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Sequential Methods for Spectrum Sensing

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Handbook of Cognitive Radio

Abstract

Spectrum sensing is widely regarded as a key enabling technology to support dynamic spectrum access (DSA) for cognitive radio (CR). Though in principle spectrum sensing can be viewed as a traditional signal detection problem, the design of spectrum sensing algorithms needs to take into account certain stringent requirements due to the nature of CR systems. Firstly, it is important for spectrum sensing algorithms to be robust to signal models as it is often difficult in practice for secondary users (SUs) to acquire complete or even partial knowledge about primary signals. Secondly, a small detection delay is essential for the spectrum sensing even under a fairly low detection signal-to-noise ratio (SNR) level with low detection error probabilities. This chapter focuses on a particular type of spectrum sensing algorithms, called sequential spectrum sensing algorithms for CR systems. Compared with block-based sensing algorithms, sequential sensing algorithms enable us to make detection decision with minimum delay while still providing certain performance guarantee. We will first illustrate the benefits of sequential detection for a single-band system. We will then discuss how to design quickest sequential scanning algorithms for multiband systems to quickly identify free channels.

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References

  1. Zhang W, Letaief KB (2008) Cooperative spectrum sensing with transmit and relay diversity in cognitive radio networks. IEEE Trans Wirel Commun 7(12):4761–4766

    Article  Google Scholar 

  2. Zhang W, Mallik RK, Letaief KB (2009) Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Trans Wirel Commun 8(12):5761–5766

    Article  Google Scholar 

  3. Duan D, Yang L, Principe JC (2010) Cooperative diversity of spectrum sensing for cognitive radio systems. IEEE Trans Signal Process 58(6):3218–3227

    Article  MathSciNet  Google Scholar 

  4. Quan Z, Cui S, Sayed AH (2007) An optimal strategy for cooperative spectrum sensing in cognitive radio networks. In: Proceedings of the IEEE Global Telecommunications Conference, Washington, DC, pp 2947–2951

    Google Scholar 

  5. Quan Z, Cui S, Sayed AH (2008) Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE J Spec Top Signal Process 2(1):28–40

    Article  Google Scholar 

  6. Zeng Y, Liang Y (2007) Maximum-minimum eigenvalue detection for cognitive radio. In: Proceedings of the IEEE 18th International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), Athens, pp 1–5

    Google Scholar 

  7. Lim TH, Zhang R, Liang Y-C, Zeng H (2008) GLRT-based spectrum sensing for cognitive radio. In: Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM), New Orleans, pp 1–5

    Google Scholar 

  8. Cabric D, Mishra SM, Brodersen RW (2004) Implementation issues in spectrum sensing for cognitive radios. In: Proceedings of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, pp 772–776

    Google Scholar 

  9. Tang H (2005) Some physical layer issues of wide-band cognitive radio systems. In: Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, pp 151–159

    Google Scholar 

  10. Shankar S, Cordeiro C, Challapali K (2005) Spectrum agile radios: utilization and sensing architectures. In: Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, pp 160–169

    Google Scholar 

  11. Lunden J, Huttunen A, Koivunen V, Poor HV (2007) Multiple sensing in cognitive radios based on multiple cyclic frequencies. In: Proceedings of the International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Orlando, pp 1583–1590

    Google Scholar 

  12. Sutton PD, Nolan KE, Doyle LE (2008) Cyclostationary signatures in practical cognitive radio applications. IEEE J Sel Areas Commun 2(1):13–24

    Article  Google Scholar 

  13. Tian Z, Giannakis GB (2006) A wavelet approach to wideband spectrum sensing for cognitive radios. In: Proceedings of the International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, pp 1–5

    Google Scholar 

  14. Ghasemi A, Sousa ES (2005) Collaborative spectrum sensing in cognitive radio networks. In: Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, pp 131–136

    Google Scholar 

  15. Ganesan G, Li YG (2007) Cooperative spectrum sensing in cognitive radio, part I: two user networks. IEEE Trans Wirel Commun 6(6):2204–2213

    Article  Google Scholar 

  16. Ganesan G, Li YG (2007) Cooperative spectrum sensing in cognitive radio, part II: multiuser networks. IEEE Trans Wirel Commun 6(6):2214–2222

    Article  Google Scholar 

  17. Ganesan G, Li YG, Bing B, Li S (2008) Spatiotemporal sensing in cognitive radio networks. IEEE J Sel Areas Commun 28(1):5–12

    Article  Google Scholar 

  18. Mishra SM, Sahai A, Brodersen RW (2006) Cooperative sensing among cognitive radios. In: Proceedings of the IEEE International Conference on Communication, vol 4, Istanbul, pp 1658–1663

    Google Scholar 

  19. Vistotsky E, Kuffner S, Peterson R (2005) On collaborative detection of TV transmissions in support of dynamic spectrum sharing. In: Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, pp 338–345

    Google Scholar 

  20. Unnikrishnan J, Veeravalli VV (2008) Cooperative sensing for primary detection in cognitive radio. IEEE J Spec Top Signal Process 2(1):18–27

    Article  Google Scholar 

  21. Kim J, Andrews JG (2010) Sensitive white space detection with spectral covariance sensing. IEEE Trans Wirel Commun 9:2945–2955

    Article  Google Scholar 

  22. Kim J, Chae C-B, Andrews JG (2011) Cooperative spectral covariance sensing under correlated shadowing. IEEE Trans Wirel Commun 10:3589–3593

    Article  Google Scholar 

  23. Zeng Y, Liang Y (2007) Covariance based signal detections for cognitive radio. In: Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Dublin, pp 202–207

    Google Scholar 

  24. Lund\(\acute{\text{e}}\) n J, Koivunen V, Huttunen A, Poor HV (2007) Spectrum sensing in cognitive radios based on multiple cyclic frequencies. In: Proceedings of the IEEE Cognitive Radio Oriented Wireless Networks and Communications (CrownCom), Orlando, pp 37–43

    Google Scholar 

  25. Quan Z, Cui S, Sayed AH, Poor HV (2008) Wideband spectrum sensing in cognitive radio networks. In: Proceedings of the International Conference on Communications, Beijing, pp 901–906

    Google Scholar 

  26. Kim SJ, Giannakis GB (2009) Rate-optimal and reduced-complexity sequential sensing algorithms for cognitive OFDM radios. In: Proceedings of the Conference on Information Science and System. Johns Hopkins University, Baltimore, pp 141–146

    Google Scholar 

  27. Urkowitz H (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531

    Article  Google Scholar 

  28. Tandra R, Sahai A (2008) SNR walls for signal detection. IEEE J Sel Top Signal Process 2(1):4–17

    Article  Google Scholar 

  29. Kundargi N, Tewfik A (2007) Hierarchical sequential detection in the context of dynamic spectrum access for cognitive radios. In: Proceedings of the IEEE 14th International Conference on Electronics, Circuits and Systems, Marrakech, pp 514–517

    Google Scholar 

  30. Chen B, Park J, Bian K (2006) Robust distributed spectrum sensing in cognitive radio networks. Technical report TR-ECE-06–07, Department of Electrical and Computer Engineering, Virginia Tech

    Google Scholar 

  31. Wald A (1945) Sequential tests of statistical hypothesis. Ann Math Stat 17:117–186

    Article  MATH  Google Scholar 

  32. Wald A, Wolfowitz J (1948) Optimum character of the sequential probability ratio test. Ann Math Stat 19:326–329

    Article  MathSciNet  MATH  Google Scholar 

  33. Kim SJ, Li G, Giannakis GB (2010) Minimum-delay spectrum sensing for multi-band cognitive radios. In: Proceedings of the IEEE GLOBECOM Conference, Miami, pp 1–5

    Google Scholar 

  34. Kim SJ, Giannakis GB (2010) Sequential and cooperative sensing for multichannel cognitive radios. IEEE Trans Signal Process 58(8):4239–4253

    Article  MathSciNet  Google Scholar 

  35. Poor HV, Hadjiliadis O (2008) Quickest detection. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  36. Quan Z, Cui S, Sayed A (2008) Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE J Sel Top Signal Process 2(1):28–40

    Article  Google Scholar 

  37. Liang Y-C, Zeng Y, Peh E, Hoang A (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wirel Commun 7(4):1326–1337

    Article  Google Scholar 

  38. Pollock SM, Golhar D (1985) Efficient recursions for truncation of the SPRT. Technical report No. 85–24, Department of Industrial and Operations Engineering, University of Michigan

    Google Scholar 

  39. Johnson NL (1961) Sequential analysis: a survey. J R Stat Soc Ser A (Gen) 3:372–411

    Article  Google Scholar 

  40. Poor HV (1994) An introduction to signal detection and estimation. Springer, New York

    Book  MATH  Google Scholar 

  41. Wald A (1947) Sequential analysis. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  42. Kay SM (1998) Fundamentals of statistical signal processing. Volume 2: Detection theory. Prentice Hall, Upper Saddle River

    Google Scholar 

  43. Jiang H, Lai L, Fan R, Poor HV (2009) Optimal selection of channel sensing order in cognitive radios. IEEE Trans Wirel Commun 8(1):297–307

    Article  Google Scholar 

Recommended Reading

  1. Hassibi B, Hochwald B (2003) How much training is needed in multiple-antenna wireless links? IEEE Trans Inf Theory 49(4):951–963

    Article  MATH  Google Scholar 

  2. Kay SM (1993) Fundamentals of statistical processing. Volume I: Estimation theory. Prentice Hall, Englewood Cliffs

    Google Scholar 

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Acknowledgements

The work of L. Lai was supported by National Science Foundation under grant CNS-1660128.

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Authors and Affiliations

  1. Futurewei Technologies Inc., 400 Crossings Blvd., Bridgewater, NJ, USA

    Yan Xin

  2. University of California, 1 Shields Ave, Davis, CA, USA

    Lifeng Lai

Authors
  1. Yan Xin
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  2. Lifeng Lai
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Correspondence to Yan Xin .

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Editors and Affiliations

  1. Electrical Engg Bldg, Rm 325, The University of New South Wales, Sydney, New South Wales, Australia

    Wei Zhang

Section Editor information

  1. School of Electrical Engineering and Telecommunications, The University of New South Wales, 2052, Sydney, NSW, Australia

    Wei Zhang

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© 2017 Springer Nature Singapore Pte Ltd.

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Xin, Y., Lai, L. (2017). Sequential Methods for Spectrum Sensing. In: Zhang, W. (eds) Handbook of Cognitive Radio . Springer, Singapore. https://doi.org/10.1007/978-981-10-1389-8_9-1

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  • DOI: https://doi.org/10.1007/978-981-10-1389-8_9-1

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  • Print ISBN: 978-981-10-1389-8

  • Online ISBN: 978-981-10-1389-8

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