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Vehicular edge computing (VEC) becomes a promising paradigm for thedevelopment of emerging intelligent transportation systems. Nevertheless, thelimited resources and massive transmission demands bring great challenges onimplementing vehicular applications with stringent deadline requirements. Thiswork presents a non-orthogonal multiple access (NOMA) based architecture inVEC, where heterogeneous edge nodes are cooperated for real-time taskprocessing. We derive a vehicle-to-infrastructure (V2I) transmission model byconsidering both intra-edge and inter-edge interferences and formulate acooperative resource optimization (CRO) problem by jointly optimizing the taskoffloading and resource allocation, aiming at maximizing the service ratio.Further, we decompose the CRO into two subproblems, namely, task offloading andresource allocation. In particular, the task offloading subproblem is modeledas an exact potential game (EPG), and a multi-agent distributed distributionaldeep deterministic policy gradient (MAD4PG) is proposed to achieve the Nashequilibrium. The resource allocation subproblem is divided into two independentconvex optimization problems, and an optimal solution is proposed by using agradient-based iterative method and KKT condition. Finally, we build thesimulation model based on real-world vehicle trajectories and give acomprehensive performance evaluation, which conclusively demonstrates thesuperiority of the proposed solutions.

Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach