In this letter, we study the robust and secure transmission in the millimeter-wave (mmWave) unmanned aerial vehicle (UAV) communication assisted by a reconfigurable intelligent surface (RIS) under imperfect channel state information (CSI). Specifically, the active beamforming of the UAV, the coefficients of the RIS elements and the UAV trajectory are jointly designed to maximize the sum secrecy rate of all legitimate users in the presence of multiple eavesdroppers. However, the CSI is coupled with the UAV trajectory, which results in complex constraints. Furthermore, the time-related issue caused by the outdated CSI also makes the formulated problem intractable to solve. To tackle these challenges, by leveraging the deep deterministic policy gradient (DDPG) framework, a novel and effective twin-DDPG deep reinforcement learning (TDDRL) algorithm is proposed. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm, and the RIS can significantly improve the sum secrecy rate.

The integration of reconfigurable intelligent surface (RIS) into millimeter wave (mmWave) vehicular communications offers the possibility to unleash the potential of future proliferating vehicular applications. However, the high-mobility-induced rapidly varying channel state information (CSI) has been making it challenging to obtain the accurate instantaneous CSI (I-CSI) and to cope with the incurable high signaling overhead. The situation may become worse when the RIS with a large number of passive reflecting elements is deployed. To overcome this challenge, we investigate in this paper a robust transmission scheme for the time-varying RIS-aided mmWave vehicular communications, in which, specifically, a multi-antenna base station (BS) serves vehicle user equipments (VUEs) with the help of RIS at the mmWave frequency. The uplink average achievable rate is maximized relying only upon the imperfect knowledge of statistical CSI. Considering the time-varying characteristics, we first propose an effective transmission protocol by reasonably configuring the time-scale of CSI acquisition in order to significantly relax the frequency of channel information updates, which constitutes one of the most critical issues in RIS-aided vehicular communications. Then, the formulated resource allocation problem is discussed in the single- and multi-VUE case, respectively. To be specific, for the single-VUE case, a closed-form expression of the average rate is derived by extracting the statistical characteristics of mmWave channels, and an alternating optimization (AO)-based algorithm is proposed. For the multi-VUE case, we develop an efficient algorithm, called JAPMC, to circumvent the unavailability of the closed-form of the objective function and probabilistic constraint by constructing quadratic surrogates of that. Simulation results confirm the effectiveness and robustness of our proposed algorithms as compared to benchmark schemes.

Reconfigurable intelligent surfaces (RISs) have the capability of reconfiguring the wireless environment in a favorable way to improve the quality-of-service (QoS) of wireless communications. This makes RISs a promising candidate to enhance vehicle-to-everything (V2X) applications. This paper investigates the spectrum sharing problem in RIS-aided vehicular networks, in which multiple vehicle-to-vehicle (V2V) links reuse the spectrum already occupied by vehicle-to-infrastructure (V2I) links. To overcome the difficulty of acquiring instantaneous channel state information (CSI) due to the fast varying nature of some V2X channels, we rely upon large-scale (slowly varying) CSI in order to fulfill the QoS requirements of V2I and V2V communications. Particularly, we aim to maximize the sum capacity of V2I links that are used for high-rate content delivery and to guarantee the reliability of V2V links that are used for the exchange of safety information. The transmit power of vehicles, the multi-user detection (MUD) matrix, the spectrum reuse of V2V links, and the RIS reflection coefficients are jointly optimized, which results in a mixed-integer and non-convex optimization problem. To tackle this problem, the outage probability of each V2V link is first approximated by introducing an analytical expression to simplify the formulated problem. By leveraging the block coordinate descent (BCD) method, the considered optimization problem is decomposed into three sub-problems, whose optimal solutions are provided independently and updated alternately to obtain a near-optimal solution. Simulation results verify the theoretical analysis and the effectiveness of the proposed algorithm, as well as unveil the benefits of introducing RISs for enhancing the QoS performance of vehicular communications.

This paper studies the resource allocation for intelligent reflecting surface aided vehicular communications based on slowly varying large scale fading channel information. To meet different quality-of-service (QoS) requirements of vehicular communications, we aim to maximize the sum vehicle-to-infrastructure (V2I) link capacity while guaranteeing the minimum signal-to-interference-plus- noise ratio (SINR) of vehicle-to-vehicle (V2V) links. More specifically, the power allocation, IRS reflection coefficients and spectrum allocation are jointly optimized. To tackle the formulated mixed integer non-convex problem, we divide it into two stages, which yields an alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm and the IRS can significantly improve the quality of vehicular communications in terms of the sum V2I capacity.

Given the limited transmission power of smart devices in Internet of Remote Things (IoRT), unmanned aerial vehicle (UAV) aided space-air-ground (SAG) networks become a beneficial remedy for uplink data transmission in IoRT networks. In this article, we propose a SAG–IoRT framework, where drones act as relays to upload the data from smart devices to low earth orbit satellites. Considering the large number of smart devices, we maximize the system capacity by jointly optimizing smart devices connection scheduling, power control, and UAV trajectory, where the joint optimization is a nonconvex optimization problem. The formulated problem is a mixed integer nonconvex optimization problem, which is challenging to solve directly. Hence, an efficient iterative algorithm is proposed for solving the above-mentioned nonconvex optimization problem by applying variable substitution, successive convex optimization techniques, and the block coordinate decent algorithm. In particular, we alternately iterate smart device connection scheduling, power control, and UAV trajectory design to obtain the maximum system capacity. Numerical simulation results show that our proposed algorithm substantially improves the system capacity in comparison to the static UAV scheme, and achieves a gain of at least 22.3% in terms of the capacity against dynamic UAV scheme with a circular trajectory.