Open AccessImproved min‐sum algorithm based on density evolution for low‐density parity check codes
Author(s) -
Wang Xiumin,
Cao Weilin,
Li Jun,
Shan Liang,
Cao Haiyan,
Li Jinsong,
Qian Fanglei
Publication year - 2017
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2017.0014
Subject(s) - algorithm , decoding methods , berlekamp–welch algorithm , belief propagation , mathematics , probability density function , computer science , minimum mean square error , low density parity check code , function (biology) , sequential decoding , statistics , block code , estimator , evolutionary biology , biology
In this study, the authors present an improved min‐sum (MS) algorithm based on density evolution (DE) called the DE MS algorithm for low‐density parity check codes. First, they use DE theory to calculate the probability density function of the check‐to‐variable message of the belief propagation (BP) algorithm and the MS/normalised MS (NMS) algorithm and furthermore to calculate the normalised factor α . Then, α is modified further by using the weighted average. Finally, in order to ensure the decoding performance and reduce the hardware complexity, the same α is used for different signal‐to‐noise ratios. The simulation results show that a gain of about 0.2 dB can be achieved in comparison with the classical NMS algorithm. In addition, this algorithm can obtain the same decoding performance compared with the Linear Minimum Mean Square Error (LMMSE) MS algorithm whose decoding performance is very close to that of the BP algorithm, and it also saves around 24.57% of logic elements and 34.33% of memory bits compared with the LMMSE MS algorithm at the same time.