Energy-Minimization Task Offloading and Resource Allocation for Mobile Edge Computing in NOMA Heterogeneous Networks

Abstract

Mobile edge computing (MEC) has been regarded as a promising technology to improve the quality of service (QoS) of users by offloading their computation-intensive and delay-sensitive tasks to the wireless network edge. Furthermore, applying non-orthogonal multiple access (NOMA) technology in heterogeneous networks (HetNets) has become a trend to improve system throughput and spectrum efficiency. Exploiting these benefits, we investigate the joint task offloading and resource allocation problem for MEC in NOMA-based HetNets. To minimize the energyconsumption of all users, we jointly consider task offloading decision, local CPU frequency scheduling, power control, computation resource and subchannel resource allocation. The optimization problem is challenging due to the combinatorial nature of the offloading decision and its strong coupling with the resource allocation. We thus decouple the problem into two sub-problems of offloading decision and resource allocation, and propose an efficient approach to find the joint solution by solving these two sub-problems iteratively. Simulation results show that the proposed approach can achieve a near optimal performance, and outperform other benchmark approaches in terms of saving energy consumption of all users with an acceptable complexity.