Parallel Error Correction Encoding for Unequal Data Protection
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Solution Overview
Problem
Existing error correction encoding methods, such as turbo codes and UEP codes, face challenges in optimizing digital data transmission resources, leading to increased transmission and reception delays, resource inefficiencies, and higher network capacity requirements due to separate processing and resynchronization of different data classes.
Innovation Solution
An error correction encoding method that encodes digital data in parallel across multiple classes using recursive systematic convolutional encoding and mixing steps, allowing for differential protection based on class priority, reducing the need for separate processing and resynchronization, and enabling a single modulation scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate processing of different data classes is implemented, then unequal error protection is achieved, but transmission delay increases due to resynchronization requirements
Solution Approach 1:
The data frame is segmented into multiple classes (first class, second class, etc.) with different priority levels. Each class is processed through dedicated encoding paths with appropriate error protection, allowing differential protection while maintaining synchronized transmission through the parallel structure
Solution Approach 2:
The system dynamically adjusts error protection levels based on data class priority. Higher priority classes receive stronger error protection through additional parity bits and encoding passes, while lower priority classes use lighter protection, optimizing resource allocation based on actual data importance
2Reliability
If separate encoding of different data classes is performed, then differential error protection is achieved, but network capacity requirements increase due to additional headers and processing
Solution Approach 1:
Multiple data classes are merged into a single parallel encoding structure where they share common processing resources. The parallel encoding paths are synchronized and transmitted together, eliminating the need for separate headers and resynchronization processing that would be required for completely independent encoding of each class
Solution Approach 2:
The encoding device performs multiple functions through its parallel structure: it simultaneously encodes multiple data classes with different protection levels, generates appropriate parity bits for each class, and maintains synchronization all in one unified processing operation, reducing the need for additional dedicated resources
3Device complexity
If all source data are equally protected, then simplified encoding is achieved, but transmission efficiency decreases due to resource loss from separate processing
Solution Approach 1:
Different portions of the data (different classes) receive different levels of error protection tailored to their specific requirements. Critical data classes receive enhanced protection with additional parity bits and encoding passes, while less critical classes use standard protection, optimizing the balance between complexity and efficiency for each local data segment
Data Source
AI summary
An error correction encoding method is provided for encoding in parallel source digital data, having the form of a frame, wherein said data can be classified into N classes, where N is an integer at least equal to 2.The encoding method includes:a first recursive systematic convolutional encoding step of data to be encoded, formed by the data of the class 1; andan implementation of the following steps, for each n ranging from 1 to M, where M is a positive integer equal to or lower than N−1:nth mixing of a set formed by the data of the class n+1 and the systematic data of the preceding encoding; and(n+1)th recursive systematic convolutional encoding of data to be encoded, formed by the result of the nth mixing.Also disclosed is a related decoding method, as well as an associated encoding and decoding devices.