CRC Anomaly Counter Normalization Across Variable PERp Rates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems face challenges in consistently detecting and reporting CRC anomalies across varying data rates and CRC computation periods, leading to inaccurate error detection and diagnostic issues, especially with new designs like Seamless Rate Adaptation and Dynamic Rate Repartition that alter data rates without changing framing parameters.
Innovation Solution
The implementation of a CRC anomaly counter normalization procedure that adjusts CRC anomaly reporting based on the actual CRC computation period (PERp value), ensuring consistent anomaly detection and diagnostic capabilities across all connections, regardless of data rate or CRC computation period, by normalizing CRC anomalies using the formula PERp/K, where K is a positive integer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CRC computation period (PERp) is reduced to enable larger data rate changes, then data rate adaptability is improved, but CRC anomaly detection accuracy deteriorates
Solution Approach 1:
The patent changes the parameter of CRC anomaly counting from a fixed threshold (18 anomalies) to a normalized threshold that adapts to different PERp values. The normalization formula (anomaly_count × PERp / reference_PERp) allows the system to maintain detection accuracy across varying computation periods while supporting larger data rate changes.
Solution Approach 2:
The patent introduces dynamic threshold adjustment based on the actual PERp value. Instead of using a static anomaly threshold, the system dynamically calculates the threshold based on the ratio of actual PERp to reference PERp, allowing the anomaly detection to adapt to changing computation periods while maintaining consistent detection accuracy.
2Reliability
If fixed CRC anomaly threshold is used across all data rates, then diagnostic consistency is improved, but false alarm rate increases
Solution Approach 1:
The patent changes the fixed anomaly threshold parameter to a dynamic normalized threshold. The formula (anomaly_count × PERp / reference_PERp) allows the threshold to scale appropriately with different PERp values, maintaining diagnostic consistency while reducing false alarms caused by mismatched thresholds.
3Measurement precision
If CRC computation is performed frequently to improve error detection, then error detection accuracy is improved, but system overhead increases
Solution Approach 1:
The patent introduces dynamic normalization that adjusts the anomaly threshold based on the actual PERp value. This allows the system to maintain high error detection accuracy by accounting for frequent CRC computations without requiring proportional increases in processing overhead, as the normalization is a simple mathematical adjustment.
Data Source
AI summary
The ability to accurately and efficiently calculate and report communication errors is becoming more important than ever in today's communications environment. More specifically calculating and reporting CRC anomalies in a consistent manner across a plurality of communications connections in a network is crucial to accurate error reporting. Through a normalization technique applied to a CRC computation period (e.g., the PERp value), accurate error identification and reporting for each individual connection can be achieved.