.png){kind=icon}
.png){kind=icon}
Return
Adaptive Gradient Descent Bit-Flipping Diversity Decoding
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2022.3195026.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this letter we propose a novel framework for designing decoders, for Low-Density Parity Check (LDPC) codes, that surpasses the frame error rate performance of Belief-Propagation (BP) decoding on binary symmetric channels. Its key component is the adaptation method, based on the genetic optimization algorithm, that is incorporated into the recently proposed Gradient Descent Bit-Flipping Decoding with Momentum (GDBF-w/M). We show that the resulting decoder outperforms all state-of-the-art probabilistic bit-flipping decoders and, additionally, it can be trained to perform beyond BP decoding, which is verified by numerical examples that include codes used in IEEE 802.3an and 5GNR standards. The proposed framework provides a systematic method for decoder optimization without requiring knowledge of trapping sets. Moreover, it is applicable to both regular and irregular LDPC codes.
Keywords:
Optimization
Maximum likelihood decoding
Iterative decoding
Genetic algorithms
Probabilistic logic
Standards
Monte Carlo methods
Belief-propagation
error-floors
gradient descent bit-flipping
genetic algorithm
low-density parity-check codes
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W