Document Type
Article
Publication Date
9-7-2009
Department
Department of Electrical and Computer Engineering
School or Division
Graduate School of Engineering and Management
Digital Object Identifier
Source Publication
EURASIP Journal on Wireless Communications and Networking (ISSN 1687-1472 | e-ISSN 1687-1499)
Abstract
Cognitive Radio (CR), a hierarchical Dynamic Spectrum Access (DSA) model, has been considered as a strong candidate for future communication systems improving spectrum efficiency utilizing unused spectrum of opportunity. However, to ensure the effectiveness of dynamic spectrum access, accurate signal classification in fading channels at low signal to noise ratio is essential. In this paper, a hierarchical cyclostationary-based classifier is proposed to reliably identify the signal type of a wide range of unknown signals. The proposed system assumes no a priori knowledge of critical signal statistics such as carrier frequency, carrier phase, or symbol rate. The system is designed with a multistage approach to minimize the number of samples required to make a classification decision while simultaneously ensuring the greatest reliability in the current and previous stages. The system performance is demonstrated in a variety of multipath fading channels, where several multiantenna-based combining schemes are implemented to exploit spatial diversity.
Recommended Citation
Like, E., Chakravarthy, V., Ratazzi, P. et al. Signal Classification in Fading Channels Using Cyclic Spectral Analysis. J Wireless Com Network 2009, 879812 (2009). https://doi.org/10.1155/2009/879812
Comments
This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Author Eric Like was an AFIT PhD candidate at the time of this article. (AFIT-DEE-ENG-10-04, March 2010)
Funding notes: This paper is based upon work supported by the Dayton Area Graduate Studies Institute (DAGSI), National Science Foundation under Grants no. 0708469, no. 0737297, no. 0837677, the Wright Center for Sensor System Engineering, and the Air Force Research Laboratory.