Application and comparison of active and transfer learning approaches for modulation format classification in visible light communication systems
Zhenquan Zhao, Faisal Nadeem Khan, Yibin Li, Zhaoming Wang, Yuan Zhang, H. Y. Fu
Abstract
Automatic modulation classification (AMC) is a crucial part of adaptive modulation schemes for visible light communication (VLC) systems. However, most of the deep learning (DL) based AMC methods for VLC systems require a large amount of labeled training data which is quite difficult to obtain in practical systems. In this work, we introduce active learning (AL) and transfer learning (TL) approaches for AMC in VLC systems and experimentally analyze their performances. Experimental results show that the proposed novel AlexNet-AL and AlexNet-TL methods can significantly improve the classification accuracy with small sizes of training data. To be specific, using 60 labeled samples, AlexNet-AL and AlexNet-TL increase the classification accuracy by 6.82% and 14.6% compared to the result without AL and TL, respectively. Moreover, the use of data augmentation (DA) operation along with our proposed methods helps achieve further better performances.