Open AccessReverse recognition of LDPC codes based on log-likelihood ratio
Author(s) -
Renxin Liu,
li-min Zhang,
Zhao-gen Zhong,
Xueli Sun
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1607/1/012106
Subject(s) - low density parity check code , coding (social sciences) , algorithm , computational complexity theory , computer science , channel code , mathematics , pattern recognition (psychology) , decoding methods , artificial intelligence , statistics
With the development of Adaptive Modulation and Coding (AMC) technology, blind identification of channel coding parameters has recently attracted more attention. To reduce the computational complexity of the closed set recognition of problem low-density parity-check (LDPC) codes, this paper improves a closed set reverse recognition algorithm based on the maximum mean log-likelihood ratio. Before using soft decision information to make a decision, the check vector is first filtered to select some check vectors to reduce the computational complexity. The experimental results show that this method can complete the inverse identification of closed sets of LDPC codes in the case of low signal-to-noise ratio. Compared with the previous algorithm, the time complexity of the proposed algorithm is significantly reduced.