A parallel sliding-window belief propagation algorithm for Q-ary LDPC codes accelerated by GPU

2020 ◽  
Vol 79 (45-46) ◽  
pp. 34287-34300 ◽  
Author(s):  
Bowei Shan ◽  
Sihua Chen ◽  
Yong Fang
Keyword(s):  
Belief Propagation ◽  
Ldpc Codes ◽  
Sliding Window ◽  
Propagation Algorithm ◽  
Belief Propagation Algorithm

scholarly journals Comprehensive Algorithmic Review and Analysis of LDPC Codes

2015 ◽  
Vol 16 (1) ◽  
pp. 111
Author(s):  
Waheed Ullah ◽  
Abid Yahya
Keyword(s):  
Belief Propagation ◽  
Ldpc Codes ◽  
Short Length ◽  
The Other ◽  
Comprehensive Review ◽  
Logarithmic Model ◽  
Awgn Channel ◽  
Propagation Algorithm ◽  
Belief Propagation Algorithm ◽  
First Time

Due to the increasing popularity of LDPC codes and its demand for future applications, first time in this paper, LDPC coding techniques have been systematically summarized and analyzed. The paper gives the comprehensive review of LDPC encoder, decoder and its architecture for simulation and implementation. The paper is specially intended for giving an insight of the algorithmic overview of the LDPC encoder, decoder and its architecture for research and practical purposes. The original belief propagation algorithm (BPA) , logarithmic model of BPA , and the other simplified form of the logarithmic sum product algorithms (SPA) has been elaborated and analyzed for medium and short length codes under AWGN channel

On the iterative decoding of sparse quantum codes

2008 ◽  
Vol 8 (10) ◽  
pp. 986-1000
Author(s):  
D. Poulin ◽  
Y. Chung
Keyword(s):  
Iterative Decoding ◽  
Belief Propagation ◽  
Quantum Codes ◽  
Error Correction Codes ◽  
Heuristic Methods ◽  
Tanner Graph ◽  
Coding Scheme ◽  
Propagation Algorithm ◽  
Belief Propagation Algorithm ◽  
Quantum Error

We address the problem of decoding sparse quantum error correction codes. For Pauli channels, this task can be accomplished by a version of the belief propagation algorithm used for decoding sparse classical codes. Quantum codes pose two new challenges however. Firstly, their Tanner graph unavoidably contain small loops which typically undermines the performance of belief propagation. Secondly, sparse quantum codes are by definition highly degenerate. The standard belief propagation algorithm does not exploit this feature, but rather it is impaired by it. We propose heuristic methods to improve belief propagation decoding, specifically targeted at these two problems. While our results exhibit a clear improvement due to the proposed heuristic methods, they also indicate that the main source of errors in the quantum coding scheme remains in the decoding.

On the iterative decoding of sparse quantum codes

2008 ◽  
Vol 8 (10) ◽  
pp. 986-1000
Author(s):  
D. Poulin ◽  
Y. Chung
Keyword(s):  
Iterative Decoding ◽  
Belief Propagation ◽  
Quantum Codes ◽  
Error Correction Codes ◽  
Heuristic Methods ◽  
Tanner Graph ◽  
Coding Scheme ◽  
Propagation Algorithm ◽  
Belief Propagation Algorithm ◽  
Quantum Error

We address the problem of decoding sparse quantum error correction codes. For Pauli channels, this task can be accomplished by a version of the belief propagation algorithm used for decoding sparse classical codes. Quantum codes pose two new challenges however. Firstly, their Tanner graph unavoidably contain small loops which typically undermines the performance of belief propagation. Secondly, sparse quantum codes are by definition highly degenerate. The standard belief propagation algorithm does not exploit this feature, but rather it is impaired by it. We propose heuristic methods to improve belief propagation decoding, specifically targeted at these two problems. While our results exhibit a clear improvement due to the proposed heuristic methods, they also indicate that the main source of errors in the quantum coding scheme remains in the decoding.

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