Long-Term Energy Consumption Minimization in NOMA-Enabled Vehicular Edge Computing Networks

Mobile Edge Computing (MEC) has envisioned to be a promising technology to provide more efficient services for computation-intensive but delay-sensitive onboard mobile services. In this paper, the Non-Orthogonal Multiple Access (NOMA) technology is applied in a vehicular edge computing network, in which vehicular users (VUs) can offload partial computation tasks to MEC servers over wireless channels for remote execution. In this network, an optimization problem for the long-term energy consumption of the system is presented and aims to minimize it by jointly optimizing the Successive Interference Cancellation (SIC) ordering of NOMA, the VUs’ transmit power for computation offloading, and computation resource allocation of the MEC server. To deal with the intractable long-term optimization problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC ordering sub-problems. For the resource allocation sub-problem, we exploit its convexity through the transformation and reparameterization, and derive the optimal solution in accordance with the Karush-Kuhn-Tucker (KKT) conditions and the gradient descent algorithm. After that, we propose a low-complexity algorithm by leveraging the Tabu search to obtain the sub-optimal SIC ordering. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to Frequency Division Multiple Access (FDMA).

I. Introduction

Recently, the unprecedented progress of Internet of Vehicles (IoV) has been witnessed as a new paradigm that enables Vehicle-to-Everything (V2X) communication to support autonomous driving and real-time navigation, and so on [1]. With the proliferation of computation-intensive yet latency-sensitive tasks in vehicular users (VUs), the VUs are difficult to perform the efficient local computation due to the limited computation capacity. Mobile Edge Computing (MEC) has been therefore recognized as a prospective technology to address the limited computing capabilities of VUs by pushing computation resources down to the proximity of VUs [2]. Thus, the computation tasks of VUs can be offloaded and computed at cellular base stations (BSs) or roadside units equipped with MEC servers, which can effectively improve the computation efficiency and reduce the task execution latency [3].