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September 10, 2025ElectronicsOpen Access

Joint Traffic Prediction and Handover Design for LEO Satellite Networks with LSTM and Attention-Enhanced Rainbow DQN

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Authors

DFDazhao FanPLA Information Engineering University ZSZhou Shilei JLJihao LuoBeijing Institute of Technology

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Implication

Simulation results show improved handover efficiency and service quality in LEO satellite networks, suggesting advances in non-terrestrial network integration.

Key Points

The approach significantly improves handover efficiency and service quality in satellite communication systems.
Simulation results indicate enhanced performance compared to existing methods, improving load balancing across satellites.
The proposed LSTM-based traffic prediction helps streamline the handover decision-making process effectively.
This joint design framework combines several crucial factors like satellite switching frequency and communication quality.

Cite This Study

Fan et al. (2025) studied this question.

synapsesocial.com/papers/68c19fa854b1d3bfb60db6c5 https://doi.org/10.3390/electronics14153040

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Implication

Simulation results show improved handover efficiency and service quality in LEO satellite networks, suggesting advances in non-terrestrial network integration.

Joint Traffic Prediction and Handover Design for LEO Satellite Networks with LSTM and Attention-Enhanced Rainbow DQN

Discussion

Member takes

Implication

Key Points

Cite This Study

Joint Traffic Prediction and Handover Design for LEO Satellite Networks with LSTM and Attention-Enhanced Rainbow DQN

Discussion

Member takes

Implication

Key Points

Cite This Study

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