Improved min-sum algorithm based on density evolution for low-density parity check codes - Wang, Xiumin; Cao, Weilin; Li, Jun; Shan, Liang; Cao, Haiyan; Li, Jinsong; Qian, Fanglei (IET communications, 2017)
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.