URLLC Service for Stacked Intelligent Metasurface-Assisted RSMA Systems

A stacked intelligent metasurface (SIM) enables high-speed electromagnetic wave processing by leveraging multiple layers, giving them more computational flexibility and efficiency compared to the simpler and single-layer reconfigurable intelligent surfaces (RIS). By performing wave-domain signal processing across multiple layers and employing low-complexity transmit RF chains, SIM significantly reduces precoding delay and power consumption, making it a compelling candidate for the sixth-generation (6 G) wireless systems. This study proposes a SIM-assisted downlink multi-user multiple-input single-output (MU-MISO) network using rate splitting multiple access (RSMA), which provides ultra-reliable low-latency communication (URLLC) services to all users. Specifically, an optimization problem is developed to maximize the sum rate, while satisfying both users' quality-of-service (QoS) requirements and the available power at the base station (BS). This is achieved through a hybrid optimization framework that combines the successive convex approximation (SCA) technique for optimizing the transmit power and common-rate allocation and a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) algorithm for configuring the SIM phase shifts. Simulation results validate the effectiveness of the proposed hybrid approach, demonstrating superior sum-rate performance compared to other benchmarks.