۶G automatic modulation classification using deep learning models in the presence of channel noise, CFO, and PN

An efficient and remarkable automatic modulation classification (AMC) technique is essential withthe advent of sixth-generation (۶G) communication systems. Using the pre-trained convolutionalneural network (CNN), a deep learning (DL) approach to classify eight types of digital modulatedsignals. National Instrument LabVIEW NXG is used to build the modulation transceivers at۱۰۰ GHz, a ۶G 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 ۹۸.۶۸% and ۹۶.۰۵% using ResNet۱۸ and ResNet۱۰۱, respectively. Furthermore, these models can classify the modulatedsignals at lower SNRs. These innovative models are suitable and effective to utilize for ۶G wireless communication networks.

کلیدواژه ها:

Automatic Modulation Classification ، deep learning ، Carrier frequency offset ، phase noise