Network Noise Localization Using SNR and CER Scoring
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Solution Overview
Problem
Service providers face challenges in accurately identifying and localizing noise sources in networks, such as cable networks, which cause service degradation, due to unreliable and inaccurate impairment detection and prioritization techniques.
Innovation Solution
A method and system for noise analysis and localization in networks that involves determining upstream SNR and CER values, identifying noisy channels and suspect devices using specific criteria, and generating noise scores based on network parameter inconsistencies and derivatives to pinpoint noise sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional impairment detection techniques are used, then network monitoring is performed, but noise source identification accuracy deteriorates
Solution Approach 1:
The patent segments the network into multiple analysis units (HFC networks, fiber networks, wireless networks) and further segments noise analysis into multiple parameters (SNR, CER, noise floors, signal levels). This segmentation allows precise identification of noise sources by analyzing specific segments rather than treating the entire network as a single unit, thereby improving measurement precision while maintaining reliability.
Solution Approach 2:
The patent introduces multiple dimensions for noise analysis including upstream/downstream directions, multiple frequency channels, and temporal variations. By analyzing noise across these additional dimensions rather than a single parameter, the system achieves higher identification accuracy while maintaining detection reliability through multi-dimensional verification.
2Measurement precision
If comprehensive network monitoring is performed, then all network parameters are captured, but analysis time increases
Solution Approach 1:
The patent applies partial action by focusing monitoring resources on specific suspicious segments identified through initial screening, rather than continuously analyzing the entire network at maximum depth. This allows accurate noise detection in targeted areas while reducing overall analysis time by avoiding exhaustive search of all network segments.
Solution Approach 2:
The system performs preliminary noise threshold assessments and basic parameter monitoring before conducting detailed analysis. By pre-identifying suspicious segments using simplified criteria, the system prepares targeted analysis lists in advance, thereby maintaining high detection accuracy while significantly reducing the time required for comprehensive noise source localization.
3Measurement precision
If multiple noise parameters are analyzed, then noise characterization improves, but computational complexity increases
Solution Approach 1:
The patent segments noise parameter analysis into hierarchical levels: basic parameters (SNR, CER) for all segments, intermediate parameters (noise floors, signal levels) for suspicious segments, and advanced parameters (interference patterns, modulation errors) for confirmed noise sources. This segmented approach achieves comprehensive noise characterization while managing computational complexity through progressive analysis.
Solution Approach 2:
The system applies different analysis depths to different network segments based on their suspiciousness level. High-complexity multi-parameter analysis is applied only to segments with high noise probability, while low-complexity monitoring is applied to normal segments. This local quality approach maintains high characterization accuracy for problematic areas while reducing overall computational complexity.
Data Source
AI summary
Various techniques include detecting noise in a network, identifying the type of noise in the network, localizing noise in the network, determining noise scores for network devices, and/or determining likelihoods that particular devices are causing noise and/or are in proximity of a point of entry of noise into the network.