Given its ability to control and manipulate wireless environments, reconfigurable intelligent surface (RIS), also known as intelligent reflecting surface (IRS), has emerged as a key enabler technology for the six-generation (6G) cellular networks. In the meantime, vehicular environment radio propagation is negatively influenced by a large set of objects that cause transmission distortion such as high buildings. Therefore, this work is devoted to explore the area of RIS technology integration with vehicular communications while considering the dynamic nature of such communication environment. Specifically, we provide a system model where RoadSide Unit (RSU) leverages RIS to provide indirect wireless transmissions to disconnected areas, known as dark zones. Dark zones are spots within RSU coverage where the communication links are blocked due to the existence of blockages. In details, a discrete RIS is utilized to provide communication links between the RSU and the vehicles passing through out-of-service zones. Therefore, the joint problem of RSU resource scheduling and RIS passive beamforming or phase-shift matrix is formulated as an optimization problem with the objective of maximizing the minimum average bit rate. The formulated problem is mixed integer non-convex program which is difficult to be solved and does not account for the uncertain dynamic environment in vehicular networks. Thereby, we resort to alternative methods based on Deep Reinforcement Learning to determine RSU wireless scheduling and Block Coordinate Descent (BCD) to solve for the phase-shift matrix, i.e., passive beamforming, of the RIS. The Markov Decision Process (MDP) is defined and the complexity of the solution approach is discussed. Our numerical results demonstrate the superiority of our proposed approach over baseline techniques.

Intelligent transportation systems are thriving thanks to a wide range of technological advances, namely 5G communications, the Internet of Things (IoT), artificial intelligence, and edge computing. Meanwhile, in environments where direct communication can be impaired by, for instance, blockages, such as in urban cities, unmanned aerial vehicles (UAVs) can be considered as an alternative for providing and enhancing connectivity. In this article, we consider a time-constrained data gathering problem from a network of sensing devices and with assistance from a UAV. A reconfigurable intelligent surface (RIS) is deployed to further help in improving the connectivity and energy efficiency of the UAV. This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoT devices' transmissions, as well as the trajectory of the UAV. First, the problem is formulated with the objective of maximizing the total number of served devices each during its activation period. Owing to its complexity, we leverage deep reinforcement learning in our solution; the UAV trajectory planning is modeled as a Markov decision process, and proximal policy optimization is invoked to solve it. Next, the RIS configuration is then handled via block coordinate descent. Finally, extensive simulations are conducted to demonstrate the efficiency of our solution approach that, in many cases, outperforms other methods by more than 50%. We also show that integrating an RIS with a UAV in IoT networks can improve the UAV energy efficiency.

Vehicular communication is a crucial component of intelligent transportation systems (ITS) upon which many services are based starting with relaying traffic conditions up to enabling autonomous driving. Using a backhaul link, a road side unit (RSU) is assumed to fetch content from the Internet to serve the requests of incoming vehicles while they are within its range. Unless an effective caching mechanism is employed at the RSU, popular content might be unnecessarily retrieved from the network backhaul repeatedly especially in scenarios with large number of vehicles. This incurs significant capital and operating expenditures to RSU operators due to backhaul leasing and utilization. In this work, we explore cost-effective solutions for RSU operators by leveraging the content that is readily available on vehicles within the RSU's coverage range to intelligently cache popular content and serve newly arriving vehicles. However, due to the limited RSUs storage capacity and continuous flow of contents, the problem of establishing an RSU caching strategy to maximize the serving rate of incoming vehicles in an energy-efficient manner is challenging. Thus, we propose a technique based on deep reinforcement learning and demonstrate its effectiveness compared to the state-of-the-art.

In this paper, the authors describe a conformance testing system for SGSF-064-1, the communication protocol between electric vehicles and conductive DC (direct current) chargers in Korea. Since the SGSF-064-1 is based on CAN (controller area network), the testing system was developed by CANoe. The DC charger known as EVSE (electric vehicle supply equipment) is the system being tested and the developed system implemented in PC (personal computer). The developed system performs as a tester to ensure that the DC chargers from various manufactures can conform to the communication protocol in SGSF-064-1. The testing system contains four testing modes which also consist of several test cases.