NDLI: Likelihood function-based modulation classification in bandwidth-constrained sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Xu, J.L. Wei Su MengChu Zhou
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA (Xu, J.L.; MengChu Zhou) \|\| US Army RDECOM, Communications-Electronics RD&E Center, Fort Monmouth, NJ, 07703, USA (Wei Su)
Abstract	Automatic modulation classification with a single receiver has been intensively studied for two decades. Enhancing the successful classification probability is a bottleneck in this research field especially with weak signals in a non-cooperative communication environment. A sensor network with distributed classification techniques is expected to achieve technology breakthrough in providing spatial diversity and increasing the classification reliability. In this paper, we developed a distributed likelihood function-based classification method and extend the automatic modulation classification to sensor or radio networks. The classification methods performed in the sensors and primary node associated with theoretical discussion and numerical results are presented.
Starting Page	530
Ending Page	533
File Size	501704
Page Count	4
File Format	PDF
ISBN	9781424464500
e-ISBN	9781424464531
DOI	10.1109/ICNSC.2010.5461606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Telecommunication network reliability Cognitive radio Intensity modulation Bayesian methods Digital modulation Random variables Amplitude estimation Radio network Software testing Wireless sensor networks wireless communication Modulation classification modulation recognition likelihood ratio test sensor networks cognitive radio software-defined radio distributed classification
Content Type	Text
Resource Type	Article

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