Time series based approach for detecting Passive Intermodulation occurrences in cellular network

Passive Intermodulation (PIM) identification is a major component in troubleshooting modern wireless communication systems, such as LTE and 5G. The introduction of carrier aggregation has increased the number of PIM occurrences. Current state-of-the-art approaches include manual rule-based and hardware-based debugging. These approaches can detect the occurrence of PIM, long after the event occurrence and result in incurring incidental costs. We propose an ensemble of time series based machine learning and signal processing approaches, that can automatically identify PIM in real-time, by analyzing Key Parameter Indicators (KPI) of the primary cell and its nearest neighbors. We validate our results for various environmental conditions in data available from LTE and 5G consumer networks.