  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.
