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Reconfigurable intelligent surfaces (RISs) have become a promising technologyto meet the requirements of energy efficiency and scalability in futuresix-generation (6G) communications. However, a significant challenge inRISs-aided communications is the joint optimization of active and passivebeamforming at base stations (BSs) and RISs respectively. Specifically, themain difficulty is attributed to the highly non-convex optimization space ofbeamforming matrices at both BSs and RISs, as well as the diversity andmobility of communication scenarios. To address this, we present a greenlygradient based meta learning beamforming (GMLB) approach. Unlike traditionaldeep learning based methods which take channel information directly as input,GMLB feeds the gradient of sum rate into neural networks. Coherently, we designa differential regulator to address the phase shift optimization of RISs.Moreover, we use the meta learning to iteratively optimize the beamformingmatrices of BSs and RISs. These techniques make the proposed method to workwell without requiring energy-consuming pre-training. Simulations show thatGMLB could achieve higher sum rate than that of typical alternatingoptimization algorithms with the energy consumption by two orders of magnitudeless.

Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning