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Fundamental Limitations on Pilot-based Spectrum Sensing at Very Low SNR

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Abstract

Spectrum sensing is one of the most challenging issues of Cognitive Radio communications. The possibility of extremely low signal-to-noise ratio (SNR) of the received signal poses a fundamental challenge to spectrum sensing. In this paper, pilot-based spectrum sensing for OFDM signals is investigated. It is shown that the existing pilot-based OFDM spectrum sensing algorithms suffer from the frequency offset between the transmitter and sensing devices, as well as the noise uncertainty in the sensing threshold design. We consequently propose a robust pilot-based spectrum sensing algorithm for low SNR OFDM signals using a sliding frequency correlator. The proposed algorithm processes additional bandwidth to eliminate the impact of frequency offset. In addition, considering the unknown noise statistics and its time-varying nature, a ratio threshold which is not sensitive to the noise power level is derived for spectrum sensing. Our theoretical analysis and simulation results show that this algorithm can achieve exceptionally good sensing performance at very low SNR, while being insensitive to time and frequency offsets and requiring no information of the noise statistics.

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References

  1. Mitola, J. (2000). Cognitive radio an integrated agent architecture for software defined radio. Ph.D. dissertation. Stockholm: KTH Royal Institute of Technology.

  2. Yucek T., Arslan H. (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials 11(1): 116–130

    Article  Google Scholar 

  3. Cabric, D. (2007). Cognitive radios: System design perspective. Ph.D’s thesis. Berkeley: University of California.

  4. Cabric, D., Mishra, S., & Brodersen, R. (2004). Implementation issues in spectrum sensing for cognitive radios. Conference record of the thirty-eighth asilomar conference on signals, systems and computers (ACSSC’04) (pp. 772–776).

  5. Alemseged, Y. D., & Harada, H. (2008). Spectrum sensing for cognitive radio. IEEE radio and wireless symposium (RAWCON’08) (pp. 356–359).

  6. Ghasemi A., Sousa E. (2008) Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs. IEEE Communications Magazine 46(4): 32–39

    Article  Google Scholar 

  7. Chen R., Park J., Reed J. (2008) Defense against primary user emulation attacks in cognitive radio networks. IEEE Journal of Selected Areas in Communications 26(1): 25–37

    Article  Google Scholar 

  8. Quan Z., Cui S., Sayed A., Poor H. (2009) Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Transactions on Signal Processing 57(3): 1128–1140

    Article  Google Scholar 

  9. Han, Z., & Jiang, H. (2008). Replacement of spectrum sensing and avoidance of hidden terminal for cognitive radio. IEEE Wireless Communications and Networking Conference (WCNC’08) (pp. 1448–1452).

  10. Advanced Television Systems Committee. (2004). ATSC recommended practice: Receiver performance guidelines.

  11. Chouinard, G., Cabric, D., & Ghosh, M. (2006). Sensing thresholds, IEEE 802.22 (doc. 802.22-06/005/r3).

  12. FCC. (2004). FCC OET Bulletin No. 69: Longley-Rice methodology for Evaluating TV Coverage and Interference.

  13. Fehske, A., Gaeddert, J., & Reed, J. (2005). A new approach to signal classification using spectral correlation and neural networks. In Proceedings of first IEEE international symposium on new frontiers in Dynamic Spectrum Access Networks (DySPAN’05) (Vol. 1, pp. 144–150).

  14. Sutton P., Nolan K., Doyle L. (2008) Cyclostationary signatures in practical cognitive radio applications. IEEE Journal of Selected Areas in Communications 26(1): 13–24

    Article  Google Scholar 

  15. Tewfik, N., & Acoustics, A. (2008). Sequential pilotsensing of ATSC signals in IEEE 802.22 cognitive radio networks Kundargi. IEEE international conference on speech and signal processing (ICASSP’08) (pp. 2789–2792).

  16. Carlos, C., Ghosh, M., Dave, C., & Kiran, C. (2007). Spectrum sensing for dynamic spectrum access of TV bands. International conference on cognitive radio oriented wireless networks and communications (CrownCom’08) (pp. 225–233).

  17. Ghosh, M. (2007). Text on FFT-based pilot sensing, IEEE 802.22 (doc. 22-07-0298-01-0000).

  18. Goldsmith A. (2005) Wireless Communications. Cambridge University Press, Cambridge

    Google Scholar 

  19. Chen, H., Gao, W., & Daut, D. (2008). Spectrum sensing for OFDM systems employing pilot tones and application to DVB-T OFDM. IEEE ICC’08 (pp. 3421–3426).

  20. Socheleau, F., Ciblat, P., & Houcke, S. (2009). OFDM system identification for cognitive radio based on pilot-induced cyclostationarity. In Proceedings of IEEE wireless communications and networking conference (WCNC’09).

  21. Tu, S., Chen, K., & Prasad, R. (2007). Spectrum sensing of OFDMA system for cognitive radios. IEEE international symposium on personal, indoor and mobile radio communications (PIMRC’07) (pp. 1–5).

  22. Wang, X., Chen, H., Wu, Y., Chouinard, J., & Wang, C. (2009). Identification of PCP-OFDM signals at very low SNR for spectrum efficient communications. IEEE VTC’09 Spring.

  23. Wang, C., Wang, X., Li, H., & Ho, P. (2009). Multi-window spectrum sensing of unsynchronized OFDM signal at very low SNR. IEEE Goblal Telecommunications Conference (pp. 1–5).

  24. Shent, B., Huang, L., Zhao, C., Zhou, Z., & Kwak, K. (2008). Energy Detection based spectrum sensing for cognitive radios in noise of uncertain power. International symposium on communications and information technologies (ISCIT’07) (pp. 628–633).

  25. Chen, D., Li, J., & Ma, J. (2008). Cooperative spectrum sensing under noise uncertainty in cognitive radio. International conference on wireless communications, networking and mobile computing (WiCOM’08) (pp. 1–4).

  26. ETSI. (2000). Digital video broadcasting: Framing structure, channel coding, and modulation for digital terrestrial television. European Telecommunication Standard EN 300 744.

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Correspondence to Xianbin Wang.

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Part of the material was presented at IEEE GLOBECOM 2009.

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Wang, C., Wang, X., Li, H. et al. Fundamental Limitations on Pilot-based Spectrum Sensing at Very Low SNR. Wireless Pers Commun 66, 751–770 (2012). https://doi.org/10.1007/s11277-011-0362-z

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