  Line of sight satellite systems, unmanned aerial vehicles, high-altitude
platforms, and microwave links that operate on frequency bands such as Ka-band
or higher are extremely susceptible to rain. Thus, rain fade forecasting for
these systems is critical because it allows the system to switch between ground
gateways proactively before a rain fade event to maintain seamless service.
Although empirical, statistical, and fade slope models can predict rain fade to
some extent, they typically require statistical measurements of rain
characteristics in a given area and cannot be generalized to a large scale
system. Furthermore, such models typically predict near-future rain fade events
but are incapable of forecasting far into the future, making proactive resource
management more difficult. In this paper, a deep learning (DL)-based
architecture is proposed that forecasts future rain fade using satellite and
radar imagery data as well as link power measurements. Furthermore, the data
preprocessing and architectural design have been thoroughly explained and
multiple experiments have been conducted. Experiments show that the proposed DL
architecture outperforms current state-of-the-art machine learning-based
algorithms in rain fade forecasting in the near and long term. Moreover, the
results indicate that radar data with weather condition information is more
effective for short-term prediction, while satellite data with cloud movement
information is more effective for long-term predictions.
