A Novel EICNet-Based Iterative Receiver for PLDPC-Coded M -ary DCSK Systems

In this letter, we propose a novel neural network-aided receiver, called extrinsic-information chaos network-aided iterative decoding (EICNet-ID) receiver, for protograph low-density parity-check (PLDPC)-coded <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {M}$ </tex-math></inline-formula>-ary differential chaos shift keying (DCSK) systems. Specifically, the EICNet structure captures the correlation features between the chaotic sequences of the received <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {M}$ </tex-math></inline-formula>-ary DCSK symbol to obtain more accurate a-priori log-likelihood ratio (LLR) without requiring the channel state information (CSI). Moreover, the EICNet structure considers both the received symbol and its a-posteriori probabilities, allowing the extrinsic information provided by the PLDPC decoder to update network input and enhance error performance. Both simulation and analysis results demonstrate that the proposed EICNet-ID receiver exhibits desirable performance compared to the existing counterparts.