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.
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.
As a promising physical layer security technique, physical layer key generation (PLKG) enables legitimate users to obtain secret keys from wireless channel without security infrastructures. However, in harsh propagation environments, the channel characteristic becomes unsatisfactory, the key generation rate (KGR) is significantly deteriorated. In this paper, we propose a novel fluid antenna (FA) enabled PLKG system to address this challenge. Specifically, we first derive the closed-form expression of the KGR for FA array, and then jointly optimize the precoding matrix and the antenna positions via a particle swarm optimization (PSO) algorithm. Next, to further reduce the computational complexity of the optimization procedure, we develop an alternating optimization (AO) algorithm, which combines the projected gradient descent (PGD) and the PSO. Simulation results demonstrate that by exploiting the additional spatial degree of freedom (DoF), our FA enabled PLKG system is superior to the benchmarks, such as the conventional fixedposition antenna (FPA) array and the reconfigurable intelligent surface (RIS). It is worth highlighting that compared to the conventional uniform planar antenna (UPA), the FA enabled PLKG achieves a 35.42 % KGR performance improvement under PSO algorithm and a 67.73 % KGR performance improvement under AO algorithm, respectively.
This paper investigates the performance of physical layer security (PLS) in fluid antenna-aided communication systems under arbitrary correlated fading channels. In particular, it is considered that a single fixed-antenna transmitter aims to send confidential information to a legitimate receiver equipped with a planar fluid antenna system (FAS), while an eavesdropper, also taking advantage of a planar FAS, attempts to decode the desired message. For this scenario, we first present analytical expressions of the equivalent channel distributions at the legitimate user and eavesdropper by using copula, so that the obtained analytical results are valid for any arbitrarily correlated fading distributions. Then, with the help of Gauss-Laguerre quadrature, we derive compact analytical expressions for the average secrecy capacity (ASC), the secrecy outage probability (SOP), and the secrecy energy efficiency (SEE) for the FAS wiretap channel. Moreover, for exemplary purposes, we also obtain the compact expression of ASC, SOP, and SEE by utilizing the Gaussian copula under correlated Rayleigh fading channels as a special case. Eventually, numerical results indicate that applying the fluid antenna with only one activated port to PLS can guarantee more secure and reliable transmission, when compared to traditional antenna systems (TAS) exploiting maximal ratio combining (MRC) and antenna selection (AS) under selection combining (SC).
Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.
Fluid antenna systems (FAS) have recently emerged as a promising technology for future wireless networks due to their ability to access a large number of channel realizations using a single radio-frequency chain. This capability creates new opportunities for physical-layer security beyond conventional beamforming and artificial-noise approaches. In this paper, we propose a novel Fluid Antenna Secure Channel-driven Constellation (FASCC) framework for downlink communications, in which information is conveyed through channel-domain constellation points generated by FAS port selection and phase adaptation. Two complementary FASCC schemes are developed, depending on the availability of eavesdropper's channel state information. Numerical results demonstrate that the proposed FASCC framework achieves significant secrecy gains in terms of bit error rate and mutual-information secrecy performance, even when Eve is significantly closer to the base station than Bob.
Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS precoding matrix and the IRS reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.
Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS's precoding matrix and the IRS's reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.