PIM Noise Detection via Anomaly Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting passive intermodulation (PIM) noise events in cellular networks are ineffective due to their reliance on expensive hardware solutions and simulations that fail to capture real-life network and environmental variables, particularly in transient PIM cases where noise, time, amplitude, and frequency are influenced by location-specific factors.
Innovation Solution
A method and apparatus that utilize anomaly detection models to compare uplink noise data from a cell with neighboring cells, determining if a PIM noise event has occurred by identifying localized anomalies and assigning a confidence level based on system characterization, allowing for the identification of PIM noise events even in the presence of missing data and other noise sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware-based solutions (DSP, advanced antennas) are used for PIM detection, then detection accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The patent replaces hardware-based detection mechanisms (DSP processors, advanced antenna systems) with a software-based machine learning model that analyzes existing uplink quality measurements. The ML model processes KPI data including uplink SINR, uplink noise, and interference measurements to detect PIM events, eliminating the need for additional specialized hardware while maintaining detection accuracy.
Solution Approach 2:
The patent creates a virtual representation of PIM detection capability through a machine learning model that simulates the functionality of hardware-based detectors. The model is trained on historical data to replicate the detection behavior of expensive hardware solutions, providing a cost-effective copy of the detection capability without physical hardware implementation.
2Ease of operation
If simulations are used for PIM detection, then detection capability is provided, but reliability decreases due to failure to capture real-life network variables
Solution Approach 1:
The patent performs preliminary training of the machine learning model using real-network historical data before deployment. The model is trained on actual KPI measurements from the network environment, capturing real-life variations in noise, interference, and signal characteristics. This preliminary action with real data ensures the model adapts to specific network conditions and maintains reliability when deployed for actual PIM detection.
Solution Approach 2:
The patent adjusts the model's behavior based on network-specific parameters and conditions. The ML model processes variable parameters including uplink SINR thresholds, noise floor levels, and interference patterns that are specific to each network environment. By adapting to these parameter variations, the model maintains reliability across different real-network conditions rather than relying on fixed simulation parameters.
3Ease of manufacture
If aggregate KPI reporting intervals are used for PIM detection, then implementation simplicity is improved, but detection effectiveness worsens due to transient PIM events cycling between good and bad
Solution Approach 1:
The patent implements periodic analysis of KPI data at multiple time scales. The ML model evaluates uplink quality measurements across different time intervals, comparing short-term variations against longer-term trends. This periodic multi-scale analysis enables detection of transient PIM events that cycle quickly, as the model can identify abrupt changes in uplink SINR or noise patterns that occur within specific time windows rather than relying on single aggregate intervals.
Solution Approach 2:
The patent introduces dynamic thresholding and adaptive analysis windows that adjust to the characteristics of PIM events. The ML model dynamically modifies its detection parameters based on the observed behavior of uplink measurements, allowing it to capture transient events with varying durations and intensities. This dynamic approach enables the system to detect quick cycling PIM events while maintaining implementation simplicity by using existing KPI data structures.
4Device complexity
If location-specific variables are not considered, then analysis simplicity is improved, but measurement precision decreases due to influence of noise, time, amplitude, and frequency variations
Solution Approach 1:
The patent applies local quality analysis by comparing uplink measurements from the affected cell against measurements from neighboring cells or reference cells. The ML model identifies local anomalies by detecting deviations from the spatial pattern expected in the network. This local comparison approach accounts for location-specific variables such as environmental noise sources, antenna characteristics, and geographic factors that affect individual cells differently, improving detection precision without requiring explicit modeling of each location-specific variable.
Data Source
AI summary
A method receives data from entities indicative of whether or not an anomalous event is occurring at the entities over a first time period. The method characterizes the system, based on the data and a system characterization model, as one of: a system in which only the first entity is experiencing an anomalous event at any one time; a system in which two or more of the plurality of entities are experiencing anomalous events occurring at any one time; and a system for which during at least one second time period within the first time period data is missing for one or more of the plurality of entities. The method uses one or more anomaly detection models to compare the data received from the first entity to the data received from other entities in the plurality of entities, and determine that a local anomalous event has occurred at an entity.