Tropospheric Ducting Prediction for Proactive Cell Interference Mitigation
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Solution Overview
Problem
Current tropospheric ducting mitigation techniques are reactive and do not effectively prevent performance degradation caused by tropospheric ducting events in wireless communication networks, leading to intermittent interference and reduced network access.
Innovation Solution
Implement a machine learning model trained on 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, then device complexity is reduced, but network reliability deteriorates due to intermittent interference and performance degradation
Solution Approach 1:
The system performs preliminary actions by collecting weather forecast data and training a machine learning model to predict tropospheric ducting events before they occur. The model processes atmospheric conditions, temperature profiles, and humidity data to generate predictions, enabling the network to proactively apply mitigation techniques such as power reduction or beamforming adjustments before interference degrades performance, thus resolving the contradiction between reliability improvement and system complexity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual network performance metrics and comparing them with predicted ducting events. The machine learning model is retrained using confusion matrix evaluation and performance feedback to improve prediction accuracy over time. This closed-loop feedback system enhances network reliability through adaptive mitigation while managing complexity through automated learning rather than manual configuration
2Reliability
If machine learning prediction systems are implemented, then network reliability improves through proactive mitigation, but device complexity increases due to additional processing and data collection requirements
Solution Approach 1:
The machine learning model serves multiple functions: it predicts tropospheric ducting events, evaluates prediction accuracy through confusion matrices, and guides mitigation strategy selection. The same system processes various input data types including weather forecasts, atmospheric profiles, and real-time network performance metrics. This multi-functionality consolidates multiple complex subsystems into a single unified prediction platform, improving reliability while containing overall system complexity
Solution Approach 2:
The system employs self-service mechanisms through automated machine learning model training and evaluation. The model automatically retrains using collected performance data and confusion matrix results without requiring manual intervention. This self-improving capability reduces the operational complexity of maintaining prediction accuracy while enhancing reliability through continuous adaptation to changing atmospheric and network conditions
3Reliability
If proactive mitigation is applied, then network performance is maintained, but loss of time occurs due to data collection and processing delays
Solution Approach 1:
The system performs preliminary data collection and model training in advance, building a ready-to-use prediction framework before ducting events occur. Weather forecast data and atmospheric profiles are pre-processed and stored, enabling rapid predictions when needed. This preliminary preparation reduces real-time processing delays while maintaining the ability to proactively mitigate performance degradation
Solution Approach 2:
The system implements periodic action by scheduling regular model training cycles and prediction intervals. Instead of continuous real-time processing, the model is trained periodically on accumulated data and makes predictions at scheduled intervals based on forecast updates. This periodic operation maintains network performance reliability while significantly reducing computational time and resource consumption compared to continuous processing
Data Source
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Figure 5A~5B
AI summary
Disclosed is a method comprising collecting input data (521) comprising at least weather forecast information for an area in which one or more cells (104B, 104C, 104D) are located; providing the input data (521) to a prediction algorithm (520), wherein the prediction algorithm (520) comprises: a machine learning model (500) trained to predict tropospheric ducting events impacting the one or more cells (104B, 104C, 104D), and a cell site database (511) indicating a location and one or more configuration parameters of the one or more cells (104B, 104C, 104D); and receiving, from the prediction algorithm (520), output data (522) indicating one or more predicted tropospheric ducting events expected to impact the one or more cells (104B, 104C, 104D) based on the input data (521).