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.
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.
In this paper, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. 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.
In many wireless sensors, the target kinematic states include location and Doppler information that can be observed from a time series of range and velocity measurements. In this work, we present a tracking strategy for comprising target velocity components as part of the measurement supplement procedure and evaluate the advantages of the proposed scheme. Data association capability can be considered as the key performance for multi-target tracking in an active sonar system. Then, we proposed an enhanced Doppler data association (DDA) scheme which exploits target range and target velocity components for linear multi-target tracking. If the target velocity measurements are not incorporated into target kinematic state tracking, the linear filter bank for the combination of target velocity components can be implemented. Finally, a significant enhancement in the multi-target tracking capability provided by the proposed DDA scheme with the linear multi-target combined probabilistic data association method is demonstrated in a sonar underwater scenario.
It has been shown that in colocated multi-input multi-output (MIMO) radar systems the sparsity of targets in the illuminated space can be exploited by compressive sensing (CS) techniques to achieve either the same localization performance as traditional methods but with significantly fewer measurements, or significantly improved performance with the same number of measurements. In the colocated CS-based MIMO radar context, this paper proposes a power allocation scheme that distributes the system total transmit power among the transmit antennas in an optimal fashion that leads to improved detection performance. In particular, the allocation scheme minimizes the coherence between the target returns from different search cells, or equivalently, the coherence of the columns of the sensing matrix.
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.
The system architecture for an adaptive multiple input multiple output (MIMO) radar-communication transceiver is proposed. A waveform design approach for communication data embedding into MIMO radar pulse using M-ary position phase shift keying (MPPSK) waveforms is introduced. A waveform optimization algorithm for the adaptive system is presented. The algorithm aims to improve the target detection performance by maximizing the relative entropy (RE) between the distributions under existence and absence of the target, and minimizing the mutual information (MI) between the current received signals and the estimated signals in the next time instant. The proposed system adapts its MPPSK modulated inter-pulse duration to suit the time-varying environment. With subsequent iterations of the algorithm, simulation results show an improvement in target impulse response (TIR) estimation and target detection probability. Meanwhile, the system is able to transmit data of several Mbps with low symbol error rates.
Assuming unknown knowledge of target impulse response (TIR), we discuss the robust optimization problem of the radar code sequence and filter bank configuration for detection of range spread target in the additive Gaussian noise and clutter jamming environment. In order to improve the capability of range spread target detection, we cope with transmit-receive system design to optimize the worst-case signal to interference plus noise ratio (SINR) gained by a bank of filters. The problem is formulated in terms of a non-convex max-min quadratic fractional optimization program. Relying on an appropriate reformulation, we present an alternate optimization technique which monotonically increases the SINR value and then converges to the limit value. All iterations in the procedure, involve a hidden convex as well as a max-min quadratic non-convex problem that is solved resorting to the generalized Dinkelbach (GD) process. The effectiveness of the alternate optimization procedure is demonstrated by numerical simulation results, highlighting the capability enhancement offered by the robust joint optimization technique.
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