Symbol Re-Grouping Decoding for Faster Data Recovery Convergence
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Solution Overview
Problem
Data processing systems often require multiple iterations through detector and decoder circuits to recover original data, but may not yield a correct result due to limitations in the number of allowed iterations, leading to inefficiencies and incomplete data recovery.
Innovation Solution
The system applies a data decode algorithm to different rotations of a decoder input, modifying it using first and second modification algorithms to yield multiple decoded outputs, and selects the best result based on unsatisfied checks, allowing for re-application of the algorithm guided by the selected decoding result to improve convergence and data recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple iterations are performed through data detector and decoder circuits, then data recovery accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies modification algorithms to the decoder input before the main decoding process to pre-process the data in a way that facilitates faster convergence during subsequent iterations. This preliminary transformation of the input data structure helps the decoder reach accurate results more quickly, reducing the total processing time while maintaining high data recovery accuracy.
Solution Approach 2:
The patent dynamically adjusts the decoding process by applying different modification algorithms (such as rotations) to the decoder input based on the specific characteristics of the received signal. This dynamic approach allows the system to adapt the processing method to match the actual data conditions, improving convergence speed and reducing unnecessary iterations while maintaining high accuracy.
2Reliability
If the number of allowed iterations is increased, then data recovery completeness is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The patent segments the decoding process into distinct phases: applying modification algorithms to generate transformed versions of the decoder input, performing initial decoding on these transformed inputs, and then using the results to guide subsequent decoding iterations. This segmentation allows the system to achieve complete data recovery through multiple specialized processing stages rather than relying solely on increasing the total number of generic iterations, thereby managing processing complexity more effectively.
Solution Approach 2:
The patent changes parameters of the decoder input by applying modification algorithms such as rotations that transform the data structure. These parameter changes create alternative representations of the input data that can be more easily decoded, enabling the system to achieve complete data recovery without proportionally increasing processing complexity. The modified inputs provide different perspectives that facilitate convergence with fewer iterations.
3Productivity
If modification algorithms are applied to decoder input, then decoding convergence is improved, but device complexity increases
Solution Approach 1:
The patent introduces modification algorithms as intermediary processing steps between the received signal and the main decoder. These intermediaries (modification algorithms) transform the decoder input into forms that are more amenable to decoding, improving convergence speed. The intermediaries handle the complexity of data transformation separately, allowing the main decoder to focus on its core function with simpler, pre-processed inputs, thereby managing overall device complexity more effectively.
Data Source
AI summary
The present invention is related to systems and methods for applying a data decode algorithm to different rotations or modifications of a decoder input as part of data processing.