Spatio-temporal correlation-based incomplete time-series traffic prediction for LEO satellite networks

Liang PENG; Jie YAN; Peng WEI; Xiaoxiang WANG

doi:10.1631/FITEE.2300873

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Spatio-temporal correlation-based incomplete time-series traffic prediction for LEO satellite networks

Regular Papers | Updated：2025-06-09

- Spatio-temporal correlation-based incomplete time-series traffic prediction for LEO satellite networks
  Enhanced Publication
- 低轨卫星网络中基于时空相关性的不完全时间序列流量预测
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 5, Pages: 788-804(2025)
- Affiliations：
  
  Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Author bio：
  
  E-mail: cpwang@bupt.edu.cn
- Funds：
- DOI：10.1631/FITEE.2300873
  CLC： TN014
- Received：26 December 2023，
  
  Revised：2024-04-19，
  
  Published：2025-05
- Accepted：
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Liang PENG, Jie YAN, Peng WEI, et al. Spatio-temporal correlation-based incomplete time-series traffic prediction for LEO satellite networks[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(5): 788-804.
DOI：

Liang PENG, Jie YAN, Peng WEI, et al. Spatio-temporal correlation-based incomplete time-series traffic prediction for LEO satellite networks[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(5): 788-804. DOI： 10.1631/FITEE.2300873.

摘要

准确的短期流量预测对于提高低轨道卫星网络的数据传输效率至关重要。但是，在复杂空间环境中，收集器失败、传输错误和内存失败可能导致流量值丢失。不完全的流量时间序列阻碍了数据的有效利用，从而显著降低流量预测精度。为解决这一问题，提出一种基于时空相关性的不完全时间序列流量预测模型，该模型分为两个阶段：通过缺失数据推断方法重构不完全时间序列和基于重构的时间序列进行流量预测。在第一阶段，提出一种基于改进的去噪自编码器的缺失数据推断模型。具体来说，将去噪自编码器与格氏角求和场相结合，建立不同时间间隔之间的时间相关性，并从时间序列中提取结构模式。利用低轨道卫星网络流量独特的时空相关性，重点改进去噪自编码器的缺失值初始化方法。在第二阶段，结合低轨道卫星网络的时空相关流量，提出一种基于多通道注意机制卷积神经网络的流量预测模型。最后，为实现这些模型的理想结构，使用多元宇宙优化算法以选择模型参数的最优组合。实验表明，在不同数据缺失率下，所提模型在流量预测精度方面优于基线模型，证明了该模型的有效性。

Abstract

Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit (LEO) satellite networks. However

traffic values may be missing due to collector failures

transmission errors

and memory failures in complex space environments. Incomplete traffic time series prevent the efficient utilization of data

which can significantly reduce the traffic prediction accuracy. To overcome this problem

we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction (ITP-ST) model

which consists of two phases: reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series. In the first phase

we propose a novel missing data imputation model based on the improved denoising autoencoder (IDAE-MDI). Specifically

we combine DAE with the Gramian angular summation field (GASF) to establish the temporal correlation between different time intervals and extract the structural patterns from the time series. Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic

we focus on improving the missing data initialization method for DAE. In the second phase

we propose a traffic prediction model based on a multi-channel attention convolutional neural network (TP-CACNN) by combining the spatio-temporally correlated traffic of the LEO satellite network. Finally

to achieve the ideal structure of these models

we use the multi-verse optimizer (MVO) algorithm to select the optimal combination of model parameters. Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates

which demonstrates the effectiveness of our proposed model.

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references

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