Meta Reinforcement Learning Empowered Orchestration of SIM and RIS for Downlink Multiuser Communications

The concept of Stacked Intelligent Metasurface (SIM) stands out as an avant-garde signal processing paradigm, empowering the instantaneous manipulation of electromagnetic waves at the speed of light. This paper explores the performance of a wireless system, where a reconfigurable intelligent surface (RIS) assists the communication between a SIM-deployed base station (BS) and downlink terminals. In contrast to conventional communication systems, the downlink multiuser beamforming is performed in the wave domain by appropriately designing the electromagnetic response of the SIM. The performance of this system is evaluated by formulating a resource allocation optimization problem, aimed at maximizing the system data rate, constrained by the quality-of-service (QoS) of terminals and the transmit power budget of the BS. Owing to its intricately coupled variables and non-convex characteristics, we adeptly reformulate this problem into the form of a Markov decision process (MDP), which mirrors its dynamicity. Subsequently, we deploy a twin delayed deep deterministic policy gradient (TD3) agent to optimize its decision variables, namely, the transmit power at the BS, the electromagnetic response at the SIM, and the reflection coefficient at the RIS, in a holistic manner. Furthermore, considering the dynamic mobility of terminals in real-time scenarios, we augment the adaptability of the trained TD3 model via meta-learning. Simulation findings reveal that incorporating the RISs into SIM systems leads to a considerable improvement—reaching as high as 20%—in the average data rate experienced by users, particularly when the BS transmit power budget lies within a moderate range.