user multiple input single output system. By leveraging the complex circle geometry for phase shifts and spherical geometry for the precoder, the optimization occurs on Riemannian manifolds, leading to faster convergence. We use a complex-valued neural network for phase shifts and an Euler inspired update for the precoder network. Our approach outperforms existing neural network-based algorithms, offering higher weighted sum rates, lower power consumption, and significantly faster convergence. Specifically, it converges faster by nearly 100 epochs, with a 0.7 bps improvement in weighted sum rate and a 1.8 dB power gain when compared with existing work. Further it outperforms the state-of-the-art alternating optimization algorithm by 0.86 bps with a 2.6 dB power gain.
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