Radio access technology classification for cognitive radio networks | IEEE Conference Publication | IEEE Xplore

Radio access technology classification for cognitive radio networks


Abstract:

In spectrum bands where spectrum sharing is allowed by national regulators, radio access technology recognition is an important technique for reducing interference and fa...Show More

Abstract:

In spectrum bands where spectrum sharing is allowed by national regulators, radio access technology recognition is an important technique for reducing interference and facilitating cooperation among cognitive radios. Unlicensed users (secondaries) need to be able to differentiate between transmissions of licensed users (primaries) and other unlicensed users. Furthermore, secondaries should only free a band when the licensed primary user starts to transmit. In this regard, secondary users' transmission technology classification will have a vital role for coexistence/cooperation purposes in such shared spectrum bands. For the purpose of this work, a practical testbed made up of software defined radio transceivers and a set of computing units was put together. A classification neural network was trained in a supervised learning method. Testbed results demonstrate the efficiency of the classification in differentiating among different radio access transmissions.
Date of Conference: 08-11 September 2013
Date Added to IEEE Xplore: 25 November 2013
Electronic ISBN:978-1-4673-6235-1

ISSN Information:

Conference Location: London, UK
References is not available for this document.

I. Introduction

The huge demand to access information wirelessly and ubiquitously in the past decade in conjunction with the scarcity of radio resources [1] has resulted in intensive research towards the concept cognitive radio networks (CRNs) [2]. Nonetheless, only a small subset of published works have found their way into implementation and standardization. This is due to many factors; among them are the ambiguity of the cognitive radio concept for which many definitions exist, and the lack of complete implementable solutions.

Select All
1.
FCC, Spectrum Policy Task Force Report, ET Docket No. 02-155, Nov 02, 2002.
2.
J. Mitola, "Cognitive radio: an integrated agent architecture for software defined radio," Ph.D. dissertation, KTH, 2000.
3.
Palicot, Jacques, and Christian Roland. "A new concept for wireless reconfigurable receivers." Communications Magazine, IEEE 41.7 (2003): 124-132.
4.
Di Benedetto, M., et al. "Automatic network recognition by feature extraction: a case study in the ISM band." Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), 2010 Proceedings of the Fifth International Conference on. IEEE, 2010.
5.
http://www.itu.int/net/pressoffice/press-releases/2012/10.aspx
6.
M.F. Moller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, 6, pp.525-533, 1993.
7.
Beale, M. Hagan, and H. Demuth, "Matlab neural network toolbox user's guide," The Math Works Inc., 2010, http://www.mathworks.com/help/ pdfdoc/nnet/nnet ug.pdf.
8.
C. M. Bishop. Pattern Recognition and Machine Learning. Springer, New York, NY, USA, first edition, 2006.
9.
M. Z. Win, P. C. Pinto, and L. a. Shepp, "A Mathematical Theory of Network Interference and Its Applications," Proceedings of the IEEE, vol. 97, no. 2, pp. 205-230, Feb. 2009.
10.
T. C. Clancy, A. Khawar, and T. R. Newman, "Robust Signal Classification using Unsupervised Learning," IEEE Transactions On Wireless Communications, 2011.
11.
T. Marwala, Finite-element-model updating using computational intelligence, Springer, 2010.

References

References is not available for this document.