A Multi-Carrier Deep Learning CSK Communication System

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-03-31 DOI:10.1109/LCOMM.2025.3556474

Majid Mobini;Georges Kaddoum;Marijan Herceg

引用次数: 0

Abstract

A new Multi-Carrier Deep Learning Chaos Shift Keying (MC-DLCSK) modulation is designed in this letter. The proposed receiver includes a Neural Network (NN)-based classifier that recovers MC-DLCSK signals. The results demonstrate that the MC-DLCSK system achieves a considerable Bit Error Rate (BER) gain compared to all existing similar systems under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels.

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一种多载波深度学习CSK通信系统

本文设计了一种新的多载波深度学习混沌移位键控（MC-DLCSK）调制。该接收器包括一个基于神经网络（NN）的分类器，用于恢复MC-DLCSK信号。结果表明，在加性高斯白噪声（AWGN）和瑞利衰落信道下，MC-DLCSK系统比现有的所有类似系统都获得了可观的误码率增益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.