6G automatic modulation classification using deep learning models in the presence of channel noise, CFO, and PN

Majlesi Journal of Electrical Engineering

مقاله 8، دوره 18، شماره 4، اسفند 2024، صفحه 1-9   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2024.710184

نویسندگان

Zhraa Zuheir Yahya¹؛ Dia M. Ali^{* 1}؛ Marwa Y. Abdallah²

¹Department of Communication Engineering, College of Electronics Engineering, Ninevah University, Ninevah, Iraq.

²Techniques Engineering Department, Technical Engineering College, Northern Technical University, Ninevah, Iraq.

چکیده

An efficient and remarkable automatic modulation classification (AMC) technique is essential with
the advent of sixth-generation (6G) communication systems. Using the pre-trained convolutional
neural network (CNN), a deep learning (DL) approach to classify eight types of digital modulated
signals. National Instrument LabVIEW NXG is used to build the modulation transceivers at
100 GHz, a 6G carrier frequency. The dataset was collected in a complicated environment, including carrier frequency offset (CFO), phase noise (PN), and distinct signal-to-noise ratios (SNR). Through experimental simulation, an improvement in the classification accuracies was achieved. In particular, the outstanding accuracy rates achieved are 98.68% and 96.05% using ResNet18 and ResNet101, respectively. Furthermore, these models can classify the modulated
signals at lower SNRs. These innovative models are suitable and effective to utilize for 6G wireless communication networks.

کلیدواژه‌ها

Automatic modulation classification؛ Deep learning؛ Carrier frequency offset؛ Phase noise

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