Title :
Wavelet-based compressed spectrum sensing for cognitive radio wireless networks
Author :
Egilmez, Hilmi E. ; Ortega, Antonio
Author_Institution :
Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
Spectrum sensing is an essential functionality of cognitive radio wireless networks (CRWNs) that enables detecting unused frequency sub-bands for dynamic spectrum access. This paper proposes a compressed spectrum sensing framework by (i) constructing a sparsity basis in wavelet domain that helps compressed sensing at sub-Nyquist rates and (ii) applying a wavelet-based singularity detector on the reconstructed signal to identify available frequency sub-bands with low complexity. In particular, for the compressed sensing, an optimized Haar wavelet basis is employed to sparsely represent piecewise constant (PWC) signals which closely approximates the frequency spectrum of a sensed signal. Our simulation results show that our proposed framework outperforms existing compressed spectrum sensing methods by providing higher accuracy at lower sampling rates.
Keywords :
Haar transforms; cognitive radio; compressed sensing; piecewise constant techniques; radio spectrum management; signal detection; signal reconstruction; signal sampling; wavelet transforms; CRWN; PWC signal reconstruction; Wavelet-based compressed spectrum sensing; cognitive radio wireless network; dynamic spectrum access; optimized Haar wavelet; piecewise constant signal reconstruction; signal sampling rate; sub-Nyquist rate; unused frequency sub-bands detection; wavelet-based singularity detector; Cognitive radio; Compressed sensing; Sensors; Sparse matrices; Training; Wavelet packets; Compressed sensing; cognitive radio; dynamic spectrum access; spectrum sensing; wavelet transform;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
DOI :
10.1109/ICASSP.2015.7178553