Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communications. However, a significant challenge in RISs-aided communications is the joint optimization of active and passive beamforming at base stations (BSs) and RISs respectively. Specifically, the main difficulty is attributed to the highly non-convex optimization space of beamforming matrices at both BSs and RISs, as well as the diversity and mobility of communication scenarios. To address this, we present a greenly gradient based meta learning beamforming (GMLB) approach. Unlike traditional deep learning based methods which take channel information directly as input, GMLB feeds the gradient of sum rate into neural networks. Coherently, we design a differential regulator to address the phase shift optimization of RISs. Moreover, we use the meta learning to iteratively optimize the beamforming matrices of BSs and RISs. These techniques make the proposed method to work well without requiring energy-consuming pre-training. Simulations show that GMLB could achieve higher sum rate than that of typical alternating optimization algorithms with the energy consumption by two orders of magnitude less.

翻译：可重构智能表面（RISs）已成为满足未来第六代（6G）通信中能效与可扩展性需求的重要技术。然而，RISs辅助通信面临基站（BS）与RIS分别实现的主动与被动波束赋形联合优化这一显著挑战。具体而言，主要困难源于基站和RIS波束赋形矩阵高度非凸的优化空间，以及通信场景的多样性与移动性。为解决此问题，我们提出了一种绿色梯度的元学习波束赋形（GMLB）方法。不同于将信道信息直接作为输入的经典深度学习方法，GMLB将和速率的梯度馈入神经网络。同时，我们设计了一种微分调节器来处理RIS的相位优化。此外，利用元学习迭代优化基站与RIS的波束赋形矩阵。这些技术使得所提方法无需高能耗预训练即可良好运行。仿真表明，GMLB在能耗降低两个数量级的情况下，仍能实现比典型交替优化算法更高的和速率。