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ABSTRACT

Spectrum sensing is an essential function for cognitive radio systems. Based on the observation that signal samples usually are correlated due to various reasons, such as oversampling, multipath propagation and correlation among raw signals, a correlation-based detection method is proposed to differentiate signals from noise in this paper. Sensing performance of the proposed method is analyzed theoretically when primary signals are fully correlated. Its complexity is compared with that of energy detection and threshold setting is discussed under an estimated false alarm probability. However, the algorithm is inferior to energy detection in sensing performance when signal samples are uncorrelated. To overcome this disadvantage, an adaptive detection model is developed. Simulations based on captured ATSC DTV signals and analog PAL TV signals are presented to verify the performance of the proposed method.

REFERENCES

• 1: FCC. FCC report of the spectrum efficiency working group. USA: FCC, 2002.
• 2: Haykin S. Cognitive radio: Brain-empowered wireless communications. IEEE J. Select. Areas Commun., Feb. 2005, vol. 23, pp. 201--220.
• 3: Mitola J. Cognitive radio: making software radios more personal. IEEE Personal Communications, 1999, vol. 6, pp. 13--18.
• 4: Ian F. Akyildiz , Won-Yeol Lee , Mehmet C. Vuran , Shantidev Mohanty, NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.50 n.13, p.2127-2159, 15 September 2006 [doi>10.1016/j.comnet.2006.05.001]
• 5: Cabric D, Tkachenko A and Brodersen RW. Spectrum sensing measurements of pilot, energy, and collaborative detection. In Proc. of IEEE MILCOM 2006, Washington, DC, USA, pp. 1--7, Oct. 2006.
• 6: Sahai A, Hoven N, Tandra R. Some fundamental limits on cognitive radio. In Proc. of 42nd Allerton Conf. Communication, Control and Computing, 2004, Monticello,IL, USA, pp. 1--10, Sep. 2004.
• 7: Kyperountas S, Correal N, Shi Q, et al. Performance analysis of cooperative spectrum sensing in Suzuki fading channels. In Proc. of IEEE CrowCom 2007, Orlando, FL, USA, pp. 428--432, Jul. 2007.
• 8: Kostylev VI. Energy detection of a signal with random amplitude. In Proc. of IEEE ICC 2002, New York ,USA, pp. 1606--1610, Apr. 2008.
• 9: Dingham FF, Alouini MS, Simon MK. On the energy detection of unknown signals over fading channels. In Proc. of IEEE ICC 2003, Hongkong, China, pp. 3575--3579, May 2003.
• 10: Qingchun Ren , Qilian Liang, Performance Analysis of Energy Detection for Cognitive RadioWireless Networks, Proceedings of the International Conference on Wireless Algorithms,Systems and Applications, p.139-146, August 01-03, 2007 [doi>10.1109/WASA.2007.80]
• 11: Zeng Y., Liang Y. Covariance based signal detections for cognitive radio. In Proc. of IEEE Dyspan 2007, Dublin,Ireland, pp. 202--207, Apr. 2007.
• 12: Santosh V. Nagaraj, Entropy-based spectrum sensing in cognitive radio, Signal Processing, v.89 n.2, p.174-180, February, 2009 [doi>10.1016/j.sigpro.2008.07.022]
• 13: Sharma R K, Wallace J W. Improved spectrum sensing by utilizing signal autocorrelation. In Proc. of IEEE VTC Spring 2009, Barcelona, Spain, pp. 1--5, Apr. 2009.
• 14: Steven M. Kay, Fundamentals of statistical signal processing: estimation theory, Prentice-Hall, Inc., Upper Saddle River, NJ, 1993

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.1 Network Architecture and Design
          Subjects: Wireless communication

General Terms:
Design, Performance

Keywords:
cognitive radio, energy detection, signal correlation, spectrum sensing