A Spatio-Temporal Feature Fusion LSTM Relaxation Measurement Method for LEO Satellites

YANG Mengxin; ZHANG Qingting; ZENG Lingxin; GU Yixiao; ZENG Dan; XIA Bin

doi:10.11999/JEIT251146

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YANG Mengxin, ZHANG Qingting, ZENG Lingxin, GU Yixiao, ZENG Dan, XIA Bin. A Spatio-Temporal Feature Fusion LSTM Relaxation Measurement Method for LEO Satellites[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251146

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YANG Mengxin, ZHANG Qingting, ZENG Lingxin, GU Yixiao, ZENG Dan, XIA Bin. A Spatio-Temporal Feature Fusion LSTM Relaxation Measurement Method for LEO Satellites[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251146

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A Spatio-Temporal Feature Fusion LSTM Relaxation Measurement Method for LEO Satellites

doi: 10.11999/JEIT251146 cstr: 32379.14.JEIT251146

1.
Shanghai University, Shanghai 200444, China
2.
The China Satellite Network Exploration Co.Ltd., Chongqing 400065, China
3.
Shanghai Jiao Tong University, Shanghai 200240, China

Funds: The Natural Science Foundation of Chongqing, China (CSTB2024NSCQ-LMX0024), The Natural Science Foundation of Shanghai (24ZR1421800)

Received Date: 2025-11-01
Accepted Date: 2026-01-06
Rev Recd Date: 2026-01-05

Available Online: 2026-01-10

Abstract

Abstract

Objective The high dynamics of Low Earth Orbit (LEO) satellite communication systems cause frequent link measurements. Existing schemes mainly adopt threshold-based or standard spatio-temporal prediction-based relaxation measurement strategies to mitigate this issue. However, these approaches do not effectively capture the dynamic evolution of the importance of historical data and multiple measurement metrics induced by satellite mobility. Therefore, adaptation to highly time-varying satellite-ground link environments remains limited. To address this problem, a spatio-temporal feature fusion relaxation measurement method based on a Long Short-Term Memory (LSTM) network is proposed for LEO satellite communication. An LSTM recurrent neural network integrated with a dual-attention mechanism is constructed. The LSTM extracts correlations among historical measurement data, whereas temporal attention and variable attention focus on key time instants and significant features, respectively. On this basis, the measurement frequency point set and the number of relaxation periods are jointly predicted. Intelligent link measurement is then performed using the selected frequency point set and relaxation period, enabling adaptive and energy-efficient link monitoring in LEO satellite systems. Methods The proposed spatio-temporal feature fusion LSTM-based relaxation measurement method employs a Dual-Attention LSTM (DA-LSTM) model to reduce measurement overhead while maintaining reliable link monitoring. Historical link quality indicators, including Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), and Doppler shift, together with satellite ephemeris information, are used as model inputs. These features capture temporal and spatial variations and support the joint prediction of a subset of measurement frequency points and their corresponding relaxation periods. Based on the predicted results, the terminal performs adaptive frequency point selection and dynamic relaxation period adjustment or executes full-band measurements with a fixed measurement period. This process enables adaptive and energy-efficient link monitoring while preserving communication performance in LEO satellite systems. Results and Discussions The proposed relaxation measurement method applies the DA-LSTM model to predict measurement frequency points and the number of relaxation periods using historical link quality information. Simulation results show higher convergence efficiency, higher training accuracy, and lower loss for both frequency point selection and relaxation period selection compared with baseline methods (Fig. 4 and Fig. 5). The proposed measurement algorithm achieves an average measurement frequency below 30% with minimal performance degradation (Table 3). This result is attributed to the adaptive selection of high-quality frequency points and dynamic adjustment of the measurement period. The trade-off between measurement frequency and communication performance is further examined (Fig. 6 and Fig. 7), indicating that the proposed method achieves a better balance than baseline methods under different terminal speeds. Additional simulations under different terminal speeds (Fig. 8) and different maximum relaxation periods (Fig. 9) further confirm that high energy efficiency and communication performance are maintained under diverse operational conditions. Conclusions This work addresses the challenge of dynamic spatio-temporal importance evolution caused by satellite mobility, which limits the effectiveness of existing relaxation measurement strategies. A DA-LSTM–based relaxation measurement algorithm is proposed to predict both the measurement frequency point set and the number of relaxation periods by extracting spatio-temporal correlations from historical link quality data. Simulation results under various scenarios show that: (1) the proposed algorithm achieves higher convergence efficiency and training accuracy than baseline methods; (2) adaptive selection of high-quality frequency points and dynamic adjustment of relaxation periods maintain a favorable balance between measurement frequency and communication reliability; and (3) the method remains effective across different terminal speeds and maximum relaxation periods, indicating good scalability and robustness in dynamic operational environments. The current study is limited to simulations and does not consider hardware constraints, atmospheric effects, or real-time processing requirements. These factors should be investigated in future work.
- Low Earth Orbit (LEO) satellite communication,
- Relaxation measurement,
- Frequency point selection,
- Measurement period relaxation

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References(23)

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