Signal Augmentations Oriented to Modulation Recognition in the Realistic Scenarios

The recent years had witnessed a resurgence on neural network. Many hidden layers were stacked hierarchically to learn the high-level representations. Great performances were achieved by the learned representations. However, this kind of learning models were highly dependent on large amounts of signals with label information. In the realistic scenarios, it is very difficult and costly to collect the modulated signals with label information. Given small training samples, the fitting power of deep models were limited. To solve the problems, a new family of signal augmentation strategies, segment-wise generation and signal-wise generation are proposed. The former builds new signal by tuning a single signal, while the latter combines several different modulated signals together to produce new signal. Four kinds of techniques, segment shift in cyclic, segment correlation in random, pairwise signals combination, and multiple signals concatenation are presented. The aim is to simulate the unforeseen disturbances during signal sampling. The recognition performance under the realistic scenarios can be then improved. Multiple comparative studies were performed. The results demonstrated the effectiveness of proposed strategy in comparisons to the classical methods, as well as the deep learning algorithms.