Reliable Cooperative Communications for Highly Mobile Internet-of-Vehicles (IoV) Environments

Abstract

The ultimate challenge of the network designer is the resource allocation for both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications due to the dynamic environment. The optimal best path can improve network quality of service (QoS) over the time-varying channel using less transmission power. The joint power allocation of V2V and V2I is the most challenging aspect due to the association of its multi-variables objective function. Alternative optimization can be used to make the global problem into a series of sub-problems. Then a semi-definite programming (SDP)-based iterative gradient descent (SDP-IGD) power allocation can be used to assign power in each relay. In vehicular network systems, end-to-end packet delay increases with the growing number of vehicles associated with that path. In the case of excessive packet delay, information is needed to be enqueued before sending through the fading channel. A new delay-sensitive buffer-aided relay selection has been proposed in literature based on the channel greedy scheduling algorithm (CGSA) to reduce the average delay by selecting a set of alternative paths based on individual node buffer status. The optimal path selection needs to guarantee both the latency and reliability requirements in vehicular communications. In a high mobility environment, deep reinforcement learning (DRL)-based decentralized algorithm may be implemented to obtain an optimal path. The optimal path-finding technique requires very large mathematical calculations. The low-power onboard devices may not be able to perform the large-size computation timely. Task offloading methods can be applied in high mobility networks aiming to guarantee latency, energy consumption, and payment cost requirement. In task offloading for vehicular communications, both park and moving vehicles can be used as computation resources. The traditional offloading may be used to find the best vehicles that may compute the offloaded task timely.