Adaptive Filter Coefficient Constraint for Signal Range Control
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Solution Overview
Problem
Existing systems for signal processing in communication and magnetic recording storage systems face challenges in adapting filter parameters effectively, leading to issues with signal quantization and saturation, which affect performance and detector error rates.
Innovation Solution
A circuit and method that update adaptive coefficients and constrained parameters based on error signals and desired values to generate sample sequences, optimizing filter parameters using techniques like minimum mean-square error and adaptive algorithms, while incorporating a constraint parameter to maintain desired signal dynamic range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If filter parameters are adapted using conventional algorithms, then signal processing performance is improved, but quantization and saturation issues occur affecting detector error rates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the constrained parameter based on the statistical properties (mean and standard deviation) of the adaptive coefficients. This allows the filter to adapt its parameters to maintain optimal signal range and avoid quantization/saturation issues while preserving detection accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the constrained parameter is continuously updated based on error signals and the statistical characteristics of adaptive coefficients. This feedback loop enables the system to automatically correct for quantization and saturation effects, maintaining reliable signal processing performance.
2Measurement precision
If adaptive coefficients are updated based on error signals, then filter accuracy is improved, but signal dynamic range may deviate from desired values
Solution Approach 1:
The patent simultaneously updates both adaptive coefficients (for accuracy) and constrained parameters (for dynamic range) based on error signals and statistical properties. This dual parameter adjustment ensures that filter accuracy improves while signal dynamic range remains within desired boundaries.
Solution Approach 2:
The patent introduces dynamic adjustment of the constrained parameter that adapts to the changing statistical properties of the adaptive coefficients. This dynamic mechanism allows the system to maintain optimal signal dynamic range while accommodating variations in filter accuracy requirements.
3Reliability
If constrained parameters are maintained to preserve signal range, then detector error rates are reduced, but filter adaptability may be limited
Solution Approach 1:
The constrained parameter is dynamically adjusted based on the statistical properties of adaptive coefficients rather than being fixed. This dynamic constraint allows the filter to adapt to changing signal conditions while maintaining signal range within acceptable boundaries, thus preserving detector accuracy.
Solution Approach 2:
The patent changes the constrained parameter values based on error signals and coefficient statistics, allowing the filter to adapt its behavior. This parameter adaptation enables the system to maintain reliability by keeping signal range optimal while preserving necessary filter adaptability.
Data Source
AI summary
An apparatus may include a circuit including a filter configured to update one or more adaptive coefficients of the filter based on an error signal. Further, the circuit may update a constrained coefficient of the filter based on the one or more adaptive coefficients, the constrained coefficient and a desired value. Moreover, the circuit may generate a sample of a sample sequence based on the one or more adaptive coefficients and the updated constrained coefficient, the error signal being based on the sample sequence.