Fluid Antenna System-Assisted Physical Layer Secret Key Generation

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 (RF) chains, the FAS effectively enables adaptive antenna port selection by exploiting channel spatial correlation to enhance the secret key rate (SKR) at legitimate nodes. To comprehensively evaluate the performance 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 (i.i.d.) and spatially correlated channel models, respectively. Specifically, the PLKG utilizes reciprocal channel probing to derive an approximate SKR expression based on the mutual information between legitimate channel estimates, explicitly accounting for the Eve’s channel observation under spatially correlated channel scenarios. Nonconvex optimization problems for these scenarios are formulated to maximize the SKR 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 ℓ1-norm-based algorithm is applied to advocate for the sparse port activation of FAS-assisted PLKG. To approximate the optimal activated ports obtained by exhaustive search, a low-complexity sliding window-based port selection is proposed to substitute reweighted ℓ1-norm method based on Rayleigh-quotient analysis. Simulation results demonstrate that the FAS-assisted PLKG scheme significantly outperforms fixed antenna-assisted PLKG schemes in both environments. It is shown that the FAS achieves higher SKR with fewer RF chains through dynamic sparse port selection, which effectively reduces the resource overhead. Also, the sliding window approach closely approximates the globally optimal port selection compared to the reweighted ℓ1-norm method, rendering it suitable for practical deployments.