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arXiv:2509.00018 · International Conference on Wireless Communications and Signal Processing (WCSP)

A Fluid Antenna Enabled Physical Layer Key Generation for Next-G Wireless Networks

Jiacheng Guo, Ning Gao, Yiping Zuo, Hao Xu, Shi Jin, Kai‐Kit Wong

Published Oct 23, 2025• 1 citation
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Abstract

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.

DOI
10.1109/wcsp68525.2025.1010537

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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.
Physical layer key generation (PKG) technology leverages reciprocal channel randomness to generate shared secret keys. However, multipath fading at the receiver may degrade the correlation between legitimate uplink and downlink channels, resulting in a low key generation rate (KGR). In this paper, we propose a PKG scheme based on the pattern-reconfigurable antenna (PRA) to boost the secret key capacity. First, we propose a reconfigurable intelligent surface (RIS) based PRA architecture with the capability of flexible and reconfigurable antenna patterns. Then, we present the PRA-based PKG protocol to improve the KGR via mitigation of the effects of multipath fading. Specifically, a novel algorithm for estimation of the multipath channel parameters is proposed based on atomic norm minimization. Thereafter, a novel optimization method for the matching reception of multipath signals is formulated based on the improved binary particle swarm optimization (BPSO) algorithm. Finally, simulation results show that the proposed scheme can resist multipath fading and achieve a high KGR compared to existing schemes. Moreover, our findings indicate that the increased degree of freedom of the antenna patterns can significantly increase the secret key capacity.
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.
With the rapid development of 6G technologies, extremely large-scale reconfigurable intelligent surfaces (XL-RIS) have been introduced to enhance spatial diversity and improve signal coverage. Meanwhile, physical-layer key generation (PKG) has emerged as a promising, quantum-resistant solution for securing wireless communications. However, the deployment of XL-RIS significantly expands the near-field region, making traditional far-field planar wave models inadequate for PKG. To address this limitation, this paper proposes an XL-RIS-assisted PKG model based on near-field spherical wave propagation theory. We further derive a expression for the legitimate key generation rate (KGR) under passive eavesdropping attacks. To maximize the achievable KGR, we propose a novel alternating optimization(AO) algorithm ADA that combines Dinkelbach and the Alternating Direction Method of Multipliers (ADMM) to jointly optimize the base station (BS) beamforming vector and RIS reflection coefficients in an iterative manner. Simulation results indicate that the spatial resolution gain provided by the near-field channel helps enhance the independence between the legitimate and eavesdropping channels. The ADA demonstrates robust convergence. Notably, it maintains superior performance even when the eavesdropper and the legitimate user share the same azimuth angle.
In widely deployed Internet of Things (IoT) scenarios, physical-layer key generation (PLKG) serves as a useful complement to conventional cryptographic methods, yet it often suffers from a fundamentally low key generation rate, which becomes particularly severe in quasi-static environments. This low rate is mainly attributed to three key issues: (1) slow channel variations, which provide insufficient randomness and thus limit the key generation rate; (2) correlation between the legitimate channel and the eavesdropping channel, which reduces the uniqueness of the extracted key and further degrades the achievable rate; and (3) insufficient degrees of freedom in the key source, which constrain the key space. To address these challenges, this paper introduces the Dynamic Agile Reconfigurable Intelligent Surface Antenna into physical-layer key generation. By deploying metasurface antennas at both ends and independently applying random phase modulation, the scheme injects two-sided randomness, thereby mitigating the adverse effects of quasi-static channels and legitimate eavesdropper channel correlation. Moreover, by leveraging the dynamic, agile, and reconfigurable characteristics of the metasurface antennas in the key generation process, the proposed approach can further enhance the key generation rate while simultaneously resolving all three issues above. The proposed scheme is developed under a general setting where correlation exists between the legitimate and eavesdropping channels. A closed-form expression for the key capacity is rigorously derived, accompanied by detailed theoretical analysis and simulations. The results demonstrate the superiority of the proposed approach when applied to physical-layer key generation.
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.