Abstract
LEO satellite network (LEO-SN) constitutes an indispensable part of the space-air-ground integrated network (SAGIN). However, the characteristics of unbalanced traffic load, intricate orbits, high mobility, frequent link handovers, diverse storage and communication capabilities of nodes gradually lead to the ineffectiveness of the central routing planning methods. In order to enhance the flexibility and agility of the planning process, this paper presents an intelligent decentralized load balancing routing algorithm using deep reinforcement learning for the LEO-SN, which takes into consideration the queuing delay, storage space, communication bandwidth and propagation delay of merely one-hop satellite node. Simulation and analysis demonstrate that the proposed load balancing routing algorithm could converge quickly for both training and test sets, and possesses better performance than the comparison methods in terms of both the packet loss rate and transmission latency.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
