Autors: Rossi T., De Sanctis M., Cianca E., Ruggieri M., Codispoti G., Parca G., Poulkov, V. K.
Title: Q/V Band Satellite Channel LSTM-Based Prediction Using Alphasat TDP5 Experimental Data
Keywords:

Abstract: The ever-growing demand for high-throughput data needs for exploration of higher frequency bands in satellite communication systems. Q/V-band frequencies offer substantial potential due to their wider bandwidth availability compared to traditional Ka-band frequencies. As a matter of fact, some of the most important satellite operators are working on the development of High Throughput Satellite (HTS) systems exploiting Q/V-band in the feeder link but also in the user link (e.g. for VLEO connections). However, these bands are susceptible to rapid fluctuations in channel quality caused by tropospheric effects. This presents a significant challenge for maintaining reliable communication links. In this framework, channel prediction techniques have a crucial importance to effectively implement adaptive Fade Mitigation Techniques (FMT). This paper proposes a novel approach for predicting Q/V-band satellite channel conditions in terms of Signal to Noise Ratio (SNR) using a Long Short-Term Memory (LSTM) neural network. The predictive capability of a LSTM neural network empowers the communication system to proactively adjust radio interface parameters, such as modulation and coding schemes, in response to real-time variations in the channel.

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Issue

Ka and Broadband Communications Conference, 2025, United States, ISSN 25736124

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