LDPC Decoder Matrix Triangulation for Lost Data Recovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for recovering lost and/or corrupted data transmitted from a transmitter device to a receiver device, such as those using Low Density Parity Check Codes, face challenges with high complexity and limited flexibility, particularly in mobile broadcasting applications where signal fades and outages are common.
Innovation Solution
A method that employs a hybrid decoder using an iterative decoder initially and a maximum-likelihood decoder only when necessary, with the parity check matrix H transformed into a triangular form by shifting columns to reduce computational complexity, allowing for efficient recovery of data even with less-performing processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a maximum-likelihood decoder is used for recovery of lost and/or corrupted data, then the recovery performance is improved, but the computational complexity increases significantly
Solution Approach 1:
The parity check matrix H is segmented into two sub-matrices: a triangular sub-matrix and a remaining sub-matrix. The triangular sub-matrix is used for efficient iterative decoding, while the remaining sub-matrix is reserved for maximum-likelihood decoding only when needed. This segmentation allows the system to achieve high recovery performance by selectively applying the computationally intensive maximum-likelihood method only to the necessary portion of the data, rather than applying it to the entire codeblock.
Solution Approach 2:
Different decoding strategies are applied to different portions of the data based on their specific needs. The triangular sub-matrix portion is decoded using the computationally efficient iterative method, while the remaining sub-matrix portion is decoded using the more reliable but complex maximum-likelihood method. This local differentiation optimizes the balance between computational complexity and recovery performance by applying the appropriate decoding strength to each region.
2Reliability
If a maximum-likelihood decoder is used for recovery of lost and/or corrupted data, then the recovery performance is improved, but the processing time increases
Solution Approach 1:
The data is segmented such that the triangular sub-matrix can be efficiently decoded using iterative methods, which require less processing time. Only the remaining sub-matrix requires maximum-likelihood decoding, significantly reducing the overall decoding time compared to applying maximum-likelihood decoding to the entire codeblock while maintaining high recovery performance.
3Device complexity
If iterative decoding is used for recovery of lost and/or corrupted data, then the computational complexity is reduced, but the recovery performance deteriorates
Solution Approach 1:
The parity check matrix is segmented into a triangular sub-matrix and a remaining sub-matrix. The triangular sub-matrix is decoded using the computationally simple iterative method, while the remaining sub-matrix is decoded using the more reliable maximum-likelihood method. This segmentation allows the system to achieve high recovery performance by ensuring that the critical remaining sub-matrix receives the more powerful decoding treatment.
Solution Approach 2:
Different decoding quality levels are applied to different portions of the data. The triangular sub-matrix receives standard iterative decoding, while the remaining sub-matrix receives enhanced maximum-likelihood decoding. This local quality differentiation ensures that the most critical parts of the data (those in the remaining sub-matrix) receive the highest level of recovery effort.
Data Source
AI summary
A method for recovery of lost and/or corrupted data transmitted from a transmitter device to a receiver device. The data is coded by an encoder connected to the transmitter device. The data is transmitted from the transmitter device to the receiver device via a transmission system and is decoded by means of a decoder connected to the receiver device. This is performed through application of a low density parity check method, wherein lost and/or corrupted data is restored during decoding. The decoding is performed by solving the equation system of the parity check matrix H. The parity check matrix H is brought into a triangular form by column and/or row permutations. Columns of a sub-matrix B of the matrix H which impede the triangulation process are shifted into a sub-matrix P of the matrix H so that the triangulation process can be continued until the matrix H except for the sub-matrix P has been completely brought into a triangular form. The Gaussian elimination method is applied to a part of the sub-matrix P. The selection of the column or columns of the sub-matrix B which are to be shifted into the sub-matrix P is performed on the basis of the weight of the column which corresponds to the number of non-zero-entries in the column, and/or on the basis of the weight of the rows of the sub-matrix B connected to the column.