Vehicular edge computing (VEC) becomes a promising paradigm for the development of emerging intelligent transportation systems. Nevertheless, the limited resources and massive transmission demands bring great challenges on implementing vehicular applications with stringent deadline requirements. This work presents a non-orthogonal multiple access (NOMA) based architecture in VEC, where heterogeneous edge nodes are cooperated for real-time task processing. We derive a vehicle-to-infrastructure (V2I) transmission model by considering both intra-edge and inter-edge interferences and formulate a cooperative resource optimization (CRO) problem by jointly optimizing the task offloading and resource allocation, aiming at maximizing the service ratio. Further, we decompose the CRO into two subproblems, namely, task offloading and resource allocation. In particular, the task offloading subproblem is modeled as an exact potential game (EPG), and a multi-agent distributed distributional deep deterministic policy gradient (MAD4PG) is proposed to achieve the Nash equilibrium. The resource allocation subproblem is divided into two independent convex optimization problems, and an optimal solution is proposed by using a gradient-based iterative method and KKT condition. Finally, we build the simulation model based on real-world vehicle trajectories and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solutions.
翻译:然而,有限的资源和庞大的传输需求给在严格的最后期限要求下实施车辆应用带来了巨大的挑战。此外,我们将计算机应用分为两个子问题,即任务卸载和资源分配。特别是,将车辆向基础设施的传输模式建成一个精确的潜在游戏(EPG),并提议建立一个多试剂分布式的深层确定性政策梯度(MAD4PG),以实现Nash均衡。资源分配子问题被分为两个独立的对流优化问题和资源分配问题,最后,我们用一个基于梯度的模型来构建一个基于模型的模型,最后,我们用一个基于模型的模型和一种基于模型的模型来构建一个最终性能。