AI-Based Cellular Base Station Performance Scoring for Dynamic Conditions

The patent combines multiple KPIs (signal strength, data throughput, call drop rate, latency) into a single composite performance score. This merging approach maintains comprehensive monitoring capability while simplifying the identification of underperforming base stations by providing a unified metric that operators can easily interpret and act upon.

The patent introduces machine learning models as an intermediary layer between raw KPI data and performance assessment. These models process numerous KPIs and transform them into meaningful performance scores and predictions, reducing the complexity of direct KPI analysis while maintaining measurement precision through sophisticated pattern recognition.

The patent transitions from static performance thresholds to dynamic, context-aware assessment. Machine learning models learn optimal performance expectations based on geographical location, climatic conditions, and traffic patterns, allowing the system to adapt automatically to varying conditions. This enables the system to recognize that different base stations have different baseline performance characteristics based on their environments.

The patent changes the parameters used for performance assessment from fixed static thresholds to dynamic parameters that vary based on environmental and operational conditions. The machine learning models adjust performance expectations based on multiple input parameters including location, weather, and traffic patterns, enabling adaptive assessment without requiring manual threshold configuration for each scenario.

The patent replaces manual KPI analysis with automated machine learning-based assessment. Instead of operators manually monitoring and analyzing numerous KPIs across many base stations, the system uses machine learning models to automatically process KPI data, generate performance scores, and identify underperforming stations. This substitution dramatically improves network management efficiency while maintaining comprehensive monitoring coverage.

The patent enables the system to self-assess base station performance without requiring continuous human intervention. The machine learning models automatically process KPI data, compare performance against learned expectations, and generate actionable insights. This self-service capability allows the system to maintain high measurement precision while significantly reducing the time and resources required for performance management.

The patent implements predictive analytics that perform preliminary assessment of base station performance trends. Machine learning models analyze historical KPI data to predict future performance issues before they occur, enabling proactive identification of underperforming stations. This preliminary action reduces the time required to identify problems by detecting trends early in the performance degradation process.

The patent implements continuous feedback loops where machine learning models learn from actual performance data and refine their predictions. The system compares predicted performance with actual outcomes, uses this feedback to improve future predictions, and continuously optimizes performance assessment. This feedback mechanism enables quick and accurate identification of underperforming stations while managing system complexity through iterative learning.