Automatic modulation classification with deep learning‐based frequency selection filters. Issue 21 (1st September 2020)

• Record Type: Journal Article
• Authors: Liu, Weisong; Huang, Zhitao; Li, Xueqiong; Wang, Xiang; Li, Baoguo
• Is Part Of: Electronics letters. Volume 56:Issue 21(2020)
• Page Start: 1144
• Page End: 1145
• Publication Date: 2020-09-01
• Subjects: neural nets -- signal classification -- cognitive radio -- modulation -- learning (artificial intelligence) -- military communication; modulation formats -- received signals -- cognitive radio -- spectrum management -- deep learning techniques -- AMC -- powerful representation -- classification abilities -- frequency selection module -- raw signal data -- in‐band signal‐to‐noise ratio -- automatic modulation classification -- deep learning‐based frequency selection filters -- important task; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1998
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Automatic modulation classification (AMC) is an important and challenging task that aims to discriminate modulation formats of received signals, such as military communications, cognitive radio and spectrum management. With the development of deep learning techniques, research in AMC has gained promising results because of its powerful representation and classification abilities. In this Letter, the authors present a new network architecture that combines a frequency selection module and a convolutional neural network (CNN). This scheme not only processes raw signal data with carriers to increase the in‐band signal‐to‐noise ratio but also converge faster than traditional CNN. Experiments demonstrate the effectiveness and efficiency of the proposed model.