Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing mobile multi-users due to the constantly shifting CSI distributions with user movements. To address this issue, we propose a Temporal Dynamic Graph Convolutional Network (TDGCN)-based PLA scheme, which employs Graph Neural Networks (GNNs) to capture the spatio-temporal dynamics induced by user movements. Firstly, we partition CSI fingerprints into multivariate time series and utilize dynamic GNNs to capture their associations. Secondly, Temporal Convolutional Networks (TCNs) handle temporal dependencies within each CSI fingerprint dimension. Additionally, Dynamic Graph Isomorphism Networks (GINs) and cascade node clustering pooling further enable efficient information aggregation and reduced computational complexity. Simulations demonstrate the proposed scheme's superior authentication accuracy compared to seven baseline schemes.

The existing works on holographic MIMO are mainly based on the time encoding (TE) scheme. Since the continuous aperture of holographic MIMO is able to capture both the temporal and the spatial variation of electromagnetic waves, we propose a space-time encoding (STE) scheme, which relies on the orthogonal basis function representation of the spatial-temporal EM waves. From the perspective of electromagnetic information theory, we derive the achievable information rate of the STE scheme in the narrowband communication systems and prove that the STE scheme achieves a higher information rate than the TE scheme. Firstly, We build the transmission model of the STE scheme based on electromagnetic information theory and investigate the characteristics of the model, including the blocklength of codewords and the signal-to- noise ratio. Specifically, the blocklength is determined through proving the eigenvalue distribution of the space-time-wavenumber-frequency limited operator and the signal-to-noise ratio is obtained based on proposed noise model. Then we derive the achievable information rate of both the STE scheme and the TE scheme by employing the finite blocklength information theory. Closed-form approximations of the rates are further derived, based on which we prove the conclusion that the STE scheme achieves a higher information rate than the TE scheme while utilizing the same spatial and temporal resources. Numerical results verify the accuracy of the approximations and indicate that the STE scheme improves the information rate by 7.96% over the TE scheme.

With the explosive growth of communication requirements for real-time intelligent tasks, mobile communication is shifting from the traditional communication to task-oriented communication, where the transmitted data is shifting from undifferentiated transmission to value-oriented transmission. To maximize the value of transmitted data, it is urgent to match the source decisions with the task demands and wireless channel state. In this article, we focus on the joint source-channel optimization problem in task-oriented communication, and we design the timeliness-accuracy degradation (TAD) metric to measure the value of transmitted source. Moreover, we design a source value-based resource allocation scheme to minimize the TAD through joint optimization of task data generation and compression strategies, bandwidth allocation, and transmit power selection. Furthermore, to avoid the curse of dimensionality, we propose dimension-refined reinforcement learning (DRRL) algorithm to obtain the optimal solution of the problem in a stable and low-complexity manner. Numerical results demonstrate that the designed scheme can effectively improve the task performance and verify the low complexity and stability of the algorithm.

The wireless control system (WCS) operating with massive ultra-reliable and low-latency communications is viewed as a promising technology for the Industrial Internet of Things (IIoT). However, the co-design of sensing, control, and communications is full of challenges, and the trade-off between reliability and control performance in a closed-loop WCS under blind areas of the wireless network’s coverage is still to be solved. In this paper, we exploit reconfigurable intelligence surface (RIS) to assist the device located in the blind areas of the wireless network’s coverage in transmitting sensing and control information over the wireless channels. Furthermore, we formulate a joint optimization of reliability and control cost for a RIS-assisted closed-loop WCS. Specifically, we apply a linear quadratic regulator (LQR) cost to measure the control performance, and a trade-off performance metric called reliability-to-control efficiency (RCE) is proposed for the WCS. In addition, we maximize the minimum RCE of IIoT devices while meeting the requirements of reliability, control cost, and communication resources. An alternating optimization-based maximum the minimum RCE algorithm (AO-MmRCEA) is formulated to jointly optimize the transmission power, transmission time, beamforming, and reflecting coefficients of RIS. The convergence and complexity of the proposed AO-MmRCEA algorithm are analyzed. Simulation results demonstrate the convergence and effectiveness of the proposed AO-MmRCEA algorithm for closed-loop WCS.

The continuous aperture of Holographic MIMO enables us to encode and transmit information spatially. This paper investigates the achievable rate of linear Holographic MIMO with arbitrary aperture-length using the finite blocklength information theory. Specifically, we first employ the prolate spheroidal wave functions to expand the received wavenumber band-limited electromagnetic field. This orthogonal representation enables two schemes to convey information related to the normal additive white Gaussian noise (AWGN) channel and the non-normal AWGN channel, namely the NA and NNA schemes, respectively. Then we derive the accurate achievable rates and the converse bounds of the two schemes by extending the$\kappa \beta $bound in finite blocklength information theory. Moreover, we derive an approximate closed-form expression of the achievable rate in the large aperture-length regime based on normal approximation. The approximation indicates that for a given space efficiency, the error probability decreases rapidly as the aperture length L increases, with the rate of decline determined by$Q\left ({{O\left ({{\sqrt {L}}}\right)}}\right)$. Finally, we obtain the asymptotic results when the aperture-length tends to infinity. Numerical results demonstrate that the NA scheme outperforms the NNA scheme when the blocklength is small, while the NNA scheme excels in the large blocklength regime. The accuracy of the approximation and the validity of the asymptotic results are verified.

This paper investigates the uplink secure transmission in an intelligent reflecting surface (IRS) aided Cell-Free Multiple Input Multiple Output network. To maximize the minimum secrecy rate (SR) among legitimate users, we jointly optimize the uplink power control vector and the passive beamforming vector at IRS with consideration of resource allocation fairness. We propose an alternating optimization based SR max-min fairness algorithm to solve the non-convex problem. Based on semidefinite relaxation, the sub-problem of phase optimization at IRS is solved. Geometric programming is utilized to handle the optimization of power control with the assist of condensation method. Simulation results verify that the proposed algorithm can converge to obtain the solution. The minimum SR of the proposed scheme is increased by 14% compared with random phase scheme and the max-min fairness among users is realized.

Key generation rate and bit disagreement ratio are two main indicators to evaluate the performance of physical layer key generation (PLKG). This work explores the key generation performance in reconfigurable intelligent surface (RIS)-assisted PLKG, where a concrete scheme of key generation is proposed. Specifically, we first deduce the key generation rate expression based on estimation theory in RIS-assisted PLKG. Then we optimize the RIS reflecting coefficients with instantaneous channel state information to maximize the key generation rate. Finally, aiming at minimizing transmit power while guaranteeing key generation rate target, we formulate a power minimization problem in RIS-assisted PLKG. An alternating optimization algorithm is applied to solve the non-convex mixed-integer nonlinear programming. Simulation results demonstrate that the key generation rate of the proposed scheme is up to 197.5% higher than the existing relay-assisted scheme, and we successfully reduce the required transmit power by at least 6.6 dBm than without power minimization scheme.