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This work aims to enhance gateway management in Q/V-band satellite networks through predictive channel estimation.

Predictive Channel Estimation and Optimal Switching Control via LSTM in Q/V‐Band Satellite Networks With Smart Gateway Diversity

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Randomized trial demonstrates improved channel prediction in Q/V-band networks, suggesting better signal reliability.

This work aims to enhance gateway management in Q/V-band satellite networks through predictive channel estimation.
Developed a channel state prediction model using long short-term memory (LSTM) neural networks.
Utilized experimental data from the Alphasat TDP#5 Q/V-band campaign for training and validation.
Compared LSTM results with persistence-based and linear prediction models.
The LSTM model significantly reduced forecasting errors compared to persistence-based methods, improving decision-making for gateway switching.
Achieved timely handovers with enhanced reliability in next-generation EHF satellite networks.

Rossi et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial demonstrates improved channel prediction in Q/V-band networks, suggesting better signal reliability.

This work aims to enhance gateway management in Q/V-band satellite networks through predictive channel estimation.
Developed a channel state prediction model using long short-term memory (LSTM) neural networks.
Utilized experimental data from the Alphasat TDP#5 Q/V-band campaign for training and validation.
Compared LSTM results with persistence-based and linear prediction models.
The LSTM model significantly reduced forecasting errors compared to persistence-based methods, improving decision-making for gateway switching.
Achieved timely handovers with enhanced reliability in next-generation EHF satellite networks.

Rossi et al. (2026) studied this question.

View Full Paper

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Bookmark