[1]TONG Yao,LIU Bo,QI Xiaogang.A traffic prediction method for a low earth orbit satellite network based on parameter-optimized VMD and improved BiLSTM[J].CAAI Transactions on Intelligent Systems,2026,21(3):627-638.[doi:10.11992/tis.202508026]
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A traffic prediction method for a low earth orbit satellite network based on parameter-optimized VMD and improved BiLSTM

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 627-638 Column: 学术论文—机器学习 Public date: 2026-05-05
Title:
A traffic prediction method for a low earth orbit satellite network based on parameter-optimized VMD and improved BiLSTM
Author(s):
TONG Yao1; 2;  LIU Bo3;  QI Xiaogang1; 2
1. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China;
2. Xi’an Key Laboratory of Information Network Optimization and Mathematical Methods, Xi’an 710071, China;
3. College of Information and Navigation, Air Force Engineering University, Xi’an 710003
Keywords:
low earth orbit satellite network; service traffic; traffic prediction; machine learning; variational mode decomposition; improved sparrow search algorithm; self-attention mechanism; bidirectional long short-term memory network
CLC:
TP181
DOI:
10.11992/tis.202508026
Abstract:
Traffic prediction in low earth orbit satellite networks is critical for mitigating congestion and optimizing resource allocation. To further enhance prediction accuracy, a prediction method based on parameter-optimized variational mode decomposition (VMD) and an improved bidirectional long short-term memory (BiLSTM) network is proposed. The method comprises two core models: a parameter-optimized VDM model based on an improved sparrow search algorithm (VPI) and a traffic prediction model based on an improved BiLSTM network (TPIB). In the VPI model, an improved sparrow search algorithm incorporating Tent chaotic mapping and Gaussian mutation is adopted to optimize key parameters for VMD, thereby improving decomposition performance. In the TPIB model, a self-attention mechanism is introduced to enhance BiLSTM, enabling dynamic feature weight allocation for decomposed data and bidirectional temporal modeling, thereby improving prediction accuracy. Experimental results show that, compared with the baseline LSTM model, the proposed method reduces the mean absolute error (MAE) by 42.64% and 81.59% on the two real-world datasets, respectively.
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