Remote Passive Intermodulation Detection in Radio Base Stations
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Solution Overview
Problem
Conventional methods for detecting passive intermodulation (PIM) in radio base stations are invasive, costly, and time-intensive, requiring on-site testing and disrupting network operations, while also being ineffective in identifying PIM issues caused by environmental factors and loose mechanical connections.
Innovation Solution
A system and method using Key Performance Indicators (KPIs) and data processing to remotely identify PIM problems by determining stability and correlation factors, allowing for non-intrusive detection and automated parameter changes to alleviate PIM issues without on-site investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional on-site testing methods are used to detect PIM, then detection accuracy is improved, but operational disruption and time consumption increase
Solution Approach 1:
The patent replaces physical on-site testing mechanisms with remote electronic monitoring. The system uses automated algorithms to analyze KPI data and generate PIM probability scores remotely, eliminating the need for physical presence at base stations while maintaining detection effectiveness.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that processes KPI data between the network and human operators. This intermediary system generates PIM probability assessments and troubleshooting recommendations, reducing direct human intervention and on-site testing requirements.
2Measurement precision
If conventional on-site testing methods are used to detect PIM, then detection accuracy is improved, but operational disruption increases
Solution Approach 1:
The patent replaces disruptive physical testing with non-intrusive remote electronic analysis. The system continuously monitors KPI data without requiring base station interruptions, maintaining network operation continuity while detecting PIM issues.
Solution Approach 2:
The patent enables continuous PIM detection through ongoing analysis of KPI data streams. The automated system operates continuously without interruption to network services, providing ongoing monitoring rather than periodic disruptive testing.
3Ease of operation
If remote detection methods are used, then operational disruption is reduced, but detection capability for environmental factors decreases
Solution Approach 1:
The patent incorporates feedback mechanisms where the system analyzes the impact of environmental factors on KPI data. By monitoring correlations between environmental conditions and performance metrics, the system can infer PIM issues caused by environmental factors like temperature changes or physical stress on connections.
Solution Approach 2:
The patent uses an intermediary automated analysis system that processes multiple KPI parameters to detect environmental PIM factors remotely. The system correlates various performance metrics to identify patterns indicating environmental stress on PIM sources without requiring physical inspection.
4Measurement precision
If comprehensive KPI analysis is performed, then PIM detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex analysis into distinct functional modules: data collection, KPI processing, PIM probability scoring, and recommendation generation. This modular approach manages complexity by dividing the comprehensive analysis into manageable, independent components.
Solution Approach 2:
The patent transforms complex multi-parameter KPI data into simplified PIM probability scores and actionable recommendations. By changing the parameter representation from raw KPI values to probability scores, the system maintains detection accuracy while reducing processing complexity for decision-making.
Data Source
AI summary
Systems and methods are described for configuring a base station node. A set of base station parameters are received by a remote control unit. The remote control unit receives and installs dynamic parameter values for base station setup selected by a server application based on the received base station parameters. The remote control unit also receives dynamic parameter values for network integration generated by the server application via the network connection, the dynamic parameter values for network integration being generated in response to a user selection. The remote control unit may then configure the base station node using the dynamic parameter values for network integration. When configuration is complete, the remote control unit may transmit an indication to the server application, and finalizes integration of the base station node in response to a request from the server application.