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A new approach to signal classification using spectral correlation and neural networks

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3 Author(s)
Fehske, A. ; Mobile & Portable Radio Res. Group, Virginia Polytech. Inst. & State Univ., Blacksbury, VA ; Gaeddert, J. ; Reed, J.

Channel sensing and spectrum allocation has long been of interest as a prospective addition to cognitive radios for wireless communications systems occupying license-free bands. Conventional approaches to cyclic spectral analysis have been proposed as a method for classifying signals for applications where the carrier frequency and bandwidths are unknown, but is, however, computationally complex and requires a significant amount of observation time for adequate performance. Neural networks have been used for signal classification, but only for situations where the baseband signal is present. By combining these techniques a more efficient and reliable classifier can be developed where a significant amount of processing is performed offline, thus reducing online computation. In this paper we take a renewed look at signal classification using spectral coherence and neural networks, the performance of which is characterized by Monte Carlo simulations

Published in:

New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on

Date of Conference:

8-11 Nov. 2005