Data augmentation with conditional GAN for automatic modulation classification
Mansi Patel, Xuyu Wang, Shiwen Mao
Abstract
Deep learning has great potential for automatic modulation classification (AMC). However, its performance largely hinges upon the availability of sufficient high-quality labeled data. In this paper, we propose data augmentation with conditional generative adversarial network (CGAN) for convolutional neural network (CNN) based AMC, which provides an effective solution to the limited data problem. We present the design of the proposed CGAN based data augmentation method, and validate its performance with a public dataset. The experiment results show that CNN-based modulation classification can greatly benefit from the proposed data augmentation approach with greatly improved accuracy.
Topics & Concepts
Computer scienceConvolutional neural networkGenerative adversarial networkArtificial intelligenceModulation (music)Deep learningHinge lossLabeled dataPattern recognition (psychology)Generative grammarMachine learningData miningSupport vector machinePhilosophyAestheticsWireless Signal Modulation ClassificationDigital Media Forensic DetectionAdvanced biosensing and bioanalysis techniques