Academic Journal

Modulation Recognition of 5G Signals Based on AlexNet Convolutional Neural Network

التفاصيل البيبلوغرافية
العنوان: Modulation Recognition of 5G Signals Based on AlexNet Convolutional Neural Network
المؤلفون: Zhang, Qing, Hu, Guobing, Zhao, Pinjiao, Yang, Li
المصدر: Journal of Physics: Conference Series ; volume 1453, issue 1, page 012118 ; ISSN 1742-6588 1742-6596
بيانات النشر: IOP Publishing
سنة النشر: 2020
الوصف: Aiming at the problem that signal modulation recognition under a non-cooperative condition requires substantial a priori information of the signal and a complex artificial selection of the features, this paper proposes a modulation recognition method for the 5th-generation (5G) signal modulation based on the AlexNet convolutional neural network. For the five commonly used 5G signals (3GPP R15 protocol recommendations) of π/2-BPSK, QPSK, 16QAM, 64QAM, and 256QAM, the constellation is selected as input feature of the AlexNet network to construct the recognition classification algorithm. The simulation results show that the average recognition accuracy of the five commonly used 5G signals is up to 90% under a 15 dB signal-to-noise ratio (SNR), an improved performance compared with that of the existing recognition algorithms based on signal scatter plots.
نوع الوثيقة: article in journal/newspaper
اللغة: unknown
DOI: 10.1088/1742-6596/1453/1/012118
DOI: 10.1088/1742-6596/1453/1/012118/pdf
الاتاحة: http://dx.doi.org/10.1088/1742-6596/1453/1/012118
https://iopscience.iop.org/article/10.1088/1742-6596/1453/1/012118/pdf
https://iopscience.iop.org/article/10.1088/1742-6596/1453/1/012118
Rights: http://creativecommons.org/licenses/by/3.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining
رقم الانضمام: edsbas.C0651109
قاعدة البيانات: BASE
الوصف
DOI:10.1088/1742-6596/1453/1/012118