DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks

Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satell...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 15; H. 11; S. 2801
Hauptverfasser:	Dong, Feihu, Song, Jiaxin, Zhang, Yasheng, Wang, Yuqi, Huang, Tao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 28.05.2023
Schlagworte:	Algorithms Altitude Artificial intelligence Communications networks Communications systems Comparative analysis Decision making Deep learning deep reinforcement learning Design high dynamics Load balancing Load balancing (Computers) Load distribution low earth orbit Low earth orbit satellites Low earth orbits Machine learning Methods Microbalances Neural networks Nodes Queues Remote sensing Rocket launches Route planning Satellite communications Satellite networks satellite routing algorithm Satellites Technology application Utilization China
ISSN:	2072-4292, 2072-4292
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Abstract	Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.
AbstractList	Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.
Audience	Academic
Author	Dong, Feihu Zhang, Yasheng Wang, Yuqi Song, Jiaxin Huang, Tao
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Title	DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks
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