Task Offloading and Resource Allocation in NOMA-VEC: A Multi-Agent Deep Graph Reinforcement Learning Algorithm

Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple ac-cess(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maxi-mize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multi-agent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and re-lationship information between agents from the sys-tem state as observations,outputs task offloading de-cision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the pro-posed method achieves a 1.52％～5.80％improvement compared with the benchmark algorithms in system service utility.