An FCResNet-Based Iterative Detection for Protograph LDPC-Coded SRMR-DCSK Systems Under Correlated Noise

In the protograph-coded short reference multifold rate differential chaotic shift keying (SRMR-DCSK) systems, the signal transmission reliability is affected by the channel noise, especially when the noise is correlated. To solve this problem, this letter proposes a fully convolutional residual netw...

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Bibliographic Details
Published in:IEEE communications letters 2023-11, Vol.27 (11), p.2894-2898
Main Authors: Lu, Chengquan, Fang, Yi, Dai, Lin, Chen, Pingping, Han, Guojun
Format: Article
Language:English
Subjects:
Artificial neural networks
Channel noise
Chaotic communication
Convolution
correlated noise
Correlation
deep learning
fully convolutional residual network
Iterative methods
Keying
Likelihood ratio
protograph codes
Receivers
Short reference multifold rate differential chaotic shift keying
Signal transmission
Symbols
Training
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Summary:In the protograph-coded short reference multifold rate differential chaotic shift keying (SRMR-DCSK) systems, the signal transmission reliability is affected by the channel noise, especially when the noise is correlated. To solve this problem, this letter proposes a fully convolutional residual network (FCResNet)-based iterative detection structure to suppress the correlated noise in the protograph-coded SRMR-DCSK systems. Specifically, we design a new FCResNet tailored for the considered systems to accurately extract the characteristics of correlated noise, and thus the correlated noise can be easily removed from the received signal in an iterative fashion. In addition, we derive the log-likelihood ratio (LLR) expression of the protograph-coded SRMR-DCSK systems to facilitate the proposed detection. Simulation results demonstrate that the proposed FCResNet-based detection structure can obtain more desirable performance gain compared with the existing detection over Gaussian and multipath Rayleigh fading channels.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3317677