ITU-ML5G-PS-012: Radio Link Failure Prediction
An ML-based RLF Prediction Algorithm to predict failures in a radio-link wireless communication channel. Cloud, rain, snow, and other weather-related phenomena affect the performance of radio links. This is especially applicable to backhaul links operating at GHz frequencies. A generic regional weather forecast data is available which lists expected conditions and coarse temperatures along with actual –precise– realizations.
Adding to the complexity are the spatial nature of the data (regions of weather data and RLF needs to be aligned) s well as the time sync needed to correlate various occurrences. Over a period of time, we have compiled and anonymised region-wise data which corresponds to weather forecasts, radio link performances and radio link failures derived from our networks.
The objective of this problem statement is to predict the occurrence of radio link failures in: in the next day and in the following 5 days A Machine Learning model is built for the same.