Computation Offloading in NOMA-Enabled Vehicular Fog Computing Networks

With the advent of the Internet of vehicles (IoV), the explosive growth of data from vehicular sensors places a heavy computing burden on green-enabled intelligent transportation systems. In this study, a paradigm of vehicular fog computing (VFC) is introduced, which is able to offload computation tasks to the nearby fog nodes. In addition, considering the advantage of non-orthogonal multiple access (NOMA), a NOMA-enabled VFC is proposed, in which a task vehicle, a main fog access point (F-AP), an idle vehicle, and other cooperative F-APs are involved to process the task. By jointly optimizing NOMA power allocation, task allocation ratio, bandwidth allocation and time slot allocation, the offloading data maximization problem is investigated. After simplifying the problem through mathematical analysis, the offloading data maximization problem is solved by the proposed interior-point method based on successive convex approximation (SCA). Simulation results show that the proposed offloading scheme performs better than other existing schemes in terms of offloading data.