Joint resource management and task offloading for ISAC-assisted vehicular edge computing networks

Edge computing and integrated sensing and communication (ISAC) technologies offer promising prospects for intelligent transportation systems (ITSs) in which the sensing data of vehicles can be processed directly or be offloaded to a base station (BS) or to the surrounding vehicles. However, the inherent scarcity of communication resources becomes a crucial problem in ITSs, especially when ISAC is introduced. In this paper, we propose an ISAC-assisted vehicular edge computing networks (VECNs) architecture composed of two interconnected stages: resource management and task offloading. Vehicles perform sensing and dynamically offload sensing tasks to the BS or nearby vehicles based on the link conditions. A two-stage joint optimization problem is formulated to optimize the resource block (RB) allocation for V2I and V2V links, including communication and sensing power among multiple vehicles, so as to maximize the overall data transmission rate. Concurrently, the offloading decisions are optimized, aiming to minimize the weighted sum of the system task completion delay and energy consumption. Considering the complex, dynamic transmission environment, we reformulate these problems as Markov Decision Processes and propose a deep reinforcement learning-based dual-stage resource management and offloading decision strategy (DDROS). Simulation results demonstrate that the proposed DDROS achieves strong convergence and exhibits significant performance advantages over baseline strategies under various conditions.