Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation

TL;DR

This paper introduces a multi-agent deep reinforcement learning approach for energy-efficient computation offloading in UAV-assisted mobile edge networks, leveraging digital twin technology for real-time MEC network management.

Contribution

It proposes a novel multi-agent MAPPO-based method for resource scheduling in UAV-assisted MEC, incorporating digital twin for enhanced network control.

Findings

Significantly reduces energy consumption of UAVs and MUs.

Outperforms benchmark algorithms in efficiency and resource utilization.

Achieves faster convergence and better performance in simulations.

Abstract

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.