Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems
نویسندگان
چکیده
Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique widely used military and civil communication systems. Due to the lack of prior information about channel parameters overlapping signals MIMO systems, traditional likelihood-based feature-based approaches cannot be applied these scenarios directly. Hence, this paper, resolve problem blind time–frequency analysis method based on windowed short-time Fourier transform was analyze characteristics time-domain modulated signals. Then, extracted are converted into red–green–blue (RGB) spectrogram images, convolutional neural network transfer learning classify types according RGB images. Finally, a decision fusion module fuse results all receiving antennas. Through simulations, we analyzed performance at different signal-to-noise ratios (SNRs); indicate that, for single-input single-output (SISO) network, our proposed scheme can achieve 92.37% 99.12% average accuracy SNRs ?4 10 dB, respectively. For achieves 80.42% 87.92% greatly improves
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ژورنال
عنوان ژورنال: Sensors
سال: 2021
ISSN: ['1424-8220']
DOI: https://doi.org/10.3390/s21010231