HetMEC: Heterogeneous Multi-Layer Mobile Edge Computing in the 6 G Era

Abstract

Driven by an increasing number of mobile applications, mobile edge computing (MEC) has been considered as a promising candidate to support the huge amount of data processing services. However, the conventional MEC suffers from the insufficient utilization of computing and transmission resources through the entire network, resulting in inevitably long processing and transmission latency especially to computation-intensive applications in the 6 G era. In this article, we propose a heterogeneous multi-layer mobile edge computing (HetMEC), where different devices, ranging from edge devices (EDs), i.e., the mobile devices that generate raw data of computing tasks in the radio access networks, to the cloud center (CC), are inherently involved different layers of the network and collaborate for data processing. To support a low-latency service, a reinforcement learning-based framework is constructed to adapt to the unstable wireless environments as well as the dynamically varying data generation speed of each ED. Under this framework, key research issues and solutions including task offloading, cognitive radio based spectrum access, pricing scheme design, and network congestion control are presented. Some further research directions and opening issues are also discussed in the perception of network planning and optimization, network control, and application-specific issues.