Multi-Class Error Correction Encoding With Mixed Convolutional 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 series, using recursive systematic convolutional encoding and mixing steps to provide non-uniform protection to different classes of data within a frame, allowing for adaptive protection based on priority levels, thereby reducing resource usage and enabling simultaneous modulation and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate processing of different data classes is implemented, then non-uniform error protection is achieved, but transmission delays and resource consumption increase
Solution Approach 1:
The data frame is segmented into N different classes based on importance, with each class receiving appropriate error protection levels. This segmentation enables differentiated protection strategies where critical data receives stronger protection while less critical data uses lighter protection, optimizing both reliability and transmission efficiency.
Solution Approach 2:
All N classes of data are merged into a single systematic convolutional encoding process rather than being processed separately. The encoder processes all classes together in one unified stream, eliminating the need for separate encoding, modulation, and resynchronization operations that would otherwise increase transmission delays and resource consumption.
2Reliability
If separate encoding of each data class is performed, then priority-based protection is achieved, but additional headers and resynchronization processings are required
Solution Approach 1:
A single systematic convolutional encoder is designed to handle multiple data classes universally. The encoder performs the function of protecting all N classes simultaneously through one encoding operation, eliminating the need for multiple separate encoders and their associated control mechanisms, thereby reducing device complexity while maintaining priority-based protection.
3Reliability
If separate modulation of encoded data classes is implemented, then class-specific protection is achieved, but transmission resources and network capacity requirements increase
Solution Approach 1:
The encoded data from all N classes are combined into a single modulation stream. This unified modulation approach allows all classes to be transmitted together using the same modulation resources, eliminating the need for separate modulation processes for each class and thereby reducing transmission resource consumption and network capacity requirements.
Data Source
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AI summary
The present invention relates to an error correction encoding method (100) for encoding so-called source digital data (30), having the form of a frame (102), wherein said data can be classified into N classes, where N is an integer at least equal to 2. The encoding method according to the invention comprises: - a first step of recursive systematic convolutional encoding (1101) of the data of the class 1 (1021); - 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-1: - nth mixing (108n+1) of a set formed by data of the class n+1 (102n+1), the systematic data and the parity data from a preceding encoding step; - (n + 1)th recursive systematic convolutional encoding (110n+1) of data formed by the result of 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.