Selective Sampling for Data Recovery in Digital Channels
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Solution Overview
Problem
Existing data recovery methods face inefficiencies in accurately recovering data from unrecoverable sectors due to errors, as they often require repetitive reads and lack optimal selection of recovery samples based on quality metrics.
Innovation Solution
A data decoding circuit generates multiple samples and quality metrics for unrecovered data sectors, comparing these to select the best sample for error recovery, thereby optimizing the recovery process by using the most effective sample for each sector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repetitive reads are performed to recover data from unrecoverable sectors, then data recovery attempts are made, but recovery time increases and efficiency decreases
Solution Approach 1:
The system performs preliminary actions by generating multiple samples and quality metrics during the reading process itself, rather than waiting for recovery failures before acting. The data decoding circuit generates multiple samples for unrecovered sectors and computes quality metrics in advance, enabling informed recovery decisions without requiring repetitive time-consuming reads.
Solution Approach 2:
The system implements feedback by using quality metrics to guide the error recovery process. The data decoding circuit compares quality metrics of different samples and uses this feedback information to select the best samples for recovery, thereby avoiding blind repetitive reads and optimizing recovery time based on actual sample quality.
2Reliability
If multiple decoding attempts are made to improve data recovery, then recovery accuracy improves, but processing complexity and time increase
Solution Approach 1:
The system applies local quality by evaluating and selecting samples based on their individual quality metrics rather than treating all decoding attempts uniformly. The data decoding circuit generates quality metrics for each sample and makes localized decisions about which samples to use for recovery, optimizing accuracy without requiring complex global re-processing.
Solution Approach 2:
The system changes the parameter approach by introducing quality metrics as a new selection criterion. Instead of relying solely on the number of decoding attempts or fixed recovery procedures, the system uses quality metric comparisons to dynamically select the best samples, simplifying the recovery process while improving accuracy.
3Productivity
If traditional error recovery methods are used without sample selection, then the process is simple, but recovery effectiveness is reduced
Solution Approach 1:
The system implements self-service by having the data decoding circuit automatically generate quality metrics and select the best samples without external intervention. The circuit self-evaluates sample quality and makes autonomous recovery decisions, improving effectiveness while keeping the complexity contained within the decoding circuit itself.
Data Source
AI summary
Systems and methods are disclosed for error recovery in a digital data channel. In an error recovery approach when the hardware fails to recover a sector, the sample for that sector can be saved along with a metric measure that indicates the quality of the sample. This process can begin from a first on-the-fly receiving and decoding of data. During each step of error recovery, a retry attempt may either use samples obtained during a new decoding attempt or may use a sample, or a combination of samples, having the best metric from an earlier attempt, or a combination of earlier attempts, to perform the recovery during a current retry recovery attempt.