🤖 AI Summary
To address the high secrecy outage probability (SOP) in physical-layer security, this paper proposes a Fluid Reconfigurable Intelligent Surface (FRIS) architecture. Unlike conventional planar or compact RISs, FRIS dynamically reconfigures both the spatial distribution of its unit elements and their phase responses to exploit low spatial correlation and thereby enhance secrecy capacity. Methodologically, we jointly model the end-to-end channel via maximum likelihood estimation (MLE) and optimize the FRIS layout adaptively using Q-learning, while co-designing beamforming and phase control for closed-loop performance enhancement. Experimental results demonstrate that FRIS significantly reduces SOP even without phase optimization; compared to compact RISs, its spatial flexibility enables superior spatial decorrelation and more robust secrecy gains. This work establishes a new paradigm for secure wireless communications in dynamic environments.
📝 Abstract
This work examines the secrecy outage probability (SOP) in Fluid Reconfigurable Intelligent Surfaces (FRIS) and contrasts their performance against two alternative RIS architectures: a traditional planar RIS and a compact RIS layout. To characterize the end-to-end FRIS channel, a maximum likelihood estimation (MLE) approach is introduced, while a Q-learning algorithm is employed to adaptively select the spatial positions of FRIS elements. Numerical evaluations show that optimizing element placement in FRIS significantly improves SOP compared to conventional RIS without phase adaptation. However, these improvements become less evident once the conventional RIS implements optimized beamforming (BF) and phase-shift (PS) controlling. In addition, FRIS maintains a clear advantage over compact RIS designs with optimized BF and PS, mainly due to its lower spatial correlation. Results further indicate that reducing the inter-element distance negatively impacts SOP, highlighting the importance of spatial diversity.