Research on LEO Constellation Interference Prediction and Detection Algorithm Driven by Collaborative Spatial Feature Mapping and Temporal Transformer

YANG Boyu; QIU Kun; CHEN Zhe; ZHAO Jin; GAO Yue

doi:10.11999/JEIT260368

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2026 >

YANG Boyu, QIU Kun, CHEN Zhe, ZHAO Jin, GAO Yue. Research on LEO Constellation Interference Prediction and Detection Algorithm Driven by Collaborative Spatial Feature Mapping and Temporal Transformer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260368

Citation:

YANG Boyu, QIU Kun, CHEN Zhe, ZHAO Jin, GAO Yue. Research on LEO Constellation Interference Prediction and Detection Algorithm Driven by Collaborative Spatial Feature Mapping and Temporal Transformer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260368

Citation:

YANG Boyu, QIU Kun, CHEN Zhe, ZHAO Jin, GAO Yue. Research on LEO Constellation Interference Prediction and Detection Algorithm Driven by Collaborative Spatial Feature Mapping and Temporal Transformer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260368

PDF( 1968 KB)

Research on LEO Constellation Interference Prediction and Detection Algorithm Driven by Collaborative Spatial Feature Mapping and Temporal Transformer

doi: 10.11999/JEIT260368 cstr: 32379.14.JEIT260368

YANG Boyu¹,
QIU Kun^{2
,
,},
CHEN Zhe²,
ZHAO Jin¹,
GAO Yue²

1.
College of Computer Science and Artificial Intelligence, Institute of Space Internet, Fudan University, Shanghai 200438, China
2.
Institute of Space Internet, Fudan University, Shanghai 200438, China

Funds: The Natural Science Foundation of China (U25A20396)

Accepted Date: 2026-07-29
Rev Recd Date: 2026-07-29

Available Online: 2026-08-08

Abstract

Abstract

Objective The continuous putting into use of large-scale low Earth orbit (LEO) satellite constellation groups has caused a serious crowding of orbital space resources. The wide employment of frequency repeating technique by satellite operation persons has seriously brought challenges to the frequency spectrum resources of current LEO satellite network systems. At the same time, the inner quick motion and changing topological structure of LEO satellites bring about violent changes in satellite electric power lead to serious co-frequency interference between satellites. Existing detection methods for interference between satellites mainly depend on compulsory rules formulated by the International Telecommunication Union (ITU), which employ the Interference-to-Noise Ratio (I/N) to serve as the primary indicator. Nevertheless, the link attenuation formulae and satellite position computations that are contained in the calculation procedure bring about a sharp rise in total calculation cost, hence it is hard to satisfy the real-time interference check demands of large-scale satellite constellation working. The recent interference detection algorithms which are based on deep learning are still limited to taking all visible satellites as input, thus they cannot carry out constraint on the spatial sparsity of interference feature distribution. For solving these problems, this paper puts forward a LEO constellation interference prediction and detection method which is based on spatial feature mapping and temporal Transformer, therefore it greatly decreases calculation complexity while it realizes high-efficiency interference prediction and detection. Methods The proposed spatial-temporal Transformer framework efficiently models dynamic co-frequency interference in large-scale heterogeneous multi-constellation LEO networks. By analyzing ITU interference formulations, we extract three core physical properties: permutation invariance, spatial sparsity, and temporal continuity. Consequently, the traditional physical iteration is reframed as a spatio-temporally decoupled feature mapping problem. A dedicated spatial mapping module, leveraging attention and parallel pooling mechanisms, compresses raw satellite parameters into robust permutation-invariant features. This effectively highlights key interference sources while filtering redundant background nodes. Finally, a temporal Transformer captures the dynamics of interference trajectories, achieving high-accuracy prediction and significantly reducing computational complexity for real-time monitoring applications. Results and Discussions The interference prediction and detection algorithm for LEO constellation uses space feature mapping and a time Transformer to carry out interference detection and forecast by making use of high-fidelity data. According to ITU regulations, one high-mobility LEO satellite scene has been simulated, which includes thousands of real satellites from Starlink and OneWeb constellations. Parameter analysis results show that setting reasonable feature dimensions and encoder layers can achieve an optimal balance between feature extraction accuracy and computational cost (Fig. 4 and Fig. 5). By setting model parameters such as sampling step size, prediction accuracy and engineering real-time performance can be comprehensively considered (Fig. 6 and Fig. 7). Simulation results show that compared to baseline methods, the predicted trajectory of the proposed method closely matches the baseline ground truth (Fig. 8), with the absolute deviation controlled within 0.5 dB at a 90% cumulative probability (Fig. 9). Furthermore, the algorithm maintains an extremely high interference detection rate with low false alarms (Fig. 10), and the Root Mean Square Error (RMSE) remains stable at 0.45 dB within a 20.0 s prediction window, significantly outperforming traditional regression and neural network models (Fig. 11). In terms of computational efficiency, the algorithm in this paper takes 14.2 ms for a single inference, which is only 4.1% of the time required by the traditional ITU-based physical iteration method (Table 3), effectively decoupling computational overhead and constellation scale. Conclusions For the problem that LEO satellite network topology has extremely strong dynamic property, and traditional physical iteration interference checking methods have limitations brought by sharply risen computation complexity, this paper puts forward a low-Earth orbit constellation interference checking algorithm which combines spatial feature mapping and temporal Transformer, therefore it effectively makes computation cost and constellation size decouple. The results of simulation show that, when the comparison is made with baseline methods, this algorithm possesses fine long-term dynamic tracking capabilities, and at the same time it keeps conformity with ITU evaluation rules. Experiments which carry on equipment analysis with different parameters have proven that the algorithm we put forward can obtain a balance between feature extraction accuracy and computation spending. By utilizing the long-distance dependence modeling ability of time-domain Transformer, the RMSE still keeps steady at 0.45 dB at identical time step. Through the method of eliminating unnecessary background satellite nodes, the single inference time has been reduced to 14.2 ms. By means of manifold experiments, which contain error distribution assessment and model parameter analysis, the algorithm put forward by us obtains the best precision and full decoupling of calculation cost from constellation scale, hence it gives a highly flexible engineering scheme for future super-large constellation arrangements.
- Low Earth Orbit satellite constellations,
- Co-frequency interference,
- Spatial feature mapping,
- Transformer architecture,
- Interference-to-Noise Ratio

FullText(HTML)

References(28)

References

[1]	LIU Jiajia, SHI Yongpeng, FADLULLAH Z M, et al. Space-air-ground integrated network: A survey[J]. IEEE Communications Surveys & Tutorials, 2018, 20(4): 2714–2741. doi: 10.1109/COMST.2018.2841996.
[2]	YUE Pingyue, AN Jianping, ZHANG Jiankang, et al. Low earth orbit satellite security and reliability: Issues, solutions, and the road ahead[J]. IEEE Communications Surveys & Tutorials, 2023, 25(3): 1604–1652. doi: 10.1109/COMST.2023.3296160.
[3]	苏昭阳, 刘留, 艾渤, 等. 面向低轨卫星的星地信道模型综述[J]. 电子与信息学报, 2024, 46(5): 1684–1702. doi: 10.11999/JEIT230941. SU Zhaoyang, LIU Liu, AI Bo, et al. Survey of satellite-ground channel models for low earth orbit satellites[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1684–1702. doi: 10.11999/JEIT230941.
[4]	杜星葵, 束妮娜, 刘春生, 等. 低轨卫星网络安全问题及防御技术综述[J]. 电子与信息学报, 2025, 47(6): 1609–1622. doi: 10.11999/JEIT240957. DU Xingkui, SHU Nina, LIU Chunsheng, et al. Overview of security issues and defense technologies for low earth orbit satellite network[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1609–1622. doi: 10.11999/JEIT240957.
[5]	International Telecommunication Union (ITU). Radio Regulations, Edition of 2024[M]. Geneva: ITU, 2024. (查阅网上资料, 未找到本条文献页码信息, 请补充).
[6]	International Telecommunication Union. Recommendation ITU-R S. 1432–1 Apportionment of the allowable error performance degradations to fixed-satellite service (FSS) hypothetical reference digital paths arising from time invariant interference for systems operating below 30 GHz[S]. Geneva: ITU, 2006.
[7]	武燕燕, 吴松, 邓伟. 6G天地一体通感算智能协同网络资源管理技术综述[J]. 电子与信息学报, 2025, 47(8): 2448–2472. doi: 10.11999/JEIT250140. WU Yanyan, WU Song, and DENG Wei. An overview of resource management technology of 6G integrated communication, sensing, and computation enabled satellite-terrestrial intelligent network[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2448–2472. doi: 10.11999/JEIT250140.
[8]	International Telecommunication Union. Space networks and related software[EB/OL]. https://www.itu.int/en/ITU-R/software/Pages/space-network-software.aspx, 2026.
[9]	刘畅, 魏文康, 李伟. 对地静止轨道卫星网络间的频率干扰分析计算问题[J]. 天地一体化信息网络, 2021, 2(1): 52–59,68. doi: 10.11959/j.issn.2096-8930.2021007. LIU Chang, WEI Wenkang, and LI Wei. Frequency interference analysis and calculation of geostationary-orbit satellite network[J]. Space-Integrated-Ground Information Networks, 2021, 2(1): 52–59,68. doi: 10.11959/j.issn.2096-8930.2021007.
[10]	KIM D, PARK J, CHOI J, et al. Spectrum sharing between low earth orbit satellite and terrestrial networks: A stochastic geometry perspective analysis[J]. IEEE Transactions on Wireless Communications, 2026, 25: 8905–8921. doi: 10.1109/TWC.2025.3646185.
[11]	RU Juanjuan, WANG Ruibo, and ALOUINI M S. Coverage and rate analysis of follower-based LEO satellite networks: A stochastic geometry approach[J]. IEEE Transactions on Wireless Communications, 2026, 25: 12662–12675. doi: 10.1109/TWC.2026.3666549.
[12]	李伟, 严康, 耿静茹, 等. NGSO通信星座系统间同频干扰场景与建模研究[J]. 天地一体化信息网络, 2021, 2(1): 20–27. doi: 10.11959/j.issn.2096-8930.2021003. LI Wei, YAN Kang, GENG Jingru, et al. Research on scenarios and modeling of co-frequency interference between NGSO communication constellation systems[J]. Space-Integrated-Ground Information Networks, 2021, 2(1): 20–27. doi: 10.11959/j.issn.2096-8930.2021003.
[13]	HEYDARISHAHREZA N, HAN Tao, and ANSARI N. Spectrum sharing and interference management for 6G LEO satellite-terrestrial network integration[J]. IEEE Communications Surveys & Tutorials, 2025, 27(5): 2794–2825. doi: 10.1109/COMST.2024.3507019.
[14]	LIM B and VU M. Interference analysis for coexistence of terrestrial networks with satellite services[J]. IEEE Transactions on Wireless Communications, 2024, 23(4): 3146–3161. doi: 10.1109/TWC.2023.3306010.
[15]	TORRENS S A, PETROV V, and JORNET J M. Modeling interference from millimeter wave and terahertz bands cross-links in low earth orbit satellite networks for 6G and beyond[J]. IEEE Journal on Selected Areas in Communications, 2024, 42(5): 1371–1386. doi: 10.1109/JSAC.2024.3365894.
[16]	LIU Xiangnan, ZHANG Haijun, SHENG Min, et al. Ultra dense satellite-enabled 6G networks: Resource optimization and interference management[J]. China Communications, 2023, 20(10): 262–275. doi: 10.23919/JCC.ea.2021-0740.202302.
[17]	ZHANG Bo, GAO Ronghao, GAO Pengyu, et al. Interference-suppressed joint channel and power allocation for downlinks in large-scale satellite networks: A dynamic hypergraph neural network approach[J]. IEEE Transactions on Wireless Communications, 2026, 25: 728–742. doi: 10.1109/TWC.2025.3586230.
[18]	SAIFALDAWLA A, ORTIZ F, LAGUNAS E, et al. Convolutional autoencoders for non-geostationary satellite interference detection[C]. Proceedings of 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, USA, 2024: 1334–1339. doi: 10.1109/ICCWorkshops59551.2024.10615457.
[19]	SAIFALDAWLA A, ORTIZ F, LAGUNAS E, et al. GenAI-based models for NGSO satellites interference detection[J]. IEEE Transactions on Machine Learning in Communications and Networking, 2024, 2: 904–924. doi: 10.1109/TMLCN.2024.3418933.
[20]	YANG Chunyu, YANG Boyu, QIU Kun, et al. DualAttWaveNet: Multiscale attention networks for satellite interference detection[C]. Proceedings of 2025 IEEE/CIC International Conference on Communications in China (ICCC), Shanghai, China, 2025: 1–6. doi: 10.1109/ICCC65529.2025.11148638.
[21]	International Telecommunication Union. Non-geostationary-satellite networks (Non-GSO): ITU regulatory procedures[EB/OL]. https://www.itu.int/en/ITU-R/space/support/nonGSO/Pages/default.aspx, 2026.
[22]	VALLADO D A, CRAWFORD P, HUJSAK R, et al. Revisiting spacetrack report #3[C]. Proceedings of AIAA/AAS Astrodynamics Specialist Conference and Exhibit, Keystone, USA, 2006: AIAA-2006–6753. doi: 10.2514/6.2006-6753.
[23]	International Telecommunication Union. Recommendation ITU-R S. 1503-3 Functional description to be used in developing software tools for determining conformity of non-geostationary-satellite orbit fixed-satellite service systems or networks with limits contained in article 22 of the radio regulations[S]. Geneva: ITU, 2018.
[24]	International Telecommunication Union. Recommendation ITU-R S. 580-6 Radiation diagrams for use as design objectives for antennas of earth stations operating with geostationary satellites[S]. Geneva: ITU, 2004.
[25]	LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539.
[26]	GREFF K, SRIVASTAVA R K, KOUTNIK J, et al. LSTM: A search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2222–2232. doi: 10.1109/TNNLS.2016.2582924.
[27]	PELLETIER C, WEBB G I, and PETITJEAN F. Temporal convolutional neural network for the classification of satellite image time series[J]. Remote Sensing, 2019, 11(5): 523. doi: 10.3390/rs11050523.
[28]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 6000–6010.