Document Type : Review Article

Authors

1 Aalto University, School of Science and Technology, Department of Micro- and Nanosciences

2 Nokia Research Center, Otaniemi, Espoo

Abstract

Cognitive radios utilize spectrum sensors to provide  information about the surrounding radio environment. This enables cognitive radios to communicate at the same frequency bands with existing (primary) radio systems, and thereby improve the utilization of spectral resources. Furthermore, the spectrum sensor must be able to guarantee that the cognitive radio devices do not interfere with the primary system transmissions. This paper describes a hardware implementation of a spectrum sensor based on cyclostationary feature detector, which has an improved detection performance achieved by decimation of the cyclic spectrum. Decimation also provides a simple way to control detection time and, therefore, allows trading the detection time to better probability of detection and vice versa. Implementation complexity in terms of power consumption and silicon area for a 65 nm CMOS process is evaluated. Measured detection performance is presented and detection of a 802.11g WLAN signal through air interface is demonstrated.

Keywords

[1] J. Mitola III, “Cognitive radio: An integrated agent architecture for software defined radio”, Ph.D. dissertation, Royal Institute of Technology, May 2000.
[2] I. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey”, Computer Networks, Vol. 50, pp. 2127–2159, 2006.
[3] T. Yucek and H. Arslan, “ A survey of spectrum sensing algorithms for cognitive radio applications”, IEEE Commun. Surveys & Tutorials, Vol. 11, pp. 116-130, First Quarter 2009.
[4] S. Haykin, D. Thompson, and J. Reed, “Spectrum sensing for cognitive radio”, Proc. IEEE, Vol. 97, pp. 849-877, May 2009.
[5] R. Tandra and A. Sahai, “SNR walls for signal detection”, IEEE J. Select. Topics Signal Processing, Vol. 2, pp. 4-17, Feb. 2008.
[6] IEEE 802.22 Working Group on Wireless Regional Area Networks, “http://www.ieee802.org/22”.
[7] C. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. Shellhammer, and W. Caldwell, “IEEE 802.22: The first cognitive radio wireless regional area network standard”, IEEE Commun. Mag., Vol. 47, pp. 130-138, Jan. 2009.
[8] A. Sahai, S. Mishra, R. Tandra, and K. Woyach, “Cognitive radios for spectrum sharing”, IEEE Signal Processing Mag., Vol. 26, pp. 140-145, Jan. 2009.
[9] OET, “Evaluation of the performance of prototype TV-band white space devices phase II”, OET rep. FCC/OET 08-TR-1005, Oct. 2008.
[10] A. Dandawate and G. Giannakis, “Statistical tests for presence of cyclostationarity”, IEEE Trans. Signal Processing, Vol. 42, pp. 2355-2369, Sep. 1994.
[11] J. Lunden, V. Koivunen, A. Huttunen, and H. V. Poor, “Spectrum sensing in cognitive radios based on multiple cyclic frequencies”, in Proc. Int. Conf. Cognitive Radio Oriented Wireless Networks and Communications, 2007, pp. 37-43.
[12] A. Tkachenko, A. Cabric, and R. Brodersen, “Cyclostationary feature detector experiments using reconfigurable BEE2”, in Proc. IEEE Int. Symp. New Frontiers in Dynamic Spectrum Access Networks, 2007, pp. 216-219.
[13] M. Öner and F. Jondral, “Air interface identification for software radio systems”, AEU – International Journal of Electronics and Communications, Vol. 61, pp. 104-117, Feb. 2007.
[14] W. A. Gardner, A. Napolitano, and L. Paura, “Cyclostationarity: Half a century of research”, Signal Processing, Vol. 86, pp. 639-697, Apr. 2006.
[15] P.K. Meher, J. Valls, T.-B. Juang, K. Sridharan, and K. Maharatna, “50 years of CORDIC: Algorithms, architectures, and applications”, IEEE Trans. Circuits Syst. I, Vol. 56, pp. 1893-1907, Sep. 2009.
[16] E. Hogenauer, “An economical class of digital filters for decimation and interpolation”, IEEE Trans. Acoust. Speech, Signal Processing, Vol. 29, pp. 155-162, Apr. 1981.
[17] S. He and M. Torkelson, “A new approach to pipeline FFT processor”, in Proc. Int. Symp. Parallel Processing, 1996, pp. 766-770.