Modulation Classification
In this paper, we design and present a testbed implementation and the resulting dataset for modulation recognition from real-world imperfect scans. We describe our efforts to build a testbed of heterogeneous spectrum sensors (low-cost RTL-SDR and mid-cost USRP) and a controlled transmitter in order to facilitate real-world data collection for modulation recognition from partial and biased scans.
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This dataset has 11 sets of feature data extracted with different signal-to-noise and a set of simulation results of modulation classification. Feature dataset consists of 2 novel features and 15 classic features, and dataset of simulation results represents the effectiveness of FS_DT-SSVM classifier.
These data match the experimental data of the paper “Automatic Modulation Classification Based on Novel Feature Extraction Algorithms”.
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In order to increase the diversity in signal datasets, we create a new dataset called HisarMod, which includes 26 classes and 5 different modulation families passing through 5 different wireless communication channel. During the generation of the dataset, MATLAB 2017a is employed for creating random bit sequences, symbols, and wireless fading channels.
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