Fluid Antenna System-assisted Physical Layer Secret Key Generation

2025-09-18

AI Summary

This work addresses the low key generation rate (KGR) in physical-layer key generation (PLKG) for multi-antenna base stations, which is fundamentally limited by the number of radio-frequency (RF) chains and insufficient channel reciprocity. To overcome these limitations, we propose a novel PLKG framework assisted by fluid antenna systems (FAS). Our method jointly optimizes dynamic sparse port selection and beamforming to adaptively exploit spatial channel correlation and enhance legitimate-channel reciprocity. To achieve low-complexity, high-accuracy FAS configuration, we introduce three algorithmic components: recursive convex approximation, reweighted ℓ₁-norm regularization, and a Rayleigh-quotient-based sliding-window port selection. Simulation results under both independent and spatially correlated channel models demonstrate that the proposed scheme achieves significantly higher KGR using fewer RF chains, outperforming conventional fixed-antenna and static-port-selection approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

This paper investigates physical-layer key generation (PLKG) in multi-antenna base station systems, by leveraging a fluid antenna system (FAS) to dynamically customize radio environments. Without requiring additional nodes or extensive radio frequency chains, the FAS effectively enables adaptive antenna port selection by exploiting channel spatial correlation to enhance the key generation rate (KGR) at legitimate nodes. To comprehensively evaluate the efficiency of the FAS in PLKG, we propose an FAS-assisted PLKG model that integrates transmit beamforming and sparse port selection under independent and identically distributed and spatially correlated channel models, respectively. Specifically, the PLKG utilizes reciprocal channel probing to derive a closed-form KGR expression based on the mutual information between legitimate channel estimates. Nonconvex optimization problems for these scenarios are formulated to maximize the KGR subject to transmit power constraints and sparse port activation. We propose an iterative algorithm by capitalizing on successive convex approximation and Cauchy-Schwarz inequality to obtain a locally optimal solution. A reweighted $ell_1$-norm-based algorithm is applied to advocate for the sparse port activation of FAS-assisted PLKG. Furthermore, a low-complexity sliding window-based port selection is proposed to substitute reweighted $ell_1$-norm method based on Rayleigh-quotient analysis. Simulation results demonstrate that the FAS-PLKG scheme significantly outperforms the FA-PLKG scheme in both independent and spatially correlated environments. The sliding window-based port selection method introduced in this paper has been shown to yield superior KGR, compared to the reweighted $ell_1$-norm method. It is shown that the FAS achieves higher KGR with fewer RF chains through dynamic sparse port selection.

Problem

• Enhancing physical-layer key generation rate using fluid antenna systems
• Optimizing transmit beamforming and sparse port selection for secure communication
• Achieving higher key rates with fewer RF chains via dynamic antenna configuration

Innovation

• FAS enables adaptive port selection
• Iterative algorithm optimizes key generation
• Sliding window method enhances efficiency