Data Shaping Circuit Using Genetic Algorithm Maps
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Solution Overview
Problem
Existing data processing systems face challenges in efficiently processing soft data, as some data sets may not converge regardless of the number of iterations through data detector and decoder circuits, necessitating advanced methods for data manipulation.
Innovation Solution
The implementation of a data processing system that includes a data detector circuit and a data shaping circuit, where the shaping circuit generates a shaping map using a genetic algorithm during training and reshapes the detected output to improve data quality, employing a genetic algorithm based calculation circuit and quality determination circuit to optimize data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple iterations through data detector and decoder circuits are used, then data decoding accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing shaping maps during a training phase before actual data processing occurs. During operation, these pre-computed shaping maps are directly applied to reshape soft data outputs, eliminating the need for iterative optimization during real-time processing and thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The system creates a copy of the optimization process by generating shaping maps through a training phase that simulates the data processing workflow. These shaping maps serve as pre-computed templates that can be directly applied during operational mode, copying the iterative optimization results into a form that enables faster execution without repeated calculations.
2Device complexity
If traditional data detection algorithms are used, then processing simplicity is maintained, but data quality and convergence fail for certain data sets
Solution Approach 1:
The system changes the parameters of the data representation by introducing shaped soft data outputs that transform the original data domain. The shaping maps modify the probability distributions and characteristic values of soft data, creating transformed representations that improve convergence behavior and data quality while maintaining algorithmic simplicity through direct parameter transformation rather than complex iterative optimization.
3Measurement precision
If genetic algorithm-based shaping maps are generated during training, then data quality is improved, but training time and computational resources increase
Solution Approach 1:
The system performs the computationally intensive genetic algorithm optimization during a training phase as a preliminary action. Once the shaping maps are generated during training, they can be directly applied during operational mode without re-executing the complex optimization algorithms, thus concentrating computational resources during training rather than during repeated operational processing.
Solution Approach 2:
The system creates a copy of the optimized data representations through shaping maps that capture the essential characteristics of high-quality data processing. These copied representations can be directly applied during operation without re-computing the complex genetic algorithm results, thereby reducing ongoing computational resource requirements while maintaining data quality improvements.
Data Source
AI summary
The present inventions are related to systems and methods for data processing, and more particularly to systems and methods for manipulating soft data in a data processing system.