Efficient Task Offloading in Double Roadside RIS-Assisted Vehicular Edge Computing Networks Using Deep Reinforcement Learning

The concept of vehicular edge computing (VEC) has recently been envisioned as a promising paradigm to satisfy the quality of service (QoS) requirement of delay-sensitive intelligent applications in future networks. However, the limited radio frequency communication range necessitates the dense deployment of communication infrastructures to accommodate the increasing number of connected vehicles and data traffic. This could lead to growing equipment and energy costs, hindering the full realization of the VEC system. To address such limitations, we introduce a double roadside reconfigurable intelligent surface (RIS) assisted VEC network in this paper, where RISs are deployed inside the coverage gaps between two RSUs to extend the service range. The research goal is to maximize the sum offloading efficiency by jointly optimizing offloading decisions, computation resource allocation, and phase shift vectors of RISs. Since the original problem is a challenging mixed-integer non-linear problem (MINLP), we decompose it into a top-problem for optimizing offloading destinations and phase shift vectors, and a sub-problem for optimizing offloading ratios and computation resources. We propose a deep reinforcement learning (DRL)-based algorithm for quickly obtaining near-optimal offloading decisions and phase shift vectors, alongside a Dinkelbach-based method for obtaining optimal offloading ratios and computation resource allocation. Simulation results demonstrate that our proposed algorithm achieves near-optimal performance compared to other benchmarks and enables real-time decision-making.