Convolutional LDPC Decoding With Iterative LLR Re-Demapping
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Solution Overview
Problem
Conventional convolutional LDPC decoding methods in communications systems face inaccuracies in decoding processes, leading to reduced transmission performance in high-speed information transmission systems, especially in coherent optical transmission systems.
Innovation Solution
The method involves adjusting the log-likelihood ratio (LLR) based on all check node information and symbol values, using a symbol caching unit and re-demapping units to improve decoding accuracy by iteratively updating check node information and LLR, ensuring accurate decoding results in convolutional LDPC decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional convolutional LDPC decoding methods are used, then the decoding process can be implemented, but the decoding accuracy is insufficient leading to reduced transmission performance
Solution Approach 1:
The patent implements feedback mechanisms where the re-demapping unit receives check node information from the decoding unit and uses it to recalculate LLR values. The updated LLR values are fed back to improve the decoding process, creating a closed-loop system that continuously refines decoding accuracy based on received feedback information.
Solution Approach 2:
The patent introduces a re-demapping dimension to the traditional decoding process. By adding the re-demapping unit that operates in parallel with the conventional decoding path, the system processes information through an additional dimensional layer, enabling more comprehensive analysis and improving decoding accuracy beyond the single-pass conventional approach.
2Measurement precision
If the LLR is adjusted based on all check node information and symbol values, then the decoding accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the decoding process into distinct functional units: the decoding unit that processes check node information, the re-demapping unit that recalculates LLR values, and the symbol caching unit that stores intermediate results. This segmentation allows the complex computation to be distributed across multiple specialized modules, making the system more manageable and implementable despite the increased computational requirements.
Solution Approach 2:
The patent implements preliminary action by caching symbol values before the decoding process and preparing check node information in advance. The symbol caching unit stores symbol values that will be needed during decoding, and the re-demapping unit pre-calculates LLR adjustments based on available information, reducing the computational burden during the actual decoding operation.
Data Source
AI summary
The present disclosure provides a method, system, and terminal device for data transmission in an unlicensed spectrum, effectively reduce mutual signal interference between different systems while meeting regulation constraints on use of the unlicensed spectrum. The method in the present disclosure includes: at a processing start moment of a terminal device in a current channel occupancy time window of a network device, when remaining duration of the current channel occupancy time window of the network device is greater than or equal to duration for the terminal device to transmit a to-be-sent data packet to the network device, selecting based on a user attribute of the terminal device and from a mapping relationship between a user attribute and a transmission mode; and sending the to-be-sent data packet to the network device in the selected transmission mode.