A Robust Spectrum Sensing Method Using Filter Bank

Dong Feng Zhao; Xiao Ma; Xian Wei Zhou

doi:10.4028/www.scientific.net/AMM.58-60.54

Paper Titles

Cooperative Pricing of Apparel Supply Chain Based on API
p.28

Control System Development of Paper and Yarn Compounding Bag-Making Machine
p.36

Application of Technical Objects Relation Model in Manufacturing Enterprise
p.42

The Research of High Speed Machining Technology for Sub-Regional Complex Surfaces Based on Feature
p.48

A Robust Spectrum Sensing Method Using Filter Bank
p.54

Technological Competency and New Product Development Performance: The Moderating Role of Environmental Turbulence
p.60

An Investigation of Innovation Capability in Small and Medium-Sized Enterprises of China
p.66

Study on the Topology Structure and Evolvement Analysis of Nonlinear Model under Superharmonic Resonance
p.73

Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine
p.79

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60A Robust Spectrum Sensing Method Using Filter Bank

A Robust Spectrum Sensing Method Using Filter Bank

Abstract:

Robust spectrum sensing is one of the essential issues for building and commercializing the cognitive radio system. With the challenging wireless environment, a reliable and computation feasible real-time spectrum sensing method using filter bank has been proposed in this letter. Extended Gaussian Function and Fast Fourier Transform are effectively combined together to implement a non-parameter and thus robust spectrum sensing. Numerical results are provided to compare the performance of the proposed method with that of the existing spectrum sensing.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

54-59

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.54

Citation:

Cite this paper

Online since:

June 2011

Authors:

Dong Feng Zhao, Xiao Ma, Xian Wei Zhou

Keywords:

Cognitive Radio (CR), Filter Bank, Spectrum Sensing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] S Haykin. Cognitive Radio: Brain-Empowered Wireless Communications: IEEE Journal on Selected Areas in Communications, Vol. 23(2005), pp.201-220.

DOI: 10.1109/jsac.2004.839380

Google Scholar

[2] T Yucek, H Arslan. A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications: IEEE Communications Surveys & Tutorials, Vol. 11(2009), pp.116-130.

DOI: 10.1109/surv.2009.090109

Google Scholar

[3] H Urkowitz. Energy Detection of Unknown Deterministic Signals: Proceeding of IEEE, Vol. 55(1967), pp.523-531.

DOI: 10.1109/proc.1967.5573

Google Scholar

[4] F F Digham, M S Alouini, M K Simon. On the Energy Detection of Unknown Signals over Fading Channels: IEEE International Conference on Communications, Vol. 5(2003), pp.3575-3579.

DOI: 10.1109/icc.2003.1204119

Google Scholar

[5] S Enserink, D Cochran. A Cyclostationary Feature Detector: Asilomar Conference on Signals, Systems and Computers, Vol. 2(1994), p.806–810.

DOI: 10.1109/acssc.1994.471573

Google Scholar

[6] L Jarmo, A K Saleem, K Visa. Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio: IEEE Transaction on Signal Processing, Vol. 58(2010), pp.38-52.

DOI: 10.1109/tsp.2009.2029790

Google Scholar

[7] S Haykin, D J Thomson, J H Reed. Spectrum Sensing for Cognitive Radio: Proceeding of IEEE, Vol. 97(2009), pp.849-877.

DOI: 10.1109/jproc.2009.2015711

Google Scholar

[8] M Alard, C Roche, P Siohan. A New Family of Function with a Nearly Optimaltime-Frequency Localization: Technical Report of the RNRT Project Modyr, (1999).

Google Scholar