Task offloading and resource allocation in cellular heterogeneous networks for NOMA-based mobile edge computing

Abstract

Mobile edge computing (MEC) is an effective strategy for real-time data processing and lowering data transmission latency between end devices and the cloud, which can minimize network congestion and improve user experience. This paper examines the problem of system energy consumption minimization in a cellular heterogeneous network (HetNets) MEC architecture with non-orthogonal multiple access (NOMA). The NOMA protocol is used by each base station to serve users within its small cell, and each base station is fitted with an edge computing server. Because of the parameter coupling, energy consumption minimization is a difficult non-convex optimization problem. As a result, the problem is divided into three subproblems, the computational and communication resource allocation subproblem and the task offloading subproblem being optimally solved by their convexity, while the subchannel allocation and power control subproblems are solved by sequential convex programming. Then, for each of the three subproblems, an efficient iterative technique is presented. Simulation results show that the partial offloading strategy under NOMA (PO-NOMA) proposed in this paper outperforms several baseline strategy, including all local computation, complete offloading, random offloading strategy, and greedy offloading strategy, and outperforms Orthogonal Multiple Access (OMA)-based MEC in cellular heterogeneous networks in a cellular heterogeneous network scenario.