Stigmergy and Hierarchical Learning for Routing Optimization in Multi-domain Collaborative Satellite Networks

Yuanfeng Li, Qi Zhang, Haipeng Yao, Ran Gao, Xiangjun Xin, F. Richard Yu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The integration of Software-Defined Networking (SDN) and Artificial Intelligence (AI) presents promising opportunities for managing and optimizing LEO satellite network routing. However, as the scale and coverage of satellite networks continue to expand, challenges are posed to both centralized and distributed architectures in terms of managing network information and coping with routing complexity. To overcome these challenges, leveraging distributed SDN technology, a stigmergy multi-agent hierarchical deep reinforcement learning routing algorithm is proposed in multi-domain collaborative satellite networks. A pheromone-based mechanism is incorporated to facilitate collaboration during independent training, and hierarchical control is employed to decouple the complexity of cross-domain routing decisions. Simulation results demonstrate that our proposed algorithm exhibits good scalability and performance in large-scale satellite networks.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Journal on Selected Areas in Communications
DOIs
Publication statusAccepted/In press - 2024
Externally publishedYes

Keywords

  • Collaboration
  • Complexity theory
  • Deep reinforcement learning
  • Distributed AI routing
  • Hierarchical reinforcement learning
  • Low Earth Orbit (LEO) satellite networks
  • Low earth orbit satellites
  • Optimization
  • Routing
  • Satellites
  • Stigmergy learning

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