In next-generation ultra-reliable and low-latency communications (xURLLC), short-packet communications (SPC) pose challenges to the security and timeliness. Additionally, the stochastic characteristics of wireless channels and imperfect channel state information (CSI) further exacerbate these challenges. Thus, the trade-off between security and timeliness under statistical quality-of-service (QoS) provisioning (SQP) is crucial. To address these issues, this paper investigates the physical security and timeliness based SQP for reconfigurable intelligent surface (RIS)-assisted short-packet rate-splitting multiple access (RSMA) in xURLLC systems. Specifically, closed-form expressions for the secrecy age outage probability (SAOP) and decoding error probability (DEP) are derived considering imperfect CSI and successive interference cancellation (SIC) based on stochastic network calculus (SNC). Under blocklength and information leakage constraints, we propose a meta asynchronous advantage actor critic (MA3C) algorithm based on mean-field approximation (MFA) theory (MFA-MA3C) to minimize SAOP and DEP through multi-objective optimization. The algorithm utilizes the mean of parameter gradients to update global network parameters. Simulation results demonstrate that, compared to baseline schemes, the proposed framework significantly improves system stability and convergence, while achieving a trade-off between security and timeliness in xURLLC systems.

With the evolution of communication systems toward the sixth‐generation technology (6G), intelligent cognitive communication has gained considerable attention. As an important part of intelligent cognitive communication, cognitive radio (CR) offers promising prospects for efficient spectrum utilization. However, with the introduction of cognitive capabilities, CR networks (CRNs) face not only common security threats in wireless systems, but also unique security threats, including primary user emulation (PUE) attacks, endangering communication reliability and confidentiality. In order to enhance the defense ability of CRNs against PUE attacks, this paper proposes an integrated sensing and communication (ISAC)‐assisted approach. Leveraging ISAC technology, our scheme enhances location detection precision. We introduce a high‐resolution perception signal parameter estimation method and a position‐based identity authentication scheme. Furthermore, deep reinforcement learning is used to dynamically optimize the authentication threshold to ensure the stability of authentication in dynamic scenarios. Simulation results show that the proposed scheme is effective in resisting PUE attacks and improves the security and reliability of CRNs.

Aiming to address the security and timeliness challenges in reconfigurable intelligent surface (RIS)‐assisted integrated sensing and communication (ISAC) system with finite blocklength (FBL), this paper jointly investigates the communication security, sensing security, and information freshness performance of the system in the presence of communicating eavesdropper and sensing eavesdropper. Specifically, based on statistical channel state information (CSI), approximate closed‐form expressions for secrecy throughput, average age of information (AoI), and channel parameter estimation errors are derived and analyzed to characterize the performance of communication security, information freshness, and sensing security. The asymptotic analyses between secrecy throughput and blocklength, number of antennas, and number of RIS reflecting elements are established. Furthermore, an optimization problem for maximizing sum secrecy throughput is established under the timeliness, sensing security, transmit power, and RIS unit modulus constraints. To handle the intractable stochastic nonconvex problem, a joint alternating optimization method based on noncooperative game and stochastic successive convex approximation (NCG‐SSCA) is proposed by jointly designing RIS phase shift, transmit beamforming vector, sensing signal covariance, and blocklength. Simulation results validate our theoretical derivations and conclusions in the performance analysis. It is also shown that compared with SSCA and stochastic gradient descent (SGD) methods, the NCG‐SSCA method proposed in this paper achieves an increase in sum secrecy throughput by 10.4% and 16.3% with faster convergence speed.

This paper investigates the secure and fresh beamforming design for reconfigurable intelligent surface (RIS) assisted multi-user multiple-input-single-output (MISO) system with finite blocklength (FBL). Specifically, to simultaneously guarantee the security and timeliness requirements, we employ the moment generating functions in stochastic network calculus to derive an approximate closed-form expression for the upper bound of secrecy age outage probability (SAOP) under statistical channel state information (CSI). The beamforming design problem is formulated to minimize sum SAOP under constraints of reliability, information leakage, power budget and unit modulus. To solve the intractable problem, we propose an alternating optimization algorithm based on semi-definite relaxation (SDR) and momentum stochastic successive convex approximation (M-SSCA), where SDR is used for a quadratically constrained quadratic programs problem to optimize transmit beamforming vector, M-SSCA is utilized for a stochastic non-convex problem to optimize RIS phase shift. Simulation results validate the derived expressions and our proposed algorithm. It is shown that compared with stochastic successive convex approximation (SSCA) and stochastic gradient descent (SGD) methods, the proposed optimization algorithm achieves a reduction in sum SAOP with faster convergence speed.

This paper addressed the trade‐off between timeliness and reliability in joint communication and over‐the‐air computation offloading (JCACO) system under short‐packet communications (SPCs). The inevitable decoding errors introduced by SPC lead to errors in the data aggregation process of over‐the‐air computation (AirComp). Due to limited resources, pursuing high reliability may prevent the JCACO system from meeting delay requirements, resulting in a trade‐off between reliability and timeliness. To address this issue, this paper investigates the timeliness and reliability of the JCACO system. Specifically, the moment generating function method is used to derive the delay outage probability (DOP) of the JCACO system, and the outage probability of AirComp is calculated based on the errors that occur during its data aggregation process. The paper established an asymptotic relationship between blocklength, DOP, and AirComp outage probability (AOP). To balance timeliness and reliability, an AOP minimization problem is formulated under constraints of delay, queue stability, and limited resources based on computation offloading strategies and beamformer design. To overcome the issues of slow convergence and susceptibility to local optima in traditional algorithms, this paper proposed a stochastic successive mean‐field game (SS‐MFG) algorithm. This algorithm utilizes stochastic continuous convex approximation methods to leverage Nash equilibria among different users, achieving faster convergence to the global optimal solution. Numerical results indicate that SS‐MFG reduces AOP by up to 60%, offering up to a 20% improvement in optimization performance compared to other algorithms while also converging faster.

Accurate automated extraction of coseismic deformation from Synthetic Aperture Radar (SAR) data can be challenging owing to interference from inherent atmospheric noise. Particularly, the limited displacement of small-to-moderate earthquakes (Mw<6.5) can easily be obscured by phase errors and/or noise. To address this issue, we developed an autoencoder model based on a deep learning framework (i.e., Pytorch) to automate the accurate extraction of coseismic displacement from Interferometric SAR (InSAR) interferograms. We constructed a training dataset using simulated interferograms. Our trained model performed well for interferograms with real noise. When applied to worldwide real earthquakes of various rupture styles, the model produced clear coseismic displacement with less noise and a better fit to coseismic fault models compared to the differential InSAR method without noise correction. Additionally, it achieved co-seismic deformation similar to popular InSAR time series and GNSS methods. The approach will enhance the proceduralization and popularization of InSAR applications in earthquake monitoring, providing improved constraints on the kinematic characteristics of earthquakes.

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) can effectively improve the computing ability and energy sustainability of energy-constrained wireless devices in the Internet of Things (IoT) networks. Intelligent reflecting surface (IRS) has recently emerged as an effective technique to improve the performance of wireless systems by intelligently reconfiguring wireless environments. This paper studies the exploitation of IRS to improve the secure computation performance of WPT-MEC systems with a passive eavesdropper. A wireless access point (AP) first charge multiple users with the emitted energy signals, and then the users perform local computing and partial offloading to complete their computation tasks with the harvested energy in the presence of an eavesdropper, where the local computing can be executed during the whole process of WPT and offloading. Meanwhile, deploying IRS can improve the energy capture and secure offloading performance of the users. We maximize the secure computation task bits of users by jointly optimizing the AP energy transmit beamforming, the IRS phase shifts, the transmit power, users’ offloading time, and the local computation frequency of users, which are tangled with each other. An iterative optimal algorithm is developed to solve this non-convex problem by combining Taylor expansion method, semidefinite relaxation (SDR) algorithm, the Lagrange duality theory and Karush-Kuhn-Tucker (KKT) conditions. The numerical results show that the proposed scheme can effectively increase the secure computation task bits compared with other benchmark schemes, especially for the maximum transmit power of AP, the improvement is above 45$\%$.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) have attracted much attention as promising technologies for the next-generation mobile networks and Internet of Things (IoT). In this article, we investigate how to improve the security of the MEC system with the assistance of IRS and artificial noise (AN) in the IoT. By adjusting the phase of the IRS, the users’ signals can be enhanced and the eavesdroppers’ signal can be weakened. In addition, the full-duplex base station (FD-BS) transmits AN to destroy the eavesdroppers’ signal and further enhance the users’ security. We minimize the users’ secure energy consumption by jointly optimizing the base station receive beamforming vectors, AN covariance matrix, IRS phase shifts, users’ offloading time, transmit power, and local computation tasks. The formulated problem is a nonconvex problem that is hard to solve directly, so we decompose it into tractable subproblems and develop an alternating optimization approach by combining the semidefinite relaxation (SDR) algorithm and Dinkelbach’s method. The results show that the proposed scheme can greatly reduce the secure energy consumption compared with other benchmark scheme.

Wireless communications are vulnerable to cyber attackers, which now have the flexibility to choose their type of attack. In this paper, combined with intelligent reflect surface (IRS), we jointly optimize base station beamforming and IRS reflected beamforming to counter smart attackers, thereby improving system security. Considering that attackers can flexibly choose their attack methods, such as jamming or eavesdropping, we make the base station intelligent by using reinforcement learning, which can predict the attack methods of attackers and choose whether to add artificial noise into the transmitted signals. At the same time, the interaction between the base station and the smart attackers are established as a non-cooperative game, the Nash equilibrium of the game is derived. Based on this, the base station anti-smart attackers strategy based on Deep Q-learning (DQN) is proposed, which can restrain the attack of the attacker to improve the security of the system. It can be verified from the simulation results that the proposed anti-smart attackers strategy can effectively enhance the secrecy rate of the wireless communication system, resist the attacker’s attack, and intelligently transmit artificial noise to improve system security.

This letter investigates the proactive eavesdropping for multiple suspicious links either through interfering or assisting the links. Considering the power constraint at eavesdropper, our objective is to maximize weighted sum eavesdropping rate of multiple suspicious links via jointly optimizing their intervention strategies (jamming or relaying) and the corresponding transmit power at eavesdropper. The formulated problem is shown to be a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard in general. By identifying the separable structure of the formulated problem, we decouple the complex MINLP problem into two subproblems: 1) a jamming subproblem; and 2) a relaying subproblem. These two subproblems are then solved by further recasting them into a combinational problem and a typical concave optimization problem, respectively. Numerical simulations show that our proposed approach can achieve higher eavesdropping rate than conventional eavesdropping approaches.

The distribution of the base stations (BSs) and users is mostly designed as a two‐dimensional model in the study about heterogeneous networks (HetNets), which is not suitable for ultra‐dense network scenarios. Meanwhile, the eavesdroppers existing in vertical dimension directly reduce the physical layer security of the HetNets. To tackle the mentioned problem, we propose to adjust the vertical dimension of the full‐dimension (FD) antenna placed in the BSs; then the signal‐to‐noise ratios (SNRs) of the legitimate users and eavesdroppers are given due to the tilted angle. According to the three‐dimensional Poisson point process (PPP) of BSs and users, the cumulative distribution function of SNR is deduced, which derives the closed expression of average security rate. The simulation results verify the correctness of the closed expression and the feasibility of proposed scenarios that the physical layer security performance can be improved by adjusting the vertical tilt angle. And density ratio of the BSs can be obtained in the various specific scenes.

A new method about renewable energy cooperation among small base stations (SBSs) is proposed, which is for maximizing the energy efficiency in ultradense network (UDN). In UDN each SBS is equipped with energy harvesting (EH) unit, and the energy arrival times are modeled as a Poisson counting process. Firstly, SBSs of large traffic demands are selected as the clustering centers, and then all SBSs are clustered using dynamic k-means algorithm. Secondly, SBSs coordinate their renewable energy within each formed cluster. The process of energy cooperation among SBSs is considered as Markov decision process. Q -learning algorithm is utilized to optimize energy cooperation. In the algorithm there are four different actions and their corresponding reward functions. Q -learning explores the action as much as possible and predicts better action by calculating reward. In addition, ε greedy policy is used to ensure the algorithm convergence. Finally, simulation results show that the new method reduces data dimension and improves calculation speed, which furthermore improves the utilization of renewable energy and promotes the performance of UDN. Through online optimization, the proposed method can significantly improve the energy utilization rate and data transmission rate.