An imbalanced signal modulation classification and evaluation method based on synthetic minority over-sampling technique

Imbalanced signal modulation classification is a challenging problem in wireless communication. In this paper, we propose an overall framework from data generation to generated data qualitative analysis. It generates data based on Synthetic Minority Over-Sampling Technique (SMOTE) and evaluates the generated data in terms of amplitude, distribution, and classification validity, namely SMOTE-ADC. The framework aims to generate synthetic data for the minority class to rebalance the dataset and improve the classification performance. SMOTE-ADC creates and defines a matrix of distances between eigenvalues by considering the eigenvalues and their relationships. By comparing the variability of neighboring feature vectors and defining the weights of the distance matrix to make the generated data more close to the real data. For the evaluation of generated data in SMOTE-ADC, we propose three methods to analyze the effect of generated data, comparing the amplitude, distribution, and classification effectiveness of generated data and original data, respectively. Experimental results demonstrate that our approach effectively mitigates the impact of data imbalance and enhances the accuracy of signal modulation classification.