This technology is a machine learning-based algorithm that predicts weather-induced signal degradation in wireless communication networks and proactively manages network resources to maintain quality of service.
Current 5G and next-generation wireless networks increasingly rely on millimeter-wave (mmWave) frequencies for high-bandwidth connectivity and cellular infrastructure. However, current millimeter-wave (mmWave) and wireless networks are heavily influenced by weather, specifically rain, which can severely which can degrade or completely disrupt network links. Existing network management systems react to disruptions only after they occur, leading to service interruptions and poor Quality of Service (QoS). There is no solution on the market that can predict weather-induced network degradation and proactively adapt network routing, power, and modulation before disruptions occur.
This technology enables the predictive management of wireless networks by leveraging a network's own commercial microwave links (CMLs) to detect and preempt weather-related signal disturbances in real time. A deep learning model analyzes signal attenuation patterns across the network to forecast conditions at each link. Based on these predictions, the system proactively manages network resources through dynamic rerouting, adaptive transmission power control (ATPC), adaptive modulation and coding (AMC), and channel switching, before weather-related network degradation occurs. This approach is self-contained and does not require external weather data sources such as radar.
The technology has been validated using real-world data from a smart-city mmWave network deployment.
Patent Issued (US 12,556,937)
IR CU19208
Licensing Contact: Greg Maskel
