CFGAN: A Conditional Filter Generative Adversarial Network for Signal Modulation Mode Recognition

Currently, in the application scenario of generative adversarial networks, determining how to improve the quality of the generated signals and ensure the modulation recognition accuracy of convolutional neural networks are important problems. In this paper, a generative sample quality screening method for the problem of low-quality samples generated by generative adversarial networks under few-shot conditions has been proposed, which innovatively establishes a sample expansion mode without fixing the network parameters, realizes the learning of the real data distribution by constantly updating the network weights, and enhances the quality of the expanded samples by adopting the quality screening method with two quality screenings. A generative adversarial network has been designed for this method, which reduces the time investment required for generating samples by extracting different features of few-shots of signals. The experimental results show the few-shot conditions, under the signal-to-noise ratio of −8∼12 dB and three expansion ratios of 1:1, 1:2 and 1:3. Compared with the general method expansion, the average modulation mode recognition accuracy of the QCO-CFGAN method expanded with the quality screening method is improved by 2.65%, 2.46% and 2.73%, respectively, which proves the effectiveness under this condition.