Mobile Edge Computing Offloading Strategy based on Deep Reinforcement Learning for Space-Air-Ground Integrated Networks
To address the problem of high network latency,high energy consumption,and limited computational resources in traditional drone-based edge computing offloading,the paper presents an integrated air-space-ground network architecture with Low Earth Orbit Unmanned Aerial Vehicle(LEO-UAV)assisted task offloading,which can provide more available computational resources and network services for ground devices.To minimize the delay and energy consumption produced in offloading tasks,the problem is formulated as a Markov Decision Process(MDP)and further solved by the Multi-Agent Deep Deterministic Policy Gradient algorithm.Experimental results demonstrate that the MADDPG algorithm can effectively reduce the system's task processing delay and energy consumption by 44.45％and 61.35％,respectively,which verifies the reliability of the MADDPG algorithm in handling mobile edge computing offloading tasks.
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