Dynamic Resource Allocation and Task Offloading for NOMA-Enabled IoT Services in MEC

Abstract

Integrating nonorthogonal multiple access (NOMA) and edge computing into the Internet of Things (IoT) for resource allocation and computing offloading can effectively reduce delay and energy consumption and improve spectrum efficiency. Computation tasks can be split into several independent subtasks and can be locally processed by IoT devices or offloaded to the MEC servers to process. The limited computing resources deteriorate the system performance. Thus, it is crucial to design the reasonable allocation strategies of computation resource and transmission power resource. In this paper, we jointly determine the CPU-cycle frequency allocation and transmission power allocation and formulate a stochastic optimization to minimize the energy consumption of IoT devices. Based on the Lyapunov optimization theory, we decompose the optimization problem into two deterministic subproblems to solve separately. One of them is obtained by seeking the first derivative, and the other is solved by using the best response idea after establishing the game model. Meanwhile, we propose a dynamic resource allocation and task offloading (DRATO) algorithm. Moreover, the simulation experiments show that the proposed algorithm effectively improves system performance and reduces energy consumption compared to three other benchmark methods.