Data Augmentation Aided Automatic Modulation Recognition Using Diffusion Model

Abstract

Automatic modulation recognition techniques enable fast spectrum access. In recent years, deep learning methods have received much attention in the field of modulation recognition. However, its performance requires a large number of data samples as support. Data augmentation methods are regarded as one of the effective ways to solve the insufficient data samples. To this end, this paper proposes a data augmentation algorithm based on the conditional diffusion model to solve the problem that the model cannot be adequately trained in the case of insufficient data. In the proposed algorithm, the noise observation model acquires the noise in the input signal at different time steps. Then, the input is continuously denoised by removing the noise observed by the noise observation model during the inverse process of the conditional diffusion model to generate the corresponding modulated signal. We put the modulation modes with high confidence in the generated signals into the training set for data augmentation. Experimental results show that the data augmentation algorithm based on the conditional diffusion model proposed in this paper can effectively improve the classification performance of the model.