Paper Info
Title
A Double-CNN BP Decoder on Fast Fading Channels Using Correlation Information
Abstract
This paper develops a novel double convolutional neural network (CNN) based belief propagation (BP) decoder to improve the error correcting performance on fast fading channels with correlated channel gain and correlated noise. The proposed double-CNN BP decoder consists of two CNNs for predicting channel gain and noise samples, respectively, concatenated with a BP decoder. The input of the BP decoder is the log-likelihood-ratio (LLR) values obtained according to the predicted channel gain along with denoised signals based on predicted noises. We note that the residual noises no longer obey a Gaussian distribution when denoising the signals. Thus, we further derive a new method to obtain the LLR values accurately. Simulation results show that the proposed double-CNN BP decoder achieves a maximum of 7dB gain compared to the conventional BP decoder and the proposed algorithm compensates the performance loss of BP decoder caused by correlation information.
Year
DOI
Venue
2019
10.1109/ICCChina.2019.8855827
2019 IEEE/CIC International Conference on Communications in China (ICCC)
Keywords
Field
DocType
Channel decoder,LDPC,CNN,correlated channel gain,correlated noise
Noise reduction,Residual,Computer science,Low-density parity-check code,Convolutional neural network,Fading,Algorithm,Communication channel,Real-time computing,Gaussian,Belief propagation
Conference
ISSN
ISBN
Citations 
2377-8644
978-1-7281-0733-2
0
PageRank 
References 
Authors
0.34
11
6
Name
Order
Citations
PageRank
Jun Li174790.31
Xiwei Zhao200.34
Jihao Fan300.34
Feng Shu430048.17
Shi Jin53744274.70
Yuwen Qian622.40