| Abstract: | Deep learning (DL)
requires massive volume of data to train the network. Insufficient training
data will cause serious overfitting
problem and degrade the classification accuracy. In order to solve this
problem, a method for automatic modulation
classification ( AMC) using AlexNet with data augmentation was proposed. Three
data augmentation methods is
considered, i. e. , random erasing, CutMix, and rotation. Firstly, modulated
signals are converted into
constellation representations. And all constellation representations are
divided into training dataset and test dataset. Then,
training dataset are augmented by three methods. Secondly, the optimal value of
execution probability for random
erasing and CutMix are determined. Simulation results show that both of them
perform optimally when execution
probability is 0.5. Thirdly, the performance of three data augmentation methods
are evaluated. Simulation
results demonstrate that all augmentation methods can improve the
classification accuracy. Rotation improves the
classification accuracy by 13.04% when signal noise ratio (SNR) is 2 dB. Among
three methods, rotation
outperforms random erasing and CutMix when SNR is greater than - 6 dB. Finally,
compared with other classification
algorithms, random erasing, CutMix, and rotation used in this paper achieved
the performance significantly
improved. It is worth mentioning that the classification accuracy can reach 90.5%
with SNR at 10 dB. |
