Tropospheric Ducting Prediction for Proactive Cell Interference Mitigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current tropospheric ducting mitigation techniques are reactive and do not effectively prevent performance degradation caused by tropospheric ducting events until after the interference occurs, leading to intermittent network issues.
Innovation Solution
A system utilizing a machine learning model trained with weather forecast information and cell site data to predict tropospheric ducting events, allowing for proactive application of mitigation techniques and optimization of network configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive mitigation techniques are used to address tropospheric ducting interference, then network performance can be restored after interference occurs, but network performance degradation occurs during the interference period before mitigation is applied
Solution Approach 1:
The system performs preliminary actions by training a machine learning model with historical weather forecast data and interference labels to predict tropospheric ducting events before they occur. The trained model proactively identifies upcoming interference events based on weather patterns, allowing the network to apply mitigation techniques in advance rather than reactively after interference begins, thus eliminating performance degradation during interference periods
2Measurement precision
If machine learning models are trained with comprehensive weather forecast information and cell site data, then prediction accuracy of tropospheric ducting events is improved, but system complexity increases
Solution Approach 1:
The system segments the prediction task into distinct components: a training phase where the machine learning model learns from historical weather forecast data and interference labels, and an inference phase where the trained model predicts future events. This segmentation allows comprehensive data processing during training while maintaining a relatively simple deployed model, balancing prediction accuracy with system complexity
Solution Approach 2:
The system performs the complex data processing and model training in advance during a preliminary training phase. Once trained, the model can make predictions with high accuracy without requiring complex real-time processing, thus improving prediction accuracy while keeping the operational system complexity manageable
Data Source
AI summary
Disclosed is a method comprising collecting input data comprising at least weather forecast information for an area in which one or more cells are located; providing the input data to a prediction algorithm, wherein the prediction algorithm comprises: a machine learning model trained to predict tropospheric ducting events impacting the one or more cells, and a cell site database indicating a location and one or more configuration parameters of the one or more cells; and receiving, from the prediction algorithm, output data indicating one or more predicted tropospheric ducting events expected to impact the one or more cells based on the input data.