Task Offloading and Resource Allocation in NOMA-Enabled Vehicular Edge Computing Networks

The increasing adoption of mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) in vehicular networks is to reduce the execution delay of computation-intensive tasks and improve the spectrum efficiency. In this paper, we introduce a NOMA-assisted vehicular edge computing network, where vehicular users (VUs) form NOMA groups to share the radio resource with cellular users (CUs) for offloading their computing tasks to the MEC server in a highway scenario. Under the VU's execution delay constraints, we jointly optimize the computation resource allocation of the MEC server, the data transmission time, and the offloading decision to minimize the long-term energy consumption of the system. However, the long-term stochastic optimization problem is intricate due to the VU's mobility and the time-varying of the wireless channel. We thus propose a Lyapunov optimization based algorithm to transform the original problem into a single time slot optimization problem. Specifically, we decouple this problem into the computation resource allocation sub-problem solved at the MEC server and the offloading decision sub-problem solved at each VU. The optimal computation resource allocation is obtained by solving the knapsack problem, while the cross-entropy based algorithm is used to determine the optimal offloading decision for VUs. After that, our numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithms.