Wet-dry classification using LSTM and commercial microwave links

The task of rain detection, or wet-dry classification using measurements from commercial microwave links (CMLs) is a subject that been studied in depth. However, these studies are based on direct measurement of the signal level, which is known to be attenuated by rain. In this paper we present, for the first time an empirical study on rain classification using records of transmissions errors in the CMLs. Based on a dataset of measurements taken from operational cellular backhaul networks and meteorological measurements, and using long short-term memory (LSTM) units with a multi-variable time series, we demonstrate that measurements of microwave link error are related to rain and can even be used for rain detection (wet-dry classification). We evaluate the performance of LSTM on CMLs empirically, and analyze the results by comparison with rain detection based on attenuation measurements in the same links.