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 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.