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.

This paper explores the near-field regional target localization problem with the reconfigurable intelligent surface (RIS) assisted system. Traditional near-field localization strategies typically estimate the position of the target through line-of-sight (LOS) signals transmitted from the anchor node. In practice, accurate location estimation is a challenging issue when the LOS link between the anchor node and target may be unavailable due to the obstacle. To this end, this paper investigates the possibility to confirm the position of the target node consisted in an area of interest (AOI) by retrieving information from the RIS reflection signals. Specifically, we establish a general framework for RIS-assisted regional localization, which consists of RIS phase design and position determination. By defining the average localization accuracy (ALA) of the AOI, we first present a discretization method to design the RIS phase. Then, a robust phase design problem is formulated via transforming the AOI into an uncertainty model of position parameters, and an efficient iterative entropy regularization (IER)-based algorithm is proposed to solve it. Using the designed RIS phase, we develop a near-field target localization algorithm and discuss the power optimization problem for the RIS-assisted localization system (RALS). Numerical results demonstrate the effectiveness of the proposed framework, in which both phase design strategies almost coincide with the optimal RIS phase, and the proposed localization method can attain the near-optimal localization performance by applying the designed RIS phase schemes.

Near-field (NF) localization is a key research topic in various applications, including autonomous vehicles, real-time tracking, target monitoring, etc. However, the traditional NF localization methods mostly rely on the line-of-sight (LOS) condition, and the performance may suffer degradation when there are lacking LOS paths. In this paper, we introduce a promising technology named reconfigurable intelligent surface (RIS) to resolve the problem and investigate the NF RSS-based localization algorithms. To be specific, we apply RIS to construct virtual line-of-sight (VLOS) paths between the anchor node (AN) and the unknown node (UN) for the sake of addressing the LOS absence problem, and propose the RIS phase adjustment schemes by maximizing the received signal strength (RSS) of the UN. On this basis, we derive the relationship between the azimuth and the phase parameters, and the accurate estimation of the UN’s position is derived via weighted least square (WLS) and alternate iteration methods. Next, we further resolve the coexisting problem in terms of LOS and VLOS paths by adjusting the reflection factors. Lastly, we put forward a method for discriminating whether the UN lies in the far-field (FF) or NF of the RIS subparts to minimize the localization error. Several simulations demonstrate the effectiveness of our proposed algorithms.

Unmanned aerial vehicle (UAV) and high-altitude platform (HAP)-enabled aerial edge computing (AEC) networks facilitate diverse Internet of Things (IoT) applications. In this paper, we investigate the average task completion time and energy consumption by jointly optimizing sensing, communication and computing in cooperative AEC networks facilitated by multiple UAVs and HAP. The sensing times, multi-UAV trajectories, transmission power control, offloading strategy and communication resource allocation are jointly optimized. We transform the original optimization problem into minimizing the average task completion time while ensuring energy consumption stability by introducing Lyapunov optimization theory. The problem is then decomposed into multiple subproblems, which are solved through numerical analysis, successive convex approximation, and the Dinkelbach algorithm, respectively. These algorithms are embedded into the proximal policy optimization (PPO)-based multi-agent deep reinforcement learning (MADRL) framework to speed up the convergence performance of the MADRL model. Simulation results demonstrate that the proposed algorithm achieves superior performance in terms of average task completion time and energy consumption.

Along with the rapid development of the fourth industrial revolution, industrial cyber-physical systems (ICPS) are anticipated to achieve precise mapping and management for the physical world by integrating digital sensing and automated control. However, the conflict between limited computing resources and extensive sampling data, combined with severe industrial interference, exacerbates the system’s processing burden and diminishes its accuracy, hindering its ability to meet the low-latency and high-reliability control requirements. To address this issue, this paper investigates an end-edge collaborative control framework to enhance control performance for a short-packet transmission ICPS by providing powerful computation capability. We utilize the age of information (AoI) to characterize the impact of information freshness on control accuracy and construct an AoI-aware control law to assist in data sensing, transmission, and computing strategy design. In addition, we consider the influence of sampling and short-packet decoding errors in AoI-aware control performance to enhance the reliability of sampling and transmission strategies design. A joint optimization scheme of sampling interval, sampling time, computation offloading, and bandwidth allocation based on the block coordinate descent method and game theory is proposed to achieve a tradeoff between the control cost and energy consumption. By considering a real-world trolley inverted pendulum manipulation model, numerical results verify the performance gain of the proposed end-edge collaborative framework and the effectiveness of the presented algorithm.