Dynamic Power–Latency Tradeoff for Mobile Edge Computation Offloading in NOMA-Based Networks

Abstract

Mobile edge computing (MEC) has been recognized as an emerging technology that allows users to send the computation intensive tasks to the MEC server deployed at the macro base station. This process overcomes the limitations of mobile devices (MDs), instead of sending that data to a cloud server which is far away from MDs. In addition, MEC results in decreasing the latency of cloud computing and improves the quality of service. In this paper, a MEC scenario in 5G networks is considered, in which several users request for computation service from the MEC server in the cell. We assume that users can access the radio spectrum by the non-orthogonal multiple access (NOMA) protocol and employ the queuing theory in the user side. The main goal is to minimize the total power consumption for computing by users with the stability condition of the buffer queue to investigate the power-latency trade-off, which the modeling of the system leads to a conditional stochastic optimization problem. In order to obtain an optimum solution, we employ the Lyapunov optimization method along with successive convex approximation (SCA). Extensive simulations are conducted to illustrate the advantages of the proposed algorithm in terms of power-latency trade-off of the joint optimization of communication and computing resources and the superior performance over other benchmark schemes.