4.7 Article

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Journal

IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 8, Issue 11, Pages 1800-1816

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004174

Keywords

BeiDou Navigation Satellite System (BDS); data-driven heuristic; inter-satellite link (ISL) scheduling; memetic algorithm; metaheuristic; quick-response

Funding

  1. National Natural Science Foundation of China [61773120]
  2. National Natural Science Fund for Distinguished Young Scholars of China [61525304]
  3. Foundation for the Author of National Excellent Doctoral Dissertation of China [2014-92]
  4. Hunan Postgraduate Research Innovation Project [CX2018B022]
  5. China Scholarship Council-Leiden University Scholarship

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The study proposes a data-driven heuristic assisted memetic algorithm (DHMA) for inter-satellite link (ISL) scheduling in the BeiDou Navigation Satellite System (BDS). By addressing normal and quick-response scheduling separately, and training with high-quality data, the DHMA demonstrates efficient performance in experiments.
Inter-satellite link (ISL) scheduling is required by the BeiDou Navigation Satellite System (BDS) to guarantee the system ranging and communication performance. In the BDS, a great number of ISL scheduling instances must be addressed every day, which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications. To address the dual requirements of normal and quick-response ISL schedulings, a data-driven heuristic assisted memetic algorithm (DHMA) is proposed in this paper, which includes a high-performance memetic algorithm (MA) and a data-driven heuristic. In normal situations, the high-performance MA that hybridizes parallelism, competition, and evolution strategies is performed for high-quality ISL scheduling solutions over time. When in quick-response situations, the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model, which is trained from the high-quality MA solutions. The main idea of the DHMA is to address normal and quick-response schedulings separately, while high-quality normal scheduling data are trained for quick-response use. In addition, this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness. A seven-day experimental study with 10 080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal (in 84 hours) and quick-response (in 0.62 hour) situations, which can well meet the dual scheduling requirements in real-world BDS applications.

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