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

TL;DR

This paper investigates how fluid reconfigurable intelligent surfaces (FRIS) can enhance physical layer security by optimizing element placement and phase design, outperforming traditional RIS architectures in secrecy outage probability.

Contribution

It introduces a maximum likelihood estimation for channel characterization and employs Q-learning for adaptive element positioning in FRIS, demonstrating improved security performance.

Findings

Optimized element placement in FRIS reduces SOP significantly.

FRIS outperforms traditional RIS with phase adaptation, especially over compact RIS.

Reducing inter-element distance in FRIS increases spatial diversity and improves security.

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.