This technology is a weather-sensitive, machine learning-based algorithm that predicts rain attenuation in microwave and millimeter-wave networks from existing data on weather patterns.
With the advent of 5G broadband communication, the field of mobile communications is expected to grow 1000-fold. To accommodate faster speeds and larger capacity, higher radio frequencies, such as the millimeter-wave (mmWave) band (30-30GHz), must be used. However, mmWave technology is extremely susceptible to moisture in the atmosphere, resulting in rain-induced attenuation and network blockage. There are currently no preventative methods enabling adjustments to data communication links during storms and rainfall.
This technology is a weather-sensitive prediction algorithm for rain attenuation in microwave and mmWave networks. The model uses already-established commercial microwave links to gather data on rainfall monitoring and weather patterns. Machine learning and the cross-layered algorithms then integrate this data to predict spatio-temporal rain attenuation patterns. This technology allows providers to predict developing weather patterns that may potentially interfere with network performance, allowing providers to preemptively adjust and reroute their systems. Therefore, this technology can prevent network disturbances, significantly improving the reliability of a provider’s service.
IR CU19208
Licensing Contact: Greg Maskel