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author:

He, Y. (He, Y..) [1] | Lv, L. (Lv, L..) [2] | Fang, Y. (Fang, Y..) [3] | Yin, L. (Yin, L..) [4] | Chen, P. (Chen, P..) [5]

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Scopus

Abstract:

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 M-ary differential chaos shift keying (DCSK) systems. Specifically, the EICNet structure captures the correlation features between the chaotic sequences of the received M-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. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keyword:

deep learning iterative decoding M-ary differential chaos shift keying (DCSK) systems protograph low-density parity-check (PLDPC) codes

Community:

  • [ 1 ] [He Y.]School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China
  • [ 2 ] [Lv L.]School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China
  • [ 3 ] [Fang Y.]School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China
  • [ 4 ] [Yin L.]The School of Information Science and Technology, Tsinghua University, Beijing, 100084, China
  • [ 5 ] [Chen P.]The Department of Electronic Information, Fuzhou University, Fuzhou, 350116, China

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Source :

IEEE Communications Letters

ISSN: 1089-7798

Year: 2024

Issue: 12

Volume: 28

Page: 2700-2704

3 . 7 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 1

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