Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring

Abstract

Dramatic advances in wireless networks have led to the Smart IoT, which may enable new modes of information transfer. Generally, data-driven advanced artificial intelligence (AI) techniques can be used in smart IoT networks, which prones to a lot of unexpected challenges. Enabling efficient management of spectrum resources based on cognitive radio is considered to be an effective means to address the limited spectrum resources. As a key technology in CR, automatic modulation recognition (AMR) is developing towards intelligence with deep learning as the main approach. Deep learning (DL) has been widely applied to AMR for possible improvement of recognition accuracy, while the superb performance highly depends on high-quality and well-labeled datasets. Consequently, these requirements prone to poor performance in the environments where the datasets are not well-labeled. Motivated by this, domain adaptation is considered for AMR in this paper, and a novel network architecture is proposed therein, termed semi-supervised automatic modulation recognition (SemiAMR). Specifically, source domain high SNR data are mapped by the source encoder to the classification domain and classified by the classifier, where the source data with labels enable this training process. Next, the discriminator and the target domain encoder are trained by determining whether the data is from the target or source domain. Finally, the target domain encoder and classifier are combined and are used to infer the data labels of the target domain. Experimental results state that the accuracy of proposed SemiAMR achieves 1\% to 27\% improvements when compared with classical schemes under the target domain condition where there is no corresponding labeled data and signal-to-noise ratios (SNRs) varies from −20 dB to −4 dB.