Anti-Causal Noise Predictive Filter Circuit for Burst Error Reduction
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Solution Overview
Problem
Existing data processing systems face limitations in recovering originally written data due to media noise, particularly burst errors caused by long runs of bit periods, which are not effectively addressed by causal noise predictive filtering.
Innovation Solution
The implementation of anti-causal noise predictive filtering, which uses historical information including future noise samples to predict and filter out noise, balancing error locations across non-transitory runs, and incorporating a data detection algorithm like the maximum a posteriori or Viterbi algorithm, along with a data decoding algorithm such as Reed Solomon or low density parity check, to improve bit error rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If causal noise predictive filtering is used, then the system can process data in real-time, but it cannot effectively address burst errors caused by long runs of bit periods
Solution Approach 1:
The patent inverts the traditional causal filtering approach by implementing anti-causal noise predictive filtering. Instead of predicting noise based only on past samples, the system uses future noise samples to predict and filter current noise, thereby effectively addressing burst errors that causal filtering cannot handle
Solution Approach 2:
The system performs preliminary noise prediction using future noise samples before the actual data detection occurs. By pre-calculating the noise characteristics and applying the anti-causal filter in advance, the system prepares optimized filtering parameters that improve subsequent error correction capability
2Reliability
If anti-causal noise predictive filtering is implemented, then burst errors are reduced and bit error rate improves, but computational complexity increases
Solution Approach 1:
The patent segments the noise predictive filtering into multiple independent components: a noise predictive filter bank with multiple filters tuned to different noise patterns, and a selective application mechanism that chooses which filter to apply. This segmentation reduces overall computational complexity by avoiding the need to apply all filters to all data
Solution Approach 2:
The system changes the temporal parameter of the filtering operation by using anti-causal filters that operate with future noise samples. This parameter change allows the system to achieve better error rates without proportionally increasing complexity, as the anti-causal structure enables more efficient noise prediction
3Measurement precision
If multiple passes through data detector and decoder are performed, then data recovery accuracy improves, but processing time increases
Solution Approach 1:
The anti-causal noise predictive filtering performs preliminary noise reduction before data detection and decoding. By pre-filtering the data with optimized anti-causal filters, the system improves signal quality in advance, reducing the number of iterative passes needed through the detector and decoder, thereby decreasing total processing time
Data Source
AI summary
Various embodiments of the present invention provide systems and methods for data processing. As an example, a data processing circuit is disclosed that includes a data detector circuit. The data detector circuit includes an anti-causal noise predictive filter circuit and a data detection circuit. In some cases, the anti-causal noise predictive filter circuit is operable to apply noise predictive filtering to a detector input to yield a filtered output, and the data detection circuit is operable to apply a data detection algorithm to the filtered output derived from the anti-causal noise predictive filter circuit.