LDPC Test Signal Generation for Fast Error Floor Estimation
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Solution Overview
Problem
Existing methods for estimating error probability of low-density parity-check codes in data-dependent noise channels are inefficient, particularly at high signal-to-noise ratios, due to the need for extensive simulations and increased error rates caused by miscorrection events and error floors related to low-weight trapping sets.
Innovation Solution
The method involves dividing codewords into classes based on error production, selecting dominant classes, and running simulations on representative test waveforms to estimate error probability, with encoder calibration and binary search to focus on noise models likely to produce errors, thereby reducing simulation time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Monte-Carlo simulation methods are used to estimate error probability, then comprehensive error coverage is achieved, but simulation time becomes excessively long and resources are wasted
Solution Approach 1:
The patent segments the simulation process into two distinct phases: an initial phase that identifies dominant trapping sets and error-prone codeword patterns, and a subsequent phase that focuses simulations only on these identified critical cases. This segmentation transforms the exhaustive simulation approach into a targeted one, dramatically reducing simulation time while maintaining accuracy in estimating error probability.
Solution Approach 2:
The patent performs preliminary analysis before the main simulation to identify and catalog dominant trapping sets and error-prone codeword patterns. This preliminary action creates a focused test set that captures the most significant error sources, allowing the subsequent simulation to achieve accurate error probability estimation without requiring exhaustive simulation of all possible error cases.
2Measurement precision
If exhaustive simulations are run to achieve accurate error probability estimation, then measurement precision improves, but productivity decreases
Solution Approach 1:
The simulation workload is segmented into identification tasks (finding dominant trapping sets) and estimation tasks (calculating error probability). By separating these tasks and focusing the estimation phase on pre-identified critical cases, the system achieves high measurement precision without sacrificing productivity, as the estimation phase requires far fewer simulations than a comprehensive approach would demand.
Solution Approach 2:
The patent extracts and isolates the dominant trapping sets and error-prone patterns from the entire code space. By taking out only these critical elements for detailed simulation analysis, the system achieves accurate error probability estimation with minimal simulation resources, thereby maintaining high productivity while ensuring measurement precision.
3Speed
If noise amplitude is increased to accelerate convergence in finding critical noise levels, then speed improves, but measurement precision deteriorates due to miscorrection events
Solution Approach 1:
The patent performs preliminary identification of dominant trapping sets and their associated critical noise levels using binary search. This preliminary action establishes accurate reference points for error boundaries, enabling subsequent simulations to use appropriately scaled noise levels that accelerate convergence without causing miscorrection events that would compromise measurement precision.
Solution Approach 2:
The patent dynamically adjusts noise amplitude parameters based on the identified critical noise levels for different trapping sets. By changing the noise parameter scale to match the critical levels of dominant trapping sets, the system achieves fast convergence in simulations while maintaining accuracy in error boundary distance measurement, avoiding both premature convergence and miscorrection events.
Data Source
AI summary
A method for estimating error rates in low-density parity check codes includes calibrating an encoder according to specific channel parameters and according to dominant error events in the low-density parity-check code. Dominant codewords are classified based on characteristics of each codeword that are likely to produce similar error rates at similar noise levels; codeword classes that produce the highest error rate are then tested. Error boundary distance is estimated using multiple binary searches on segments. Segments are defined based on codeword, trapping set and biasing noise components of the channel. To improve calculation speed the most significant subclasses of codewords, trapping sets and noise signals are used.