A covert ISAC transmission scheme based on an innovative heterogeneous sub-connected hybrid analog and digital (H2AD) multiple-input multiple-output (MIMO) transceiver is investigated in this paper. Specifically, H2AD ISAC system possesses the capability to detect the point-like target while covertly transmitting confidential information to a singleantenna legitimate user, and enabling secure transmission without detection by the warden. The objective is to maximize the covert transmission rate for legitimate users while adhering to the Cramér-Rao bound (CRB) threshold. However, due to the coupling of multiple variables under the H2AD transceiver framework, the optimization problem becomes non-convex. To tackle the challenging, an alternating optimization algorithm based on Dinkelbach’s transformation and semidefinite relaxation (DTSDR) is proposed to design the analog and digital beamforming along with the sensing signal. Then, by utilizing historical system states and optimizing for long-term returns, an improved distributional soft Actor-Critic with three refinements (DSACv2) algorithm framework based on deep reinforcement learning (DRL) is proposed. Simulation results demonstrate that incorporating the novel H2AD MIMO antenna array into ISAC system design enhances the covert performance while ensuring target sensing performance.

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone programming (SOCP), penalty-dual-decomposition (PDD) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the active RIS and UAV techniques into the optimization design, the covert transmission performance of ISCC systems are improved while ensuring a certain level of target sensing and EC performance.

Unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) systems are envisioned to simultaneously provide secure data transmission and nighttime illumination. However, when buildings obstruct the optical links, both connectivity and lighting are disrupted, which may severely compromise system reliability and safety. This paper investigates an artificial noise-based physical layer security (PLS) scheme for a VLC-enabled UAV communication system in a multiuser environment with potential eavesdroppers, while explicitly incorporating awareness of shadowed area caused by blockage and enabling the UAV to autonomously adjust its trajectory to proactively avoid such shadow coverage to the ground users. We formulate a joint optimization problem of user association, power allocation, and UAV trajectory design to maximize the average secrecy rate of the system, while taking into account illumination requirements, shadowing effects, and UAV mobility. To tackle this mixed-integer and non-convex optimization problem, we decompose it into three subproblems and transform them into tractable convex forms. Furthermore, we also develop an iterative algorithm by leveraging successive convex approximation techniques under a block coordinate descent framework to efficiently obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can achieve fast convergence and improve the average secrecy rate at least by 51.1% compared with conventional schemes. Moreover, the algorithm still exhibits robustness and efficacy in exploiting the spatial-temporal trade-offs under severe eavesdropping threats and shadowing with diverse user geometries, highlighting its practicality for secure nighttime urban VLC-UAV communication.

This paper proposes a novel covert transmission framework for an unmanned aerial vehicle (UAV)-reconfigurable holographic surface (RHS)-aided full-duplex (FD) integrated sensing and communication (ISAC) system, where the aerial access point (AP) simultaneously performs target sensing and downlink covert communication. We jointly design the AP’s downlink transmit signal and uplink receive beamformers, the RHS weights, the users’ uplink transmit powers, and the UAV’s trajectory, considering imperfect knowledge of the warden’s channel state information (CSI). An optimization problem is formulated to maximize the minimum covert transmission rate (CTR) among all downlink covert users (DCUs), subject to constraints on required sensing and uplink transmission capabilities, covertness, and total power budget. To tackle the intractable non-convex problem, we leverage the Bernstein-type inequality, majorization-minimization (MM), and successive convex approximation (SCA), and propose a secure optimization framework that efficiently updates all variables using convex optimization techniques. To further understand the proposed algorithm, its convergence behavior and computational complexity are discussed. Simulation results demonstrate that integrating RHS and UAV techniques into the optimization design enhances the covert transmission performance of FD-ISAC systems while ensuring a certain level of sensing capability.

This paper proposes optimizing a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system in the vehicular networks to sense a potential eavesdropping target and transmit the confidential information to the legitimate cellular vehicular users (CVUs). An optimization problem is formulated by maximizing the sum system secrecy rate of all vehicle-to-infrastructure (V2I) links subject to waveform similarity and radar echo signal-to-interference-plus-noise ratio (SINR) constraints. To address the challenging non-convex problem, we first cast the formulated problem into an equivalent form relying on the mean-square error (MSE) technique given the overall resource budget, and then develop a sequential optimization procedure to update all optimization variables sequentially. Specifically, to solve the dual-function waveform optimization subproblem, we leverage a dual ascent approach (DAA) and the Limited-memory Broyden Fletcher Goldfarb and Shanno (LBFGS) technique. Simulation results confirm the effectiveness of the developed design method indicating a secure communication with improved performance against the state-of-the-art ISAC schemes.

An optical reconfigurable intelligent surface (ORIS) was expected to offer extra secrecy performance gain in a visible light communication (VLC) system. However, nonlinear impairments involved degrade the confidential signal reception and have not been fully considered in designing physical layer security (PLS). In this paper, a novel PLS approach is proposed for an ORIS-aided VLC system with consideration of practical nonlinear impairments. It is mathematically formulated to be an optimization problem that maximizes the signal-to-interference-plus-distortion-and-noise ratio of the legitimate link, while entirely suppressing that of multiple eavesdroppers by jointly optimizing the beamforming, jamming and clipping at the transmitters, and also the surface configuration in terms of mirror assignments and rotation angles at the ORIS. We decompose this mixed combinatorial and non-convex optimization problem into three sub-problems and elaborately transform them to be conventional convex programming, quadratic programming and nonlinear programming problems, respectively. Moreover, we also develop a time-efficient iterative approach to achieve the suboptimal solution with low-computational complexity. Simulation results demonstrate the improvement of secrecy performance as compared with conventional schemes, and also the robustness to severe nonlinear impairments and spatial correlation, thereby confirming the beneficial insights of this methodology for secure VLC with nonlinear devices.

This paper investigates an energy-efficient beamforming design for a hardware-impaired simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided integrated sensing and communication (ISAC) system, where the base station (BS) concurrently performs target sensing and multi-user communication. Accounting for hardware impairments (HWIs) at the BS, user equipments (UEs) and STAR-RIS, a joint optimization problem is posed to maximize the system energy efficiency, subject to constraints on required sensing and transmission capabilities, and the total power budget. To tackle the intractable conflicts among sensing and transmission metrics introduced by HWIs, we propose a novel learning-based method that integrates a denoising diffusion probabilistic model (DDPM) into a twin-delayed deep deterministic policy gradient (TD3) algorithm enhanced with prioritized experience replay (PER). By leveraging the DDPM and PER for beamforming policy determination, our approach accurately models the complex dynamics, achieving a better balance between sensing and communication performance. Simulation results demonstrate that the proposed PER-DDPM-TD3-based beamforming strategy achieves a 69.3% higher energy efficiency performance than the existing deep reinforcement learning (DRL)-based method.

Hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks (SUTNs) can provide maritime users with ubiquitous communication services. However, eavesdropping poses a significant challenge to the secure communications of SUTNs due to their wide-area coverage. In this paper, we propose a novel secure scheme for maritime communications, where a terrestrial-UAV integrated network coexists with marine satellite (MS) systems in the presence of an eavesdropper (Eve). Considering imperfect channel state information (CSI) for both the MS and Eve, we focus on the collaborative design of beamforming for the terrestrial base station (TBS), UAV, and MS, as well as the UAV’s trajectory. A robust optimization problem is formulated to maximize the worst-case secrecy rate, subject to constraints on worst-case communication quality for each user, UAV locations, and TBS backhaul throughput. To tackle this intractable non-convex problem, we leverage the S-procedure, general sign-definiteness, and successive convex approximation (SCA) to propose a security solution that efficiently optimizes all variables using convex optimization techniques. Numerical results validate the effectiveness of the proposed solution, illustrating the impact of CSI errors and the secure performance enhancements achieved through joint trajectory and beamforming optimization.

In industrial internet of things (IIoT) scenarios, the energy efficiency of task offloading is challenged by the quasi-periodic fading of wireless channels and the energy constraints of IIoT devices. To address this, we propose a multi-stage offloading framework, which allows intelligent reflecting surfaces (IRS)-assisted unmanned aerial vehicles (UAV) to dynamically reflect transmitted signals between a small base station (SBS) and a macro base station (MBS), aiming to mitigate inter-tier and cross-tier interference. However, achieving efficient offloading while minimizing energy consumption remains a critical challenge due to the complex interplay between device offloading decisions, IRS phase shift design, subchannel allocation, and power control. To tackle this, we first formulate a mixed-integer nonlinear programming problem based on uplink communication and computational models. Then, an improved escape optimization algorithm (IESC) is developed to solve the problem, which achieves efficient convergence through dynamic solution space exploration. Finally, simulation results demonstrate that our proposed scheme significantly outperforms existing benchmarks in terms of energy efficiency and offloading performance.

The integration of network function virtualization (NFV) with mobile edge computing (MEC) fosters a more agile service provisioning in a network operational cost-efficient manner. However, some challenges exist in adapting to the unpredictable network stochastics and resource restrictiveness, when placing virtualized network functions (VNFs) or service function chains (SFSs) appropriately onto MEC networks. In this work, we study the cost-efficient online SFC deployment in MEC networks, where each service is translated as an SFC flow and traverses through networks to meet service demands. First, we formulate a long-term time-averaged network operational cost minimization problem, by optimizing both SFC mapping and flow routing, to keep the system stability. Then, to deal with the non-trivial mixed-integer programming (MIP) and stochasticity properties in the SFC deployment, we use both Lp(0 <p< 1) norm-based relaxation and penalization, and learn-and-adapt techniques, to obtain an improved performance-stability tradeoff. Finally, both theoretical analyses and numerical simulations are conducted to demonstrate the proposed method’s superiority, in terms of its asymptotic optimality and reduced queue backlog.

This paper investigates joint transmit beamforming and trajectory optimization techniques for an unmanned aerial vehicle (UAV)-enabled covert full-duplex (FD) integrated sensing and communication (ISAC) systems with hardware impairments (HWIs), where the aerial access point (AP) transmits and receives sensing signals while the integrated communication operates in either downlink or uplink. We jointly optimize the downlink transmit signal and the uplink receive beamformers at the AP, the transmit power at the uplink users and the trajectory of the UAV. An optimization problem is formulated for maximizing the minimum covert transmission rate (CTR) among all covert users (CUs) subject to the constraints of the required sensing and uplink transmission capabilities, system covertness, total power budget. To tackle the intractable non-convex problem, we leverage majorization-minimization (MM) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Numerical results demonstrate the effectiveness of the proposed method in balancing the trade-off between covert communication and sensing performance, highlighting the UAV’s potential in adaptive ISAC deployment.

In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom) enabled integrated sensing, communication and computation (ISCC) systems. We consider the joint design of transmit beamforming at the BS and the reflecting coefficients at the RIS as well as the computation resource allocation of each user. The optimization problem for the max computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the alternative optimization (OA) and the alternating direction method of multipliers (ADMM) algorithm is developed. Furthermore, a more computationally efficient approach is introduced, which utilizes transmit beamforming based on an accelerated primal gradient (APG) method. Furthermore, the approximation principle is proposed to transform non-convex constraints in the optimization of the reflection coefficients at RISs. Simulation results show that introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) lend themselves to tracking users and operating as an aerial base station carrying out communication enhancement functionality. A core challenge neglected by most existing works is that the true-but-unknown obstacles can jeopardize UAV flight security and shadow its communication links with users, resulting in poor achievable rate and high collision risks. In this paper, a deep-reinforcement-learning (DRL)-based UAV target tracking and digital-twin (DT)-assisted collision avoidance method is proposed to optimize UAV’s communication performance while tracking moving users. Toward this end, Twin Delayed Deep Deterministic policy gradient (TD3) as a novel and policy-based DRL algorithm is used to construct an agent responsible for adaptive deciding UAV flying control actions. To efficiently detect unknown obstacles in a flight environment, an orthogonal frequency division multiple (OFDM)-based integrated sensing and communication (ISAC) system is investigated, endowing UAV’s agent with real-time obstacle distance. Finally, we present a DT obstacle model construction mechanism and integrate it with TD3 agent training. The extensive simulations demonstrate the reward convergence of the TD3 algorithm and the communication improvement with stable user tracking and reliable collision avoidance, compared with conventional approaches.

In this paper, we propose a novel covert transmission scheme for reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) system with imperfect channel state information (CSI). Considering full and partial channel uncertainty models, we jointly devise the digital and holographic beamforming along with the receive filter to maximize the worst-case and outage-constrained achievable rate (AR) of communication users while guaranteeing the sensing capability and covertness requirement. The resulting optimization problems are difficult to solve owing to the non-convexity caused by the semi-infinite constraints (SICs) and the coupled design variables. After approximating the worst-case and outage constraints by exploiting the S-procedure, successive convex approximation (SCA) and Bernstein-type inequality, we propose a secure solution that efficiently optimizes all variables by using convex optimization methods. To understand the proposed algorithm better, both the convergence and computational complexity are discussed. Simulation results show that by incorporating the RHS technique into the optimization design, the covert transmission performance of ISAC systems are improved while ensuring a certain level of sensing performance.

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known channel state information (CSI) for the eavesdropping target, we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the discrete reflection coefficients (DRC) of the multiple hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

Vehicle-to-everything (V2X) applications are usually powered by vehicular batteries and thus are power limited in general. Reconfigurable intelligent surfaces (RISs) are capable of improving the spectral efficiency and conserving energy of the wireless communications, due to the planar array architecture of which is superior beamforming gain and energy-efficient. In this paper, we study a novel design scheme where a double-RIS-enabled integrated sensing and communication (ISAC) system in vehicular network performs both a single target sensing and multi-user communications synchronously. Specifically, two transmit power budget minimization problems are formulated based on Cramér-Rao bound (CRB)-based framework under the known target location model, and radar signal-to-noise ratio (SNR)-related framework under the uncertain target location model, respectively. For the former, we propose an efficient solver based on alternative direction method of multipliers (ADMM) technique to obtain high-quality solutions for transmit beamforming and phase shifts. For the latter, an efficient algorithm based on penalty-dual-decomposition (PDD) and second order cone programming (SOCP) approaches is proposed. Simulation results demonstrate the effectiveness of two proposed algorithms and also show the superiority of our developed schemes over state-of-the-art benchmark ISAC schemes.

Integrated sensing and communication (ISAC), by combining the communication and sensing functions in shared frequency bands, emerges as a promising technology for future wireless networks. However, the performance of ISAC may be affected by the channel fading and massive connections. In this paper, we propose a non-orthogonal multiple access (NOMA) aided ISAC scheme via intelligent reflective surface (IRS) to set up virtual line-of-sight links for multi-user communication and target sensing. Specifically, our goal is to maximize the sum rate through the joint optimization of active transmit beamforming at the base station and passive phases for reflecting at the IRS, while satisfying the sensing requirement for the target user. Since the original optimization problem is non-convex, we first decompose it into two subproblems, which are converted into convex ones by applying the successive convex approximation. Then, an alternating optimization algorithm is proposed to derive a solution to the original problem. Simulation results validate that the proposed scheme can effectively enhance the multi-user NOMA communication performance while guaranteeing the sensing quality by introducing IRS.

User-centric (UC) philosophy is a promising network formation method in light emitting diode enabled visible light communication (VLC) systems. Nevertheless, the nonlinear channel impairments restrict the overall system performance and have not been fully considered in the association structure designing. In this paper, an adaptive user association approach within the UC-cells formation of dense VLC networks is investigated under the consideration of practical nonlinear impairments and adjacent interference. It is mathematically formulated to be an achievable data rate maximization problem by coordinately determining the optimal candidates of access point, clipping ratio and information-carrying power. We divide this mixed combinatorial and non-convex optimization problem into two subproblems and delicately transform them to be binary nonlinear programming and constrained linear programming problems, respectively. In addition, we develop an efficient approach to obtain the local optimal solution with low-computational complexity in an alternating iterative way. Simulation results demonstrate that the proposed scheme has relatively fast convergence and shows robustness to the variation of complex interference patterns and nonlinear impairments. Moreover, it can achieve significant throughput gain as compared with the conventional schemes, demonstrating the prospect and validity of this methodology for dense VLC networks with actual nonlinear devices.

Undesired jamming launched by malicious jammers can attack authorized communications, which is viewed as one of the critical challenges in vehicular networks. In this paper, in order to handle the problem, anti-jamming communication driven by reinforcement learning is studied in intelligent reflecting surface (IRS)-aided vehicular networks. The system sum transmission rate is optimized by joint designing the transmit beamforming at the roadside unit (RSU) and the reflection coefficients at the IRS. An anti-jamming strategy based on combining annealing bias-priority experience replay method and twin delayed deep deterministic policy gradient (TD3) technique is developed to handle the formulated challenging non-convex problem. The proposed strategy is employed to train the replay buffer in TD3, which can eliminate the deviation under the distribution change and has the advantages of fast convergence and is not easy to fall into local optima. Numerical results confirm that our proposed strategy can enhance the sum rate of multiple vehicular users and ensure radar sensing capability of RSU compared with the existing methods.

In this paper, a novel system utilizing a hybrid relay-intelligent reflecting surface (HR-IRS) to boost the security performance of directional modulation (DM) is established. In particular, the malicious attacker works in full-duplex (FD) mode and it will eavesdrop on confidential message (CM) as well as send malicious jamming. To maximize the secrecy rate (SR), a joint problem of optimizing the receive beamforming, transmit beamforming, power allocation (PA) factor, and phase shift matrix (PSM) of HR-IRS is formulated. Since the optimization problem is un-convex and the variables are coupled with each other, we address this problem by iteratively optimizing these variables. First, the receive beamforming is designed based on the generalized Rayleigh-Ritz theorem. Then, the transmit beamforming and PA factor are optimized via Dinkelbach’s Transform and successive convex approximation methods. And for PSM, two strategies, called separate optimization of PSM (SO-PSM) and joint optimization of PSM (JO-PSM), are proposed. Thus, two iterative schemes are proposed accordingly, namely maximizing SR based on SO-PSM (Max-SR-SOP) and maximizing SR based on JO-PSM (Max-SR-JOP). The former has a better performance and the latter has a lower complexity. Simulation results show that given a sufficient power budget of HR-IRS, the proposed Max-SR-SOP and Max-SR-JOP can enable HR-IRS-aided DM network to obtain a higher SR than that aided by passive IRS.

In this paper, we consider optimizing a multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) transceiver at the roadside unit (RSU) to detect a potential eavesdropping target and transmit the private information securely to the legitimate vehicular users. An optimization problem is formulated by optimizing the sum secrecy throughput of vehicle-to-infrastructure (V2I) links under requirements of waveform similarity and target return signal-to-interference-plus-noise ratio (SINR) threshold. To handle the challenging issue, we first cast the resulting non-convex problem into an equivalent optimization problem relying on the mean-square error (MSE) technique specified resource budget, and then develop an alternating procedure to decouple two optimization variables and decompose the resulting problem as two subproblems. To deal with the subproblem, we propose a dual ascent approach (DAA) based on the Limited-memory Broyden Fletcher Goldfarb and Shanno (LBFGS). Simulation results confirm the efficiency of the devised optimization method.

As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Multiple-input multiple-output (MIMO) radar design for extended targets in a spectrally crowded environment is a challenge owing to the high sensitivity of the target impulse response (TIR) and the increasing requests for spectrum. Assuming unknown TIR, this paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in MIMO structure ensuring spectral compatibility with the overlayed radiators. A priori information is used to impose a spectral constraint on the waveforms, which is the result of a non-convex optimization problem aimed at enhancing the average signal-to-interference-plus-noise-ratio (SINR) over a finite uncertainty set for the TIR. The new method realizes an improved spectral cohabitation with the surrounding radiators through an appropriate modulation of the transmitted energy. In addition, we develop an iterative optimization algorithm which successively enhances the average SINR. Each iteration of the algorithm involves a hidden convex problem, which can be solved resorting to the rank-one decomposition procedure. Finally, the performance is assessed by studying the trade-off among the achieved SINR and spectral shape. The reported results are presented to analyze the performance of the devised method against several counterparts in terms of the SINR value and robustness.

This paper presents a variable density (sinusoidal) antenna deployment scheme which is designed for mobile relay (MR) system of the high-speed train. By analyzing the large-scale fading under the high-speed railway (HSR) wireless channel environment, the instantaneous channel capacity and the total service amount of several antenna deployments are derived. Theoretical analysis and simulation results indicate that the proposed deployment can provide higher capacity for the HSR relay system. Comparing with several traditional deployments, the proposed deployment utilizes the feature of the HSR wireless environment, provides better coverage to the edge of the base stations (BS). In addition, an antenna selection scheme is proposed based on the sinusoidal deployment.