Exploring Spatial Flexibility and Phase Design in Fluid Reconfigurable Intelligent Surfaces: A Physical Layer Security Perspective

This paper 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) control. 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. Moreover, the proposed MLE-based channel modeling and learning-driven optimization framework offer a scalable and data-efficient methodology for exploring secrecy performance. These findings establish FRIS as a promising architecture for improving physical layer security in spatially constrained and correlation-limited wireless environments.