On the Orchestration of SIM and UAV

This paper centers around a multi-antenna unmanned aerial vehicle (UAV) which leverages stacked intelligent metasurface (SIM) as a green technology for signal processing at the electromagnetic wave domain. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory and transmit power at the UAV, as well as the electromagnetic response at the SIM as decision variables. Since the problem is non-convex and challenging to solve, we reformulate it in Markov decision process form and train a distributed distributional deep deterministic policy gradient (D4PG) agent to optimize its decision variables. Concerning the significant mobility of the UAV and thus remarkable rearrangement of the system, we enhance the adaptability and generalization of the trained D4PG model by integrating meta-learning strategy. According to simulations, wave-domain beamforming via SIM at UAV leads to 40% and 22% reduction in average energy consumption, compared fullydigital and beamspace beamforming, respectively.