Dynamic Resource Scheduling and Frequency Scaling in NOMA-Based Multi-access Edge Computing System

Merging Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) into the sixth generation (6G) Internet of Things (IoT) can satisfy the computationally intensive task's requirement of extensible and low-energy consumption service. However, it is challenging to assigning task in MEC system due to that the channel transforms over time in dynamically varying network environments. In this paper, we propose a dynamic resource scheduling and frequency scaling algorithm (DRSFS) to allocate tasks and MEC frequency optimally. On the basis of Lyapunov optimization technique, DRSFS converts the long-range random optimization problem to a suite of determinate sub-problems and obtain the optimal solution. DRSFS can obtain an optimal offload strategy by utilizing dynamic programming theory, which can be verified by the effects of different parameters. The simulation experiment results shows the superiority of DRSFS by comparing it with other two baseline algorithms in the field of the energy consumption and the queue length.