Deep Learning Based Radio-Signal Identification With Hardware Design

被引：51

作者：

Mendis, Gihan Janith ^{[1
]}

Wei-Kocsis, Jin ^{[1
]}

Madanayake, Arjuna ^{[2
]}

机构：

[1] Univ Akron, Akron, OH 44325 USA

[2] Florida Int Univ, Miami, FL 33199 USA

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2019年 / 55卷 / 05期

基金：

美国国家航空航天局;

关键词：

RF signals; Deep learning; Modulation; Feature extraction; Training; Hardware; Field programmable gate arrays; Automated modulation classification (AMC); deep learning; low-complexity deep belief network; microunmanned aerial system (UAS); radio signals classification; COGNITIVE RADIO; FRAMEWORK;

D O I：

10.1109/TAES.2019.2891155

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

This paper proposes a deep learning based intelligent method for detecting and identifying radio signals considering two applications: first, cognitive radar for identifying micro unmanned aerial systems and second, an automated modulation classification scheme for cognitive radio, which can be used for aeronautical communication systems. Our proposed intelligent method is designed of a spectral correlation function based feature extractor and a low-complexity deep belief network classifier with low FPGA logic utilization.

引用

页码：2516 / 2531

页数：16

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