Cyclostationary Signal Analysis for Passive Intermodulation Detection
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Solution Overview
Problem
Intermodulation distortion, particularly passive intermodulation distortion (PIM), is difficult to identify in communication networks, causing signal interference and reducing network throughput, and existing methods are costly and time-consuming, requiring network downtime for investigation and testing.
Innovation Solution
The use of cyclostationarity detection techniques, including cyclic autocorrelation and spectral correlation analysis, to identify intermodulation distortion by classifying signals based on their cyclostationary properties, allowing for the detection of PIM in RF signals without disrupting network operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active investigation and testing are performed to identify intermodulation distortion, then measurement precision is improved, but loss of time and productivity deteriorate due to network downtime
Solution Approach 1:
The system performs preliminary detection by continuously monitoring RF signals for cyclostationary characteristics that indicate PIM distortion. This allows early identification of distortion sources before they cause major interference, enabling proactive maintenance without network downtime.
Solution Approach 2:
The patent replaces physical active investigation methods (disconnecting components, injecting test signals) with signal processing-based detection. By analyzing the cyclostationary properties of existing RF signals, the system identifies PIM distortion without mechanical intervention or network disruption.
2Measurement precision
If sophisticated diagnostics equipment is used for active testing, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system uses the existing communication signals themselves as the detection source. By analyzing the cyclostationary characteristics of normal RF traffic, the system performs self-diagnosis without requiring external test equipment or injected test signals, simplifying the diagnostic infrastructure.
Solution Approach 2:
The patent transforms the detection approach by changing from analyzing signal amplitude and frequency content to analyzing cyclostationary parameters (spectral correlation, cyclic autocorrelation). This parameter transformation enables PIM detection using standard signal processing equipment rather than specialized diagnostics tools.
3Productivity
If network operation is maintained during detection, then productivity is improved, but difficulty of detecting and measuring increases due to operational interference
Solution Approach 1:
The system dynamically adapts to changing network conditions by continuously analyzing RF signals for cyclostationary characteristics. The detection method remains effective regardless of varying traffic patterns, signal levels, or network configurations, enabling reliable PIM identification in live operational environments.
Solution Approach 2:
The patent uses cyclostationary analysis as an intermediary method that bridges the gap between operational network conditions and PIM distortion detection. By detecting the unique cyclostationary signature of PIM-distorted signals, the system can identify distortion sources amidst normal network traffic without requiring network shutdown or special test modes.
Data Source
AI summary
The present disclosure describes systems and methods for determining intermodulation distortion in a system such as, but not limited to, a communication system. In an embodiment, intermodulation distortion is determined by injecting a signal into a frequency band which interacts with a second signal, searching a second frequency band for a product signal formed from the first and second signals, applying a cyclostationarity detection technique to the product signal and identifying the product signal as an intermodulation distortion signal.