Automatic Modulation Classification of Frequency-Hopping Signals Using High-Dimensional Phase Diagrams
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB
Volume
2023-June
DOI
10.1109/BMSB58369.2023.10211277
Abstract
Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.
Funding Number
2020GXNSFAA159146
Funding Sponsor
National Natural Science Foundation of China
Keywords
Automatic modulation classification, embedded phase diagram, frequency-hopping (FH) signals, phase-space features, support vector machine (SVM), tensor regression
Department
Applied Data Science
Recommended Citation
Qibo Chen, Kun Yan, Hsiao Chun Wu, Shih Yu Chang, Xiao Yan, Yiyan Wu, and Haonan Chang. "Automatic Modulation Classification of Frequency-Hopping Signals Using High-Dimensional Phase Diagrams" IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB (2023). https://doi.org/10.1109/BMSB58369.2023.10211277
