Cell Performance Degradation Analysis Using AI Data Compression

The patent combines multiple individual KPI detection results into a unified performance degradation assessment. By integrating data from multiple KPI items (throughput, latency, error rates, etc.) and applying machine learning models, the system merges separate measurements into a comprehensive evaluation that accurately identifies overall cell performance degradation while reducing false positives.

The patent introduces machine learning models and data fusion mechanisms as intermediaries between individual KPI measurements and final performance degradation determination. These intermediaries process, correlate, and analyze multiple KPI data streams to produce an accurate assessment of overall cell performance, bridging the gap between individual metrics and holistic evaluation.

The patent implements preliminary action by pre-training machine learning models with historical KPI data and establishing baseline performance profiles before actual performance monitoring begins. This pre-processing enables the system to quickly and reliably assess current cell performance by comparing real-time data against pre-established patterns, reducing analysis time while maintaining high reliability.

The patent incorporates feedback mechanisms where performance assessment results are continuously fed back into the machine learning models to refine and update performance profiles. This closed-loop approach improves assessment reliability over time as the system learns from accumulated data, while the automated nature of the feedback process minimizes additional time requirements.