Geo-Based Scheduling for C-V2X Networks

Cellular Vehicle-to-Everything (C-V2X) networks can operate without cellular infrastructure support. Vehicles can autonomously select their radio resources using the sensing-based Semi-Persistent Scheduling (SPS) algorithm specified by the Third Generation Partnership Project (3GPP). The sensing nature of the SPS scheme makes C-V2X communications prone to the well-known hidden-terminal problem. To address this problem, this paper proposes a novel geo-based scheduling scheme that allows vehicles to autonomously select their radio resources based on the location and ordering of neighboring vehicles on the road. The proposed scheme results in an implicit resource selection coordination between vehicles (even with those outside the sensing range) that reduces packet collisions. This paper evaluates analytically and through simulations the proposed scheduling scheme. The obtained results demonstrate that it reduces packet collisions and significantly increases the C-V2X performance compared to when using the sensing-based SPS scheme

Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201

On the Design of Sidelink for Cellular V2X: A Literature Review and Outlook for Future

Connected and fully automated vehicles are expected to revolutionize our mobility in the near future on a global scale, by significantly improving road safety, traffic efficiency, and traveling experience. Enhanced vehicular applications, such as cooperative sensing and maneuvering or vehicle platooning, heavily rely on direct connectivity among vehicles, which is enabled by sidelink communications. In order to set the ground for the core contribution of this paper, we first analyze the main streams of the cellular-vehicle-to-everything (C-V2X) technology evolution within the Third Generation Partnership Project (3GPP), with focus on the sidelink air interface. Then, we provide a comprehensive survey of the related literature, which is classified and critically dissected, considering both the Long-Term Evolution-based solutions and the 5G New Radio-based latest advancements that promise substantial improvements in terms of latency and reliability. The wide literature review is used as a basis to finally identify further challenges and perspectives, which may shape the C-V2X sidelink developments in the next-generation vehicles beyond 5G