TY - GEN
T1 - Feeder Link Handover Strategy Based on Hybrid Clonal Selection Algorithm in the Dense LEO Constellation
AU - Yu, Tianqi
AU - Zhang, Qiang
AU - Meng, Weixiao
AU - Chen, Shuyi
AU - Godoy, Sebastián E.
AU - Saavedra, Gabriel
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - In order to extend the connection time of the feeder link for dense LEO constellation and make full use of the resources of the gateway station, this article proposes a feeder link handover strategy for dense LEO constellation based on Hybrid Clonal Selection Algorithm. Firstly, the satellite-earth visible window is defined as an independent task, the link handover problem is transformed into a task assignment problem, and the corresponding mathematical model is established. Then, the task execution time window is calculated using the Conflict Resolution Algorithm, and the associated satellite coincidence time window is reassigned. Then, the Clonal Selection Algorithm is used for global search to complete the whole link handover. In order to enhance the local optimization ability of the algorithm, a Hybrid Clonal Selection Algorithm is proposed by combining the local search algorithm of adaptive neighborhood selection. Finally, the model is set up and the performance comparative analysis experiment is carried out. The experimental results show that the Hybrid Clonal Selection Algorithm can improve the task scheduling rate and extend the link connection time without sacrificing the link switching times.
AB - In order to extend the connection time of the feeder link for dense LEO constellation and make full use of the resources of the gateway station, this article proposes a feeder link handover strategy for dense LEO constellation based on Hybrid Clonal Selection Algorithm. Firstly, the satellite-earth visible window is defined as an independent task, the link handover problem is transformed into a task assignment problem, and the corresponding mathematical model is established. Then, the task execution time window is calculated using the Conflict Resolution Algorithm, and the associated satellite coincidence time window is reassigned. Then, the Clonal Selection Algorithm is used for global search to complete the whole link handover. In order to enhance the local optimization ability of the algorithm, a Hybrid Clonal Selection Algorithm is proposed by combining the local search algorithm of adaptive neighborhood selection. Finally, the model is set up and the performance comparative analysis experiment is carried out. The experimental results show that the Hybrid Clonal Selection Algorithm can improve the task scheduling rate and extend the link connection time without sacrificing the link switching times.
KW - Dense LEO Constellation
KW - Feeder Link Handover Strategy
KW - Hybrid Clonal Selection Algorithm
UR - https://www.scopus.com/pages/publications/105002131245
U2 - 10.1007/978-3-031-86203-8_30
DO - 10.1007/978-3-031-86203-8_30
M3 - 会议稿件
AN - SCOPUS:105002131245
SN - 9783031862021
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 381
EP - 398
BT - Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings
A2 - Chen, Hsiao-Hwa
A2 - Meng, Weixiao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024
Y2 - 23 August 2024 through 25 August 2024
ER -