Read Channel Neural Noise Estimation for Lower Bit Error Rates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data storage devices face challenges in real-time adjustment of noise compensation parameters, which affect data detection and decoding efficiency, particularly due to varying noise sources in read signals.
Innovation Solution
Implementing a channel circuit with a trained neural network noise estimator that processes digital read signals to determine noise mixture components like jitter, electronic noise, and color noise, enabling real-time adjustment of read channel parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise compensation methods are used, then device complexity is reduced, but detection precision and bit error rate performance deteriorate
Solution Approach 1:
The patent replaces traditional mechanical/mathematical noise analysis systems with a neural network-based intelligent system. The neural network automatically learns and identifies noise patterns from read signals, substituting complex manual noise breakdown characterization with an automated machine learning approach that improves detection precision while managing system complexity through software-based solutions.
Solution Approach 2:
The patent dynamically adjusts noise compensation parameters based on real-time analysis of read signals. The neural network continuously evaluates noise characteristics and modifies compensation parameters accordingly, transitioning from static noise compensation to dynamic parameter adjustment, thereby improving bit error rate performance and detection accuracy.
2Productivity
If real-time noise estimation is implemented, then productivity and data detection efficiency improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs noise estimation using neural networks during the data detection process itself, rather than requiring separate preliminary noise characterization steps. The system continuously estimates noise parameters in real-time as data is being read and processed, eliminating the need for separate training phases and enabling immediate application of noise compensation to improve detection efficiency.
Solution Approach 2:
The neural network-based noise estimator operates autonomously, automatically analyzing read signals and generating noise compensation parameters without requiring external intervention or manual configuration. The system self-adjusts to changing noise conditions, eliminating the need for external noise characterization equipment or manual parameter tuning, thereby improving productivity while containing complexity through self-contained operation.
3Reliability
If noise breakdown characterization is performed, then reliability of data detection improves, but loss of time due to multiple writes and reads increases
Solution Approach 1:
The patent implements continuous noise estimation during normal data read operations, rather than performing discrete noise characterization steps that interrupt data access. The neural network continuously processes read signals to estimate noise parameters, ensuring that noise compensation is always current without requiring separate characterization operations, thereby maintaining high data detection reliability while minimizing time loss.
Solution Approach 2:
The system performs noise estimation in advance during the initial portion of each data sector read, enabling immediate application of noise compensation to the remaining data. This preliminary noise characterization occurs as part of the normal read operation rather than as a separate step, eliminating time loss while maintaining detection reliability through proactive noise assessment.
Data Source
AI summary
Example channel circuits, data storage devices, and methods for using a trained neural network to estimate the noise mixture in a read signal are described. Samples are determined from a digital read signal, such as the read signal from the non-volatile storage medium of a data storage device. The samples are processed through one or more instances of a neural network comprised of trained coefficients and outputting a set of estimate values for a noise mixture of the read signal. The set of estimate values may then be used to adjust parameters of the read channel for processing the read signal to detect and decode data.