Intelligent Load Balancing Algorithm of Mega Constellation

Abstract

To overcome the local traffic congestion caused by the huge number of satellites in mega-constellation, a load balancing algorithm based on multi-agent deep reinforcement learning was proposed.Firstly, the satellites in the mega constellation were divided into clusters to perform the distributed management of the mega constellation, which could reduce the overhead of whole network.Then, based on the coordinated multi-agent deep reinforcement learning model, routing planning, which could be individually operated by satellites in the mega constellation, was designed to achieve the intra-cluster coordination.Additionally, a cluster state compression mechanism with autoencoder was proposed to compress the state space and improve the efficiency of multi-agent deep reinforcement learning.Finally, simulation results showed that compared with the traditional single-task routing algorithm, the proposed algorithm could increase the transmission success rate by more than 40% and the proposed algorithm could efficiently avoid local traffic congestion.

Keywords

• mega constellation;multi-agent deep reinforcement learning;load balancing;routing algorithm