Harmonizing Flexibility and Intelligence: RIS-Aided Flexible Intelligent Metasurface Systems

Composed of an array of low-cost radiating elements, flexible intelligent metasurfaces (FIMs) can adaptively morph their surface shapes by adjusting the positions of elements along the direction perpendicular to the surface. This adaptive morphing, not only enhances wireless channel conditions, but also significantly curtails power consumption. This paper conducts an adaptive performance analysis of a wireless system, in which a FIM-equipped base station (BS) leverages the presence of a reconfigurable intelligent surface (RIS) to facilitate downlink transmission. To rigorously evaluate the system's efficiency, we formulate an optimization problem centered on resource allocation, with the primary objective of maximizing the network achievable data rate. This maximization is subject to multiple constraints, especially on stringent quality-of-service (QoS) requirements of users and the BS finite power budget. Due to the highly intricate interdependencies among optimization variables and the inherent non-convexity of the problem, we strategically reformulate it as a Markov decision process (MDP), encapsulating its dynamic characteristics. To derive an optimal solution, we train a deep deterministic policy gradient (DDPG) agent, relying on MDP, which simultaneously optimizes the decision variables: the BS transmit beamforming, the morphology of the FIM and the reflection coefficient matrix of the RIS. Furthermore, to accommodate the real-world challenges imposed by user mobility, we enhance the generalization of the DDPG model through the integration of meta-learning technique, thereby significantly improving its adaptability to system variations. Numerical evaluations affirm that incorporating an RIS yields a pronounced improvement in achievable network data rate, particularly when the BS transmit power budget is maintained within a moderate operational range.