ABSTRACT Integrated sensing and communication (ISAC) is a pivotal technology for sixth‐generation (6G) networks. Intelligent reflecting surfaces (IRS), particularly the simultaneous transmitting and reflecting IRS (STAR‐RIS), enable dynamic channel control to enhance ISAC performance. However, conventional passive STAR‐RIS is constrained by limited signal gain. While the emerging active STAR‐RIS addresses this limitation via signal amplification, it introduces heightened power consumption and security risks, especially when sensing targets act as potential eavesdroppers. This paper investigates the secure transmission problem in an active STAR‐RIS‐aided ISAC downlink system. Our objective is to maximize the sum secrecy rate by jointly optimizing the base station beamforming vectors, the sensing signal covariance matrix and the coefficients of the active STAR‐RIS for both reflection and transmission, while satisfying practical constraints on power and sensing performance. To solve this non‐convex problem, we propose an efficient two‐layer alternating optimization algorithm that decomposes it into tractable subproblems. These subproblems are solved using semidefinite relaxation and a novel eigenvalue‐penalty‐based method. Numerical simulations demonstrate that the proposed active STAR‐RIS scheme significantly outperforms baseline architectures (passive STAR‐RIS, active RIS and passive RIS) in achieving a superior balance between communication security and sensing capability.

Driven by the escalating demand for extensive coverage and ultra-reliable communication in the Internet of Vehicles (IoV), conventional single-transmission technologies frequently encounter a severe ”bottleneck effect” in long-distance and heavily obstructed environments. To break through this limitation, this paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted cascaded amplify-and-forward (AF) and decode-andforward (DF) relay system over independent and identically distributed (i.i.d.) Nakagami-mfading channels. The proposed architecture innovatively exploits the synergistic advantages of cascaded active relays (providing initial power amplification via AF and terminal signal regeneration via DF) alongside the ”passive array gains” from the RIS, thereby effectively overcoming the severe ”double path loss” inherent in passive links while suppressing accumulated noise. Addressing the challenges of prohibitive pilot overhead and latency associated with acquiring instantaneous channel state information (CSI) in highly dynamic IoV environments, we propose a statistical CSI-based adaptive power allocation algorithm utilizing particle swarm optimization (PSO). By jointly optimizing the transmit power allocation among the source node, the AF relay, and the DF relay, the proposed algorithm efficiently tackles the intricate non-convex optimization problem subject to a total power constraint. Theoretically, we derive exact closed-form expressions for the system outage probability (OP) and average channel capacity, establishing a rigorous theoretical evaluation framework. Extensive simulation results not only validate the accuracy of the theoretical analysis but also demonstrate that the proposed PSO strategy effectively exploits spatial diversity gains and RIS passive beamforming gains. The findings reveal that the scheme significantly enhances transmission reliability and system robustness in complex propagation environments, substantiating its application potential for Intelligent Transportation Systems (ITS).

Relay-based communication has become a key approach to meeting the growing demands for low latency and high reliability links in intelligent transportation and vehicle-to-everything (V2X) systems. In complex urban environments such as roads, tunnels, and dense high-rise building areas, traditional vehicle-to-vehicle (V2V) relay links are severely impeded by deep fading and multiple interference sources, which significantly degrade end-to-end performance. To enhance relay-based transmission under such harsh conditions, this paper investigates a dual reconfigurable intelligent surface (RIS)-assisted decode-and-forward (DF) relay V2V system as a representative relay-enhanced architecture. By combining two reconfigurable intelligent surfaces with a DF relay, the proposed scheme strengthens both hops of the relay link, effectively alleviating the performance bottlenecks commonly encountered in single-RIS or traditional relay schemes. The system adopts a Nakagami-mfading channel model and explicitly considers the aggregated interference at the relay and destination nodes. Based on this, we derive analytical expressions for the end-to-end outage probability and average channel capacity, employing Fox’s H function and the Gaussian-Laguerre quadrature method for precise evaluation. Additionally, an adaptive RIS reflection unit allocation algorithm is proposed to jointly optimize the total number of RIS units and their two-stage distribution under reliability constraints, thereby enhancing the efficiency of relay-based communication while reducing hardware deployment costs.

In this paper, we pay our attention towards secure and robust communication in the presence of a Reconfigurable Intelligent Surface (RIS)-enhanced mobile eavesdropping attacker in Multiple-Input Multiple-Output (MIMO) wireless networks. Specifically, we first provide a unifying framework that generalizes specific intelligent wiretap model wherein the passive eavesdropper configured with any number of antennas is potentially mobile and can actively optimize its received signal strength with the help of RIS by intelligently manipulating wiretap channel characteristics. To effectively mitigate this intractable threat, we then propose a novel and lightweight secure communication scheme from the perspective of information theory. The main idea is that the data processing can in some cases be observed as communication channel, and a random bit-flipping scheme is then carefully involved for the legitimate transmitter to minimize the mutual information between the secret message and the passive eavesdropper’s received data. The Singular Value Decomposition (SVD)-based precoding strategy is also implemented to optimize power allocation, and thus ensure that the legitimate receiver is not subject to interference from this random bit-flipping. The corresponding results depict that our secure communication scheme is practically desired, which does not require any a prior knowledge of the eavesdropper’s full instantaneous Channel State Information (ICSI). Perfect acquisition of ICSI is clearly always not affordable, which is further exacerbated by the RIS involved and the potential mobility of the passive eavesdropper that leads to unavoidable fast fading channels. Furthermore, we consider the RIS optimization problem from the eavesdropper’s perspective, and provide RIS phase shift design solutions under different attacking scenarios. Finally, the optimal detection schemes respectively for the legitimate user and the eavesdropper are provided, and comprehensive simulations are presented to verify our theoretical analysis and show the effectiveness and robustness of our secure communication scheme across a wide range of attacking scenarios.

Addressing the problem of complex communication in intelligent transportation system (ITS) under aggregated interference, it is clear that reconfigurable intelligent surface (RIS)-assisted communication is an advantageous solution to the problem of high energy consumption, due to its low-cost and sustainable features. We first propose a transmission protocol architecture of dedicated short range communication (DSRC) compatible mode under cellular-vehicle to everything (C-V2X), and a transmission selection scheme, which selects whether to perform RIS-assisted communication or not based on the requirement of real-time outage probability. Next, single-RIS-assisted and multi-RIS-assisted ITS models are proposed separately, and precise expressions for outage probability, bit error rate, and channel capacity are successfully derived. Finally, we provide sufficient Monte Carlo simulations and analysis of the proposed systems, and the simulation results verified the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

With the continuous advancement of wireless technologies, the design of cost-effective and efficient communication solutions to support large-scale Internet of Vehicles (IoV) systems has become increasingly critical. Reconfigurable Intelligent Surfaces (RIS), a cutting-edge technology in wireless communication, presents a promising solution to this challenge. This paper focuses on leveraging RIS to address the key obstacles encountered in vehicle communication. We propose an IoV communication system in which RIS is employed as the transmitter, operating under Nakagami-m fading channels. The basic system model consists of a radio frequency (RF) transmitter and an RIS, both serving as the transmission source. The signal is transmitted to a relay, which subsequently forwards it to multi-antenna-equipped vehicles via an amplify-and-forward (AF) scheme. We provide a thorough performance analysis of the system, deriving expressions for the outage probability and the average channel capacity. Furthermore, we validate the theoretical results through extensive simulations. Our findings suggest that RIS has the potential to significantly enhance the performance of IoV systems.

In this paper, we propose two vehicular communication system models based on Reconfigurable Intelligent Surface (RIS)-assisted sources. Specifically, we assume two scenarios: RIS-Relay aided RIS-assisted source and Relay aided RIS-assisted source, where the relay employs the Power Splitter (PS) to divide the received signal into information transmission and energy harvesting parts. Moreover, both scenarios consider whether a direct link exists. We derive in detail precise approximate closed-form expressions for the outage probability in various vehicular communication scenarios under Nakagami-m channels, and conduct asymptotic analysis of the outage probability to gain further insights. Finally, we validate all the analytical results through simulations, demonstrating the accuracy of the expressions we derived. The simulation results indicate that system and channel parameters significantly influence system performance. Additionally, through comparison, we find that the system with RIS-assisted source and RIS-Relay outperforms system where the RIS is used solely as transmitter or relay, as well as the system with RIS-assisted source and energy harvesting relay.

Aiming to solve the problem of interruption of normal communication service caused by the damage of ground communication facilities after disaster, an Air-Ground Integrated Mobile Edge Network (AWMEN) offloading model was established under the constraints of communication security, energy consumption and coverage. The traditional method needs to be re-iterated every time the preset environmental state changes, which will waste a lot of communication resources and computing resources, greatly reduce the efficiency, and face the risk of data privacy disclosure. However, the deep reinforcement learning method under the federated learning framework will be more flexible and applicable to dynamic scenarios. A Markov decision process model is constructed based on the unmanned aerial vehicles (UAV) and the environment. The experience trajectory is designed by interacting with the external environment, and the optimal offloading strategy is obtained. The Twin Delayed Deep Deterministic Policy Gradient of behavior cloning (TD3-BC-R) is compared with baseline method (0-1 mode), Actor-Critic (AC-R), Deep Deterministic Policy Gradient (DDPG-R) and Twin Delayed Deep Deterministic Policy Gradient (TD3-R), the experiment shows that, The total time cost of TD3-BC-R is reduced by more than 1/3, and low latency transmission is also achieved.

The rapid development and continuous innovation of wireless services have led to a boom in the number and types of smart terminals. The huge amount of data that deep learning needs to calculate relies on the continuous improvement of hardware devices to solve. Therefore, the realization of intelligent transportation system(ITS) has become the general trend. The increase in the number of Internet of Things devices and the changes in the location of vehicles in the Internet of Vehicles(IOV) system have put forward higher requirements for the reliability and effectiveness of information transmission and the effective use of spectrum resources. Aiming at the influence and elimination of aggregated interference in intelligent transportation system, an anti-fragile communication algorithm is proposed to improve the reliability of signal transmission. At the same time, the outage probability of energy harvesting and cognitive radio technology enhanced with relay cooperative transmission under cognitive wireless network system with the aggregate interference can be deduced in detail. Finally, the correctness of theoretical analysis and the reliability of the proposed anti-fragile communication algorithms are verified by simulation experiments.

The Internet of Vehicles (IoV) has always attracted attention as the emerging communication network with the most development potential in the 5G era. However, the performance of IoV under 5G ultra-dense networks is an open issue, especially in practice the outage probability and ergodic capacity of the relay cooperative IoV network under aggregate interference are still unclear. Therefore, an opportunistic Decoding and Forwarding (DF) relay cooperative transmission algorithm was proposed in this paper when the destination node of IoV has aggregated interference. In addition, based on mathematical theoretical knowledge such as numerical analysis, the closed expressions of the outage probability and ergodic capacity of the IoV system under aggregated interference was derived. Finally, simulation experiments verify the effectiveness of the proposed scheme and the correctness of the theoretical analysis, which improves the transmission rate of the system.