面向低轨卫星的时空特征融合LSTM松弛测量方法

杨孟欣; 张青婷; 曾令昕; 顾忆宵; 曾丹; 夏斌

doi:10.11999/JEIT251146

面向低轨卫星的时空特征融合LSTM松弛测量方法

doi: 10.11999/JEIT251146 cstr: 32379.14.JEIT251146

1.
上海大学上海 200444
2.
中国星网网络应用有限公司重庆 400065
3.
上海交通大学上海 200240

基金项目: 重庆市自然科学基金 (CSTB2024NSCQ-LMX0024)，上海市自然科学基金 (24ZR1421800)

详细信息

作者简介:
杨孟欣：女，硕士生，研究方向为卫星通信、分布式学习

张青婷：女，工程师，研究方向为卫星通信、地面5G通信

曾令昕：男，高级工程师，研究方向为卫星通信、信号处理

顾忆宵：男，讲师，研究方向为卫星通信、边缘计算、人工智能等

曾丹：女，教授，研究方向为计算机视觉与模式识别

夏斌：男，教授，研究方向为卫星通信，通感一体网络

通讯作者:
顾忆宵　yxgu@shu.edu.cn

中图分类号: TN927.2
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出版历程
- 收稿日期: 2025-11-01
- 修回日期: 2026-01-05
- 录用日期: 2026-01-06
- 网络出版日期: 2026-01-10
- 刊出日期: 2025-12-10

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

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)

摘要

摘要: 为降低低轨(LEO)卫星系统的高动态性导致的终端频繁链路测量，现有松弛测量方案主要采用基于静态阈值或常规的时空预测模型的方法。然而，卫星的高动态性导致不同历史时刻的测量数据和不同测量指标的重要性随之动态演化，而传统方法难以捕获这种时空重要性的动态变化。为此，针对上述挑战，该文提出一种面向低轨卫星的时空特征融合长短期记忆递归神经网络(LSTM)松弛测量方法。首先，建立了LEO卫星通信系统模型，以获取所需要的历史测量数据。然后，构建了集成双重注意力机制的LSTM预测模型，实现对关键历史时刻与动态重要特征的精准聚焦，从而完成对测量频点集合与测量周期的精准预测。最后，在选择的测量频点集合和松弛周期下，执行自适应的链路测量。仿真结果表明，相较于基线方法，该文所提松弛测量方案在确保链路可靠性的同时，可以显著降低终端的测量频次。与基线相比，在更大的速度和松弛周期下，具有更强的自适应性。
- 低轨卫星通信 /
- 松弛测量 /
- 频点选择 /
- 测量周期松弛
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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图 1 星地直连通信系统

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图 2 链路松弛测量流程

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图 3 DA-LSTM预测模型架构

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图 4 预测模型训练准确率变化

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图 5 预测模型训练损失值变化

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图 6 测量频次与通信性能之间的权衡

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图 7 测量频次与通信性能之间的权衡

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图 8 不同终端速度下算法性能对比

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图 9 不同最大松弛周期下算法性能对比

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表 1 具体仿真参数配置

系统参数	参数设置
轨道面数、每个轨道卫星数	6,10
相位因子	1,2
倾角	55°,89°
卫星频段	n510,n511,n512
偏心率	0.001020
轨道高度	875 km, 925 km
天线增益、带宽B	16 dBi, 380 MHz

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表 2 神经网络模型参数

系统参数	参数设置
损失函数	均方误差(MSE)
优化器	Adam
激活函数	ReLU, Softmax
可学习参数量	308.5 k
学习率	0.00001

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表 3 终端测量平均性能(%)

	传统松弛测量方法	LSTM松弛测量方法	本文松弛测量方法
测量频次降低率	60.38	62.92	72.57
最佳频点RSRP相对值	97.57	97.68	99.89

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