A Semantic Knowledge-enhanced Assessment Method for Spectrum Effectiveness of Low Earth Orbit Constellations

Authors

Yiqing Liu, Joint Laboratory for Space Information Network Frequency and Orbit Technology and Applications, China Satellite Network System Research Institute Co., Ltd, Beijing 100000, China
Qiuyang Zhang, Joint Laboratory for Space Information Network Frequency and Orbit Technology and Applications, China Satellite Network System Research Institute Co., Ltd, Beijing 100000, China
Chunyu Liu, Joint Laboratory for Space Information Network Frequency and Orbit Technology and Applications, China Satellite Network System Research Institute Co., Ltd, Beijing 100000, China
Yao Xue, Joint Laboratory for Space Information Network Frequency and Orbit Technology and Applications, China Satellite Network System Research Institute Co., Ltd, Beijing 100000, China
Zhiwei Wei, Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China
Yan Feng, Beijing Monitoring Station, State Radio Monitoring Center, Beijing 102609, China

Abstract

Abstract: In order to scientifically assess the spectrum effectiveness of low earth orbit constellations, the problems of insufficient adaptability of the traditional assessment framework and inaccurate estimation of KPIs under sparse data conditions were solved, a semantic knowledge-enhanced assessment method for spectrum effectiveness of low earth orbit constellations was proposed. A comprehensive multi-level, all band, and multi-dimensional spectrum effectiveness assessment framework covering link level, system level, geographical level, and service application level was constructed. A KPI intelligent prediction agent model integrating semantic knowledge and machine learning was proposed to quantify text-like design parameters using SentenceTransformer, so as to rapidly and accurately predict KPIs under conditions of sparse data. A comprehensive subjective and objective assessment decision-making technique that integrated the AHP enhanced by the LLM with EWM was designed. Simulation results show that compared with those of traditional machine learning models, the KPIs of this model have significantly reduced prediction errors. The proposed framework can effectively quantify the effectiveness differences of different beam strategies under different geographical environments; the LLM-assisted weighting method successfully generates subjective weights with good consistency and interpretability, and combined with objective data to reach a comprehensive evaluation conclusion.

Recommended Citation

Liu, Yiqing; Zhang, Qiuyang; Liu, Chunyu; Xue, Yao; Wei, Zhiwei; and Feng, Yan (2026) "A Semantic Knowledge-enhanced Assessment Method for Spectrum Effectiveness of Low Earth Orbit Constellations," Journal of System Simulation: Vol. 38: Iss. 2, Article 16.
DOI: 10.16182/j.issn1004731x.joss.25-0504
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss2/16

First Page

460

Last Page

475

CLC

TN927; TP391

Recommended Citation

Liu Yiqing, Zhang Qiuyang, Liu Chunyu, et al. A Semantic Knowledge-enhanced Assessment Method for Spectrum Effectiveness of Low Earth Orbit Constellations[J]. Journal of System Simulation, 2026, 38(2): 460-475.

Corresponding Author

Feng Yan

DOI

10.16182/j.issn1004731x.joss.25-0504