NPML Filter Coefficient Calibration Under Variable Noise
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Solution Overview
Problem
Existing data processing systems face inaccuracies in noise predictive filtering, leading to ineffective data transfer due to unmanaged noise scenarios.
Innovation Solution
The implementation of a data processing system with a forced variance filter coefficient adaptation circuit that adjusts and normalizes noise predictive filter coefficients to maintain a defined variance, enhancing the operation of noise predictive filter circuits and improving data detection and decoding algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing noise predictive filtering is used, then data transfer is performed, but inaccurate results occur in various noise scenarios
Solution Approach 1:
The patent implements dynamic adaptation of filter coefficients through multiple filter circuits (first noise predictive filter, second noise predictive filter) that can switch between different coefficient sets based on noise conditions. The forced variance filter coefficient adaptation circuit continuously adjusts coefficients to maintain optimal performance across varying noise scenarios, transforming the static filtering system into a dynamic one that adapts to changing conditions.
Solution Approach 2:
The patent changes the parameters of the noise predictive filter by introducing multiple coefficient sets (first coefficient set, second coefficient set) with different variance characteristics. The forced variance filter coefficient adaptation circuit modifies these parameters dynamically, constraining variance to predefined values to optimize filtering accuracy under different noise conditions, directly addressing the inaccuracy problem in existing systems.
2Adaptability or versatility
If filter coefficients are adapted without variance constraint, then filtering flexibility increases, but consistency and reliability decrease
Solution Approach 1:
The forced variance filter coefficient adaptation circuit implements a feedback mechanism that continuously monitors the variance of filter coefficients and adjusts them to maintain predefined variance levels. This feedback loop ensures that while coefficients adapt to different noise conditions, their variance remains constrained and consistent, resolving the contradiction between adaptability and stability.
Solution Approach 2:
The system dynamically switches between different coefficient sets (first and second noise predictive filters) depending on noise conditions, providing adaptability. Simultaneously, the forced variance constraint maintains stability by ensuring that whichever coefficient set is active operates within predefined variance boundaries, achieving both adaptability and consistency.
Data Source
AI summary
The present invention is related to systems and methods for adaptive parameter modification in a data processing system.