When Mobile-Edge Computing (MEC) Meets Nonorthogonal Multiple Access (NOMA) for the Internet of Things (IoT): System Design and Optimization

Mobile-edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and nonorthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this article, we consider a NOMA-MEC-based Internet-of-Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then, we maximize the total energy saving also from two aspects, i.e., the device-side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.

Automated summary

The article discusses a NOMA-MEC-based Internet-of-Things network and proposes a joint optimization framework to maximize effective system capacity and total energy saving. The effective system capacity is improved by introducing NOMA from the wireless side and optimizing task offloading decisions from the IoT device side, while energy saving is maximized through computation resource allocation on the device-side and various techniques on the wireless side. The proposed joint optimization algorithm demonstrates good performance in both effective system capacity optimization and energy saving maximization through abundant simulation results.