Deep reinforcement learning based joint partial computation offloading and resource allocation in mobility-aware MEC system

Abstract

Mobile edge computing (MEC) emerges as a paradigm to free mobile devices (MDs) from increasingly dense computing workloads in 6G networks. The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers. Renewable energy harvested by energy harvesting equipments (EHQs) is considered as a promising power supply for users to process and offload tasks. In this paper, we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy. We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system. We propose an effective joint partial computation offloading and resource allocation (CORA) algorithm which is based on deep reinforcement learning (DRL) to obtain the optimal scheduling without prior knowledge of task arrival, renewable energy arrival as well as channel condition. The simulation results verify the efficiency of the proposed algorithm, which undoubtedly minimizes the cost of MDs compared with other benchmarks.