Hierarchical Error Correction Coding 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 by equally protecting all source data, leading to resource inefficiencies, increased transmission and reception delays, and higher network capacity requirements.
Innovation Solution
An error correction encoding method that classifies digital data into multiple classes, applying different levels of protection based on priority, using recursive systematic convolutional encoding and mixing steps to optimize protection and reduce resource usage, allowing for hierarchical error protection and efficient data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UEP codes process each class separately, then each class can be protected according to its priority level, but transmission resources are lost due to additional headers and resynchronization requirements
Solution Approach 1:
The patent merges multiple classes of data into a single systematic code structure, eliminating the need for separate processing of each class. This combining approach removes the overhead of multiple headers and resynchronization operations, thereby reducing transmission resource consumption while maintaining unequal error protection across different data classes.
Solution Approach 2:
The patent creates a universal encoding framework that handles multiple data classes with different priority levels within a single systematic code. This multi-functional approach allows the same code structure to provide differentiated protection levels for various classes without requiring class-specific processing mechanisms, thus saving transmission resources.
2Reliability
If UEP codes process each class separately, then each class can be protected according to its priority level, but reception delays increase due to resynchronization steps
Solution Approach 1:
By combining all data classes into a single systematic code structure, the patent eliminates the need for separate resynchronization operations for each class. The receiver can process all classes simultaneously through a unified decoding process, thereby reducing reception delays while maintaining priority-based error protection.
3Ease of manufacture
If all source data are equally protected using turbo codes, then implementation is simple, but transmission resources are inefficiently used
Solution Approach 1:
The patent applies local quality by implementing different protection levels for different classes of data within the same systematic code. High-priority data classes receive stronger protection while low-priority classes receive lighter protection, optimizing the use of transmission resources according to the actual importance of each data class, thereby improving efficiency without significantly complicating the implementation.
Data Source
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AI summary
The present invention relates to an error correction encoding method (300) for encoding in parallel so-called source digital data (30), having the form of a frame (102), wherein said data can be classified into N classes (102i), where N is an integer at least equal to 2. The encoding method according to the invention comprises: - a first recursive systematic convolutional encoding step (306i) of data to be encoded, formed by the data of the class 1 (1021); and - an 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-l : - nth mixing (304n+1) of a set formed by the data of the class n +1 (102n+1) and the systematic data of the preceding encoding; - (n +1)th recursive systematic convolutional encoding (306n+1) of data to be encoded, formed by the result of the nth mixing. The invention also relates to a method for decoding encoded data with the encoding method according to the invention, as well as associated encoding device and decoding device.