Е-Публикации
Технически университет - София

Abstract: The increasing demand for broadband satellite services is driving the adoption of higher frequency bands, with Q/V-band (40–50 GHz) offering substantially greater bandwidth than Ka-band and enabling next-generation High Throughput Satellite (HTS) systems. However, the strong susceptibility of Q/V-band links to atmospheric impairments, particularly rain-induced fading, poses significant reliability challenges. Accurate channel prediction is therefore essential for adaptive Fade Mitigation Techniques (FMT), such as Smart Gateway Diversity (SGD), which exploits geographically distributed gateways and real-time control to counteract weather-induced signal degradation. This paper investigates AI-based channel prediction methods for SGD, focusing on the evaluation of performance using experimental data collected during the Alphasat TDP#5 Q/V-band experiments.

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