Integrated sensing and communication (ISAC) is pivotal for enabling simultaneous environment perception and data transmission in intelligent transportation systems (ITS). However, mission-critical ITS management applications, such as collision avoidance and autonomous driving, require stable and reliable ISAC services. Unfortunately, dense urban canyons, with their skyscraper-induced occlusions, create persistent coverage blind zones, posing significant challenges to these applications. To address these challenges, this paper explores a novel aerial intelligent reflecting surface (AIRS)-assisted ISAC system, where multiple AIRSs dynamically reconfigure the wireless propagation environment to enhance multi-vehicle sensing and base station (BS)-to-multiuser communication. To maximize the minimum achievable communication rate while ensuring sensing performance, we formulate a joint resource allocation problem considering BS beamforming, AIRS trajectory optimization, AIRS phase shift control, and user association. Given its highly coupled and nonconvex nature, we develop an alternating optimization framework tackling each subproblem sequentially. Specifically, we employ the Lagrangian dual transform and semi-definite relaxation (SDR) for BS beamforming, the successive convex approximation (SCA) method for AIRS trajectory optimization, matrix decomposition and equivalent rank-constrained transformation techniques for AIRS phase shift design, and a penalty dual decomposition (PDD)-based approach for user association. Furthermore, considering uncertainties in the vehicle’s angle of departure (AoD) due to urban mobility, we derive a worst-case sensing performance bound and generalize the proposed algorithm to a more complex scenario. Simulations validate the algorithm’s effectiveness, demonstrating superior communication rates and sensing performance over benchmark schemes, while ensuring robustness against AoD uncertainties.

Uncrewed aerial vehicle (UAV)-assisted joint communication and localization (JCAL) system have great potential and capacity to make future Internet of Things efficient, safe, smart, reliable, and sustainable. Generally, the traditional UAV path planning methods set the flying duration and hovering duration of UAVs as constants, and ignore the importance of UAV operation time in emergency rescue and other scenarios. In this article, we consider the path planning and time scheduling problem of UAV-assisted JCAL system for minimizing the UAV operation time under the constraints of the localization accuracy, communication message, and energy loss. Specifically, we first formulate path planning and time scheduling problem for UAV-assisted JCAL system and derive Cramér-Rao bound (CRB) as the localization accuracy constraint. The variables in the constraints of localization accuracy, communication overhead, and energy loss are deeply coupled, which leads to nonconvex optimization problems. Next, to solve the high nonconvex problem, we divide the original problem into two subproblems, i.e., time scheduling subproblem and path planning subproblem. We use equivalent convex transformation and successive convex approximation (SCA) to transform the nonconvex constraints into convex forms for solving the subproblems, respectively. Lastly, aiming to the robust problem of target and channel parameters, we convert the robust constraints into convex constraint forms by equivalent proof and S-Procedure. On this basis, we develop a robust algorithm for solving the uncertainty of target and channel parameters. Simulation results verify the feasibility of the proposed methods.

Future vehicle localization will be committed to improving the positioning accuracy and energy efficiency of localization systems in the intelligent transportation. Recently, reconfigurable intelligent surface (RIS) as an emerging technology has gained widespread attention and is favorable to enhance the performance of vehicle localization systems because of its capacity of customizing the wireless channel. In this paper, in order to minimize the transmit power, we consider the joint active and passive beamforming problem of RIS-assisted vehicle localization system under the constraints of the localization accuracy and the phase shift parameters of the RIS. Specifically, we establish the model of RIS-assisted vehicle localization system and derive the Cramér-Rao bound (CRB) as the localization performance metric. Next, for the scenario of single vehicle localization, we derive the optimal RISs’ phases, and obtain the optimal solution for joint active and passive beamforming based on semidefinite programming relaxation of the non-convex beamforming problem and the corresponding equivalent analysis. Lastly, aimming to the scenario of multiple vehicles localization, we transform the nonconvex joint active and passive beamforming problem into semidefinite programming (SDP) and geometric programming (GP) form subproblems through alternating optimization. Simulation results verify the feasibility of the proposed methods.

This paper considers a reconfigurable intelligent surface (RIS)-aided multi-user simultaneous localization and communication (SLAC) system with statistical position uncertainty, where an RIS is deployed to simultaneously enhance the quality of service. To this end, we first derive the closed-form Cramér-Rao lower bound concerning position parameters as the localization metric and also provide the achievable rate metric for communication services. Then, the joint robust design of subcarrier groups, beamforming vectors, and the phase-shift matrix of the RIS is formulated as a stochastic bi-objective optimization problem to maximize expected localization and communication metrics. Due to the nonlinearity of the multi-objective function and the coupling between optimizing variables, the resulting problem is highly non-convex. Accordingly, we transform the expected achievable rate into an analytical form and further develop a novel unified successive convex approximation (U-SCA)-based iterative algorithm to obtain a robust resource allocation strategy. In particular, we derive closed-form solutions of beamforming vectors and the phase-shift matrix of RIS to decrease the computational complexity. In addition, we also analyse the convergence of the proposed U-SCA-based algorithm. Simulation results demonstrate the effectiveness of the presented method.

Multi-target tracking (MTT) plays a significant role in intelligent transportation systems, serving as an enabling technology for applications such as self-driving, surveillance, and navigation. To enhance the MTT performance, the unmanned aerial vehicles (UAVs) have emerged as effective assistants to MIMO radar system, due to their advantages of high flexibility, controllable deployment and cost-effectiveness. Towards this end, this work investigates a multi-UAV-enabled MIMO radar system, in which each UAV is equipped with a MIMO radar unit and dispatched to track multiple targets simultaneously. We are interested in the joint trajectory planning and transmit resource optimization (i.e. radar waveform optimization and transmit power allocation) to minimize the system power consumption, subject to constraints related to UAVs motion, system resources, and tracking accuracy. Specifically, the posterior Cramér-Rao Lower Bound (PCRLB) is derived and employed as a guideline for the joint optimization. Given the non-convex and inter-variable coupling nature of the formulated problem, we decompose it into three sub-problems and design an alternating optimization method. Firstly, for the UAVs trajectory planning, we obtain sub-optimal results leveraging the successive convex approximation (SCA)-based algorithm. Next, we present a feasible solution set for radar waveform optimization. For transmit power allocation, we perform a convex transformation and find the numerical solution. In addition, through introducing the Lagrange dual method, we further obtain the optimal analytical solution. Finally, simulation results demonstrate the effectiveness and advantages of the developed strategy.

In this paper, a novel reconfigurable intelligent surfaces (RIS)-assisted joint localization and communication (JLAC) scheme is presented to supply both position-sensing and data transmission functions for a multi-user system by a frequency division strategy. In particular, considering the parameter uncertainty, we formulate the robust resource design problem as a statistical mixed-integer form, aiming to maximize localization and communication performance by joint subcarrier group, beamforming, and phase-shift optimization. To tackle the formulated non-convex problem efficiently, we develop an iterative method based on the stochastic successive convex approximation technology to handle the original problem. Simulation studies are presented to demonstrate the effectiveness of the proposed JLAC scheme and method.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Joint localization and communication systems have drawn significant attention due to their high resource utilization. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided simultaneously localization and communication system. We first determine the sum squared position error bound (SPEB) as the localization accuracy metric for the presented localization-communication system. Then, a joint RIS discrete phase shifts design and subcarrier assignment problem is formulated to minimize the SPEB while guaranteeing each user’s achievable data rate requirement. For the presented non-convex mixed-integer problem, we propose an iterative algorithm to obtain a suboptimal solution by utilizing the Lagrange duality as well as penalty-based optimization methods. Simulation results are provided to validate the performance of the proposed algorithm.

This paper studies a simultaneous wireless information and power transfer system with a helping relay in wireless body area network, where the relay harvests energy from the radio-frequency signals sent by other nodes, then the relay uses the harvested energy to help transmit energy to the destination and forward information to the source, respectively. Compared with the existing protocols, we propose the dynamic time allocation strategy in this paper. First, based on power splitting (PS) and time switching (TS) transmission protocols, we propose two new transmission protocols, where the transmission time slots are unequal allocation. Then the optimal strategy to achieve the maximum information throughput by solving nonlinear programming problems is presented. And by changing the relay position, the optimal time and power ratios for the best system performance are presented. Finally, the fitting curves of the optimal solutions for different relay positions are plotted. Numerical results show that our proposed optimal strategy can achieve the best throughput performance and the protocol based on TS outperforms slightly than the protocol based on PS.

This letter presents a new mixed-order MUSIC algorithm for far-field and near-field sources localization using a sparse symmetric array. By exploiting the special array geometry, the proposed algorithm constructs a cumulant matrix to estimate the directions of arrival (DOAs) of both far-field and near-field sources using the conventional MUSIC method. With the estimated DOAs and the covariance matrix of the sparse array, the far-field and near-field sources are identified and the range parameters of near-field sources are also obtained by defining the range spectrum. Compared with the traditional algorithms, the proposed algorithm has moderate computation complexity, and provides higher resolution, and also improves the parameters estimation accuracy. Simulation results are provided to demonstrate the performance improvement of the proposed method.

In this letter, a novel mixed sources localization method based on sparse signal reconstruction is presented, which can efficiently estimate direction-of-arrival (DOA) and range parameters of near-field and far-field sources. By constructing the cumulant domain data of array which is only related to DOA parameters of mixed sources, we obtain DOA estimation of all sources using the weightedl1-norm minimization. And then, a mixed overcomplete matrix on the basis of DOA estimation is introduced in the sparse signal representation framework to estimate range parameters and distinguish far-field sources from mixed sources. Compared with the two-stage MUSIC algorithm, the proposed method can provide improved accuracy and resolve closely spaced sources. The simulation results show the effectiveness of our method.