Wideband spectrum sensing is an essential functionality for cognitive radio networks. It enables cognitive radios to detect spectral holes over a wideband channel and to opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. However, most of the work on wideband spectrum sensing presented in the literature employ the Nyquist sampling which requires very high sampling rates and acquisition costs. In this paper, a new wideband spectrum sensing algorithm based on compressed sensing theory is presented. The proposed method gives an effective sparse signal representation method for the wideband spectrum sensing problem. Thus, the presented method can effectively detect all spectral holes by finding the sparse coefficients. At the same time, the signal sampling rate and acquisition costs can be substantially reduced by using the compressive sampling technique. Simulation results testify the effectiveness of the proposed approach even in low signal-to-noise (SNR) cases.
2. Mitola, , J. , G. Q. Maguire, and , "Cognitive radio: Making software radios more personal," IEEE Personal Communications, Vol. 6, No. 4, 13-18, 1999. doi:10.1002/wcm.732
3. Haykin, S., , "Dynamic spectrum management for cognitive radio: An overview," Wireless Communications and Mobile Computing, Vol. 9, No. 11, 1447-1459, , 2009..
4. Sahai, , A. , D. Cabric, and , "A tutorial on spectrum sensing: Fundamental limits and practical challenges," IEEE DySPAN , 2005. doi:10.1109/TSP.2008.2008540
5. Quan, Z., , S. Cui, A. H. Sayed, and H. V. Poor, , "Optimal multiband joint detection for spectrum-sensing in cognitive radio networks," IEEE Transactions on Signal Processing, Vol. 57, No. 3, 1128-1140, 2009..
6. Paysarvi-Hoseini, P. , N. C. Beaulieu, and , "On the e±cient implementation of the multiband joint detection framework for wideband spectrum sensing in cognitive radio networks," IEEE Vehicular Technology Conference, , 1-6, 2011. doi:10.1109/TSP.2010.2096220
7. Paysarvi-Hoseini, , P. and N. C. Beaulieu, "Optimal wideband spectrum sensing framework for cognitive radio systems," IEEE Transactions on Signal Processing, Vol. 59, No. 3, 1170-1182, 2011. doi:10.1109/TIT.2006.871582
8. Donoho, , D., , "Compressed sensing," IEEE Transactions on Information Theory, Vol. 52, No. 4, 1289-1306, , 2006.. doi:10.1109/MSP.2007.914731
9. Candes, , E. J. , M. B. Wakin, and , "An introduction to compressive sampling," IEEE Signal Processing Magazine, Vol. 25, No. 2, 21-30, 2008.
10. Tian, , Z. , G. B. Giannakis, and , "Compressed sensing for wideband cognitive radios," IEEE ICASSP, Vol. 4, 1357-1360, , 2007.. doi:10.1155/2010/730509
11. Yu, , Z. Z., , X. Chen, S. Hoyos, B. M.Sadler, M. J. X. Gong, and C. L. Qian, "Mixed-signal parallel compressive spectrum sensing for cognitive radios ," International Journal of Digital Multimedia Broadcasting, Vol. 2010, 1-10, 2010.. doi:10.1109/JSTSP.2010.2055037
12. Zeng, , F. Z., , C. Li, and Z. Tian, "Distributed compressive spectrum sensing in cooperative multihop cognitive networks," IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 1, 37-48, , 2011. doi:10.1109/TWC.2011.071411.101929
13. Zhang, , Z. H., , Z. Han, H. S. Li, D. P. Yang, and C. X. Pei, , "Belief propagation based cooperative compressed spectrum sensing in wideband cognitive radio networks," IEEE Transactions on Wireless Communications, Vol. 10, No. 9, 3020-3031, , 2011.. doi:10.1016/j.sigpro.2009.08.013
14. Liu, , F. L., J. K. Wang, and R. Y. Du, "Unitary-JAFE algorithm for joint angle-frequency estimation based on Frame-Newton method," Signal Processing,, Vol. 3, No. 90, 809-820, 2010. doi:10.1109/IWSOC.2006.348224
15. Kirolos, S., , T. Ragheb, J. Laska, M. E. Duarte, Y. Massoud and R. G. Baraniuk, "Practical issues in implementing analog-to-information converters," International Workshop on System on Chip for Real Time Applications, , 141-146, 2006.
16. Liu, , F. L., , J. K. Wang, R. Y. Du, L. Peng, and P. P. Chen, "A second-order cone programming approach for robust downlink beamforming with power control in cognitive radio networks," Progress In Electromagnetics Research M, Vol. 18, 221-231, 2011.
18. Malioutov, , D. M., , M. Cetin, and A. S. Willsky, "A sparse signal reconstruction perspective for source localization with sensor arrays," IEEE Transactions on Signal Processing, Vol. 53, No. 8, 3010-3022, 2005.
19. Zeng, , Y. H., Y. C. Liang, and , "Maximum-minimum eigenvalue de-tection for cognitive radio," Proceedings of the 18th International Symposium on Personal, Indoor and Mobile Radio Communications,, 2.