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

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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

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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 National Natural Science Foundation of China (U25A20396)

Received Date: 2026-03-31
Accepted Date: 2026-07-29
Rev Recd Date: 2026-07-29

Available Online: 2026-08-08

Abstract

Abstract

Objective The rapid deployment of large-scale Low Earth Orbit (LEO) satellite constellations has intensified competition for orbital and spectrum resources. To improve spectrum utilization, different LEO satellite constellations commonly employ co-frequency reuse, which increases the risk of inter-system co-frequency interference. The high-speed motion of LEO satellites and their highly dynamic, heterogeneous topology further cause rapid variations in interference power, resulting in severe co-frequency interference. Existing interference assessment methods mainly rely on the regulations of the International Telecommunication Union (ITU), with the Interference-to-Noise Ratio (I/Noise) as a key evaluation metric. However, conventional ITU-based physical-iteration methods require repeated calculations of satellite positions, link attenuation and interference contributions for all visible satellites, causing computational cost to increase rapidly with constellation size. A complete interference assessment for a single ground station in a constellation of approximately 10 000 satellites can require more than 30 h. Recent deep-learning-based methods still commonly use all visible satellites as inputs and therefore do not adequately exploit the spatial sparsity of interference features. To address these limitations, an LEO constellation interference prediction and detection method driven by collaborative spatial feature mapping and temporal Transformer is proposed to reduce computational cost while maintaining accurate interference prediction and detection. Methods A spatiotemporal framework is developed to model dynamic co-frequency interference in large-scale heterogeneous multi-constellation LEO networks. Based on the ITU physical interference model, three key properties of the interference function are identified: permutation invariance, spatial sparsity and temporal continuity. The conventional physical-iteration process is therefore reformulated as a spatiotemporally decoupled feature-mapping problem. A spatial feature mapping module with adaptive attention and symmetric pooling is constructed to compress unordered and variable-length satellite interference-source features into fixed-dimensional, permutation-invariant spatial features. The attention mechanism adaptively focuses on dominant interference sources, whereas symmetric pooling using maximum and mean pooling captures extreme and global statistical characteristics while suppressing redundant satellite nodes. A temporal Transformer is then employed to model the long-range evolution of interference trajectories. The future I/Noise trajectory is predicted to support rapid interference detection under dynamic constellation configurations. Results and Discussions A large-scale heterogeneous LEO constellation scenario consisting of 6 800 Starlink satellites and 650 OneWeb satellites is simulated according to ITU regulations. Real Two-Line Element (TLE) data are used to propagate satellite orbits, and 100,000 time-series samples are generated at 0.5 s intervals. Parameter analysis shows that appropriate feature dimensions and encoder depths provide a favorable balance between feature extraction accuracy and computational cost (Figs. 4 and 5). The sampling interval and sliding-window length are further optimized to balance prediction accuracy and real-time inference performance (Figs. 6 and 7). Compared with the baseline methods, the proposed method produces interference trajectories that closely follow the physical-iteration ground truth (Fig. 8). At a cumulative probability of 90%, the absolute prediction error is maintained within 0.5 dB (Fig. 9). The method also maintains a high interference recall under a low false-alarm-rate constraint (Fig. 10). For a 20.0 s prediction horizon, the Root Mean Square Error (RMSE) remains at 0.45 dB, substantially lower than those of the baseline models. At a 20 s prediction step, the RMSE values of the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) network increase to 5.53 dB and 4.82 dB, respectively (Fig. 11). In terms of computational efficiency, the proposed method requires 14.2 ms for a single inference, which is only 4.1% of the computational time required by the ITU-based physical-iteration method (Table 3). The computational cost is therefore effectively decoupled from constellation size. Conclusions To address the high computational cost caused by the highly dynamic topology of LEO satellite networks, a collaborative spatial feature mapping and temporal Transformer-based interference prediction and detection method is proposed. The spatial feature mapping module compresses variable-length interference-source features into fixed-dimensional representations, whereas the temporal Transformer captures the long-range evolution of interference trajectories. Simulation results show that the proposed method provides accurate long-term trajectory tracking while remaining consistent with ITU-based interference assessment. Parameter-sensitivity experiments demonstrate that the proposed method can balance feature extraction accuracy and computational cost under different configurations. With its long-range dependency modeling capability, the temporal Transformer maintains an RMSE of 0.45 dB over a 20 s prediction horizon. By filtering redundant satellite nodes and decoupling computational cost from constellation size, the method reduces single-inference time to 14.2 ms. Comprehensive evaluations of prediction error distributions, detection performance and model parameters demonstrate that the proposed method achieves high prediction accuracy and substantially reduced computational complexity, providing a flexible engineering solution for interference monitoring in large-scale LEO constellation deployments.

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