Error Correction Decoding with Randomized Reliability Metrics
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Solution Overview
Problem
Existing error correction methods in noisy data channels require significant hardware resources and are inefficient in determining which bits are erroneous, especially when only partially redundant data is transmitted, leading to increased costs and decreased data throughput.
Innovation Solution
A method that modifies reliability metrics of an input data stream with random perturbation values, generating an output data stream by iteratively inverting bits with the lowest reliability metrics to correct errors, using a weighted sum of channel measurements and parity check results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing error correction methods are used to correct errors in noisy data channels, then error correction capability is improved, but hardware resource requirements and complexity increase significantly
Solution Approach 1:
The patent changes the parameter of reliability metric calculation by introducing random perturbation values to modify the weighted sum of channel measurements and parity check results. This stochastic modification allows the system to escape local optima and improve error correction capability without requiring more hardware resources, effectively resolving the contradiction between reliability and device complexity
Solution Approach 2:
The patent introduces random perturbation values as an intermediary element that modifies the reliability metrics. These perturbations act as a mediator between the channel measurements/parity checks and the bit selection process, enabling more effective error correction with the same hardware resources by adding controlled randomness to the decision-making process
2Measurement precision
If existing error correction methods are used to determine erroneous bits, then error detection capability is improved, but efficiency decreases due to inability to intelligently select which bits to flip
Solution Approach 1:
The patent modifies the reliability metric parameter by adding random perturbations, which changes how bits are selected for flipping. This parameter change enables the system to identify and correct multiple erroneous bits more efficiently by escaping local optima in the reliability landscape, thereby improving both measurement precision and productivity simultaneously
Solution Approach 2:
The patent introduces dynamic randomness through perturbation values that vary across iterations and bits. This dynamic approach allows the error correction process to adaptively select different bits for flipping based on the current state, improving efficiency by avoiding static, suboptimal selection patterns while maintaining high error detection capability
3Quantity of substance
If partial redundancy is used in data transmission to reduce costs, then transmission cost is reduced, but error correction difficulty increases
Solution Approach 1:
The patent changes the approach to using partial redundancy by combining it with random perturbation in reliability metric calculation. This parameter change allows the system to effectively utilize limited redundant information to identify erroneous bits among non-redundant data, reducing error correction difficulty while maintaining cost-effective partial redundancy transmission
Solution Approach 2:
The patent implements a feedback mechanism where parity check results are combined with channel measurements and random perturbations to iteratively improve bit selection. This feedback loop allows the system to progressively identify erroneous bits even with partial redundancy, reducing the difficulty of error correction by continuously refining the reliability metrics based on accumulated information
Data Source
AI summary
For low complexity error correction, a decoder modifies each reliability metric of an input data stream with a random perturbation value. The reliability metric comprises a weighted sum of a channel measurement for the input data stream and parity check results for the input data stream. In addition, the decoder may generate an output data stream as a function of the reliability metrics.