Joint Task Offloading and Resource Allocation for Mobile Edge Computing in Ultra-Dense Network

Abstract

Mobile edge computing (MEC) enabled user-centric ultra-dense network (UDN) is a promising solution to the energy constrained mobile users with delay-sensitive and computation intensive applications. Due to the high density of access points and MEC servers in UDN, both task offloading decision, power control, communication and computation resource allocation need to be addressed. To this end, we consider the joint problem of task offloading, uplink transmission power control, communication and computation resource allocation in a UDN, where the task of each user can be partitioned into several subtasks and offloaded to different access points. To handle the continuous action space of task partitioning and power control, we propose a multi-agent deep deterministic policy gradient (MADDPG) approach to solve this problem. Simulation results reveal the effectiveness of the proposed method.