Spectrum Sensing, Measurement, and Modeling

  • Ghaith HattabEmail author
  • Danijela Cabric
Living reference work entry


Modeling spectrum sensing is a critical step that paves the way to (i) identify the key impairments that affect the detection performance and (ii) help develop algorithms and receiver architectures that mitigate these impairments. In this chapter, realistic and practical sensing models are presented beyond those developed for classical detection theory. These models capture the impact of different sensing receiver impairments on several detectors such as the energy, the pilot, and the cyclostationarity detectors. Several receiver nonidealities are investigated, including noise uncertainty, imperfect synchronization, and cyclic frequency offsets. In addition, challenges and impairments pertaining to wideband sensing are analyzed, including the presence of strong adjacent interferers as well as the nonlinearities of the receiver RF front-end. From these models, several mitigation techniques are developed to compensate for the presence of the different sensing receiver impairments. Measurements and simulation results are presented throughout the chapter to show the negative impact of such impairments and validate that the developed mitigation techniques provide tangible performance gains.


  1. 1.
    ATSC Digital Television Standard (2007) ATSC Std. A/53.
  2. 2.
    Axell E, Leus G, Larsson EG, Poor HV (2012) Spectrum sensing for cognitive radio: state-of-the-art and recent advances. IEEE Signal Proc Mag 29(3):101–116. doi:10.1109/MSP.2012.2183771CrossRefGoogle Scholar
  3. 3.
    Cabric D (2008) Addressing feasibility of cognitive radios. IEEE Signal Proc Mag 25(6):85–93. doi:10.1109/MSP.2008.929367CrossRefGoogle Scholar
  4. 4.
    Cabric D, Mishra S, Brodersen R (2004) Implementation issues in spectrum sensing for cognitive radios. In: Proceedings of the 38th Asilomar Conference on Signals, System and Computers (ASILOMAR’04), vol 1, pp 772–776Google Scholar
  5. 5.
    Cabric D, Tkachenko A, Brodersen R (2006) Spectrum sensing measurements of pilot, energy, and collaborative detection. In: Proceedings of the IEEE Military Communications Conference (MILCOM’06), pp 1–7Google Scholar
  6. 6.
    Chuinard G, Cabric D, Ghosh M (2006) Sensing thresholds. Technical report, EEE 802.22-06/005/r3Google Scholar
  7. 7.
    Dandawate AV, Giannakis GB (1994) Statistical tests for presence of cyclostationarity. IEEE Trans Signal Process 42(9):2355–2369. doi:10.1109/78.317857CrossRefGoogle Scholar
  8. 8.
    Gardner W (1991) Exploitation of spectral redundancy in cyclostationary signals 8(2):14–36Google Scholar
  9. 9.
    Harjani R, Cabric D, Markovic D, Sadler BM, Palani RK, Saha A, Shin H, Rebeiz E, Basir-Kazeruni S, Yuan FL (2015) Wideband blind signal classification on a battery budget. IEEE Commun Mag 53(10):173–181. doi:10.1109/MCOM.2015.7295481CrossRefGoogle Scholar
  10. 10.
    Hattab G, Ibnkahla M (2014) Enhanced pilot-based spectrum sensing algorithm. In: Proceedings of the IEEE Biennial Symposium on Communication (QBSC’14), pp 57–60. doi:10.1109/QBSC.2014.6841184Google Scholar
  11. 11.
    Hattab G, Ibnkahla M (2014) Multiband spectrum access: great promises for future cognitive radio networks. Proc IEEE 102(3):282–306. doi:10.1109/JPROC.2014.2303977CrossRefGoogle Scholar
  12. 12.
    Hossain K, Champagne B (2011) Wideband spectrum sensing for cognitive radios with correlated subband occupancy. IEEE Signal Proc Lett 18(1):35–38. doi:10.1109/LSP.2010.2091405CrossRefGoogle Scholar
  13. 13.
    Kay S (1993) Fundamentals of statistical signal processing, vol I – estimation theory. Prentice HallzbMATHGoogle Scholar
  14. 14.
    Lunden J, Koivunen V, Huttunen A, Poor HV (2009) Collaborative cyclostationary spectrum sensing for cognitive radio systems. IEEE Trans Signal Process 57(11):4182–4195. doi:10.1109/TSP.2009.2025152MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mariani A, Giorgetti A, Chiani M (2011) Effects of noise power estimation on energy detection for cognitive radio applications. IEEE Trans Commun 59(12):3410–3420. doi:10.1109/TCOMM.2011.102011.100708CrossRefGoogle Scholar
  16. 16.
    Paysarvi-Hoseini P, Beaulieu NC (2011) Optimal wideband spectrum sensing framework for cognitive radio systems. IEEE Trans Signal Process 59(3):1170–1182. doi:10.1109/TSP.2010.2096220MathSciNetCrossRefGoogle Scholar
  17. 17.
    Pei Y, Liang YC, Teh KC, Li KH (2009) How much time is needed for wideband spectrum sensing? IEEE Trans Wirel Commun 8(11):5466–5471. doi:10.1109/TWC.2009.090350CrossRefGoogle Scholar
  18. 18.
    Quan Z, Cui S, Sayed A, Poor H (2009) Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Trans Signal Process 57(3):1128–1140. doi:10.1109/TSP.2008.2008540MathSciNetCrossRefGoogle Scholar
  19. 19.
    Rebeiz E, Ghadam ASH, Valkama M, Cabric D (2015) Spectrum sensing under RF non-linearities: performance analysis and DSP-enhanced receivers. IEEE Trans Signal Process 63(8):1950–1964. doi:10.1109/TSP.2015.2401532MathSciNetCrossRefGoogle Scholar
  20. 20.
    Rebeiz E, Urriza P, Cabric D (2012) Experimental analysis of cyclostationary detectors under cyclic frequency offsets. In: Conference on Signals, Systems and Computers (ASILOMAR’12), pp 1031–1035Google Scholar
  21. 21.
    Rebeiz E, Urriza P, Cabric D (2013) Optimizing wideband cyclostationary spectrum sensing under receiver impairments. IEEE Trans Signal Process 61(15):3931–3943. doi:10.1109/TSP.2013.2262680MathSciNetCrossRefGoogle Scholar
  22. 22.
    Rebeiz E, Yuan FL, Urriza P, Markovi D, Cabric D (2014) Energy-efficient processor for blind signal classification in cognitive radio networks. IEEE Trans Circuits Syst I Regul Pap 61(2):587–599. doi:10.1109/TCSI.2013.2278392CrossRefGoogle Scholar
  23. 23.
    Sun H, Nallanathan A, Wang CX, Chen Y (2013) Wideband spectrum sensing for cognitive radio networks: a survey. IEEE Wirel Commun 20(2):74–81. doi:10.1109/MWC.2013.6507397CrossRefGoogle Scholar
  24. 24.
    Tandra R, Sahai A (2008) SNR walls for signal detection. IEEE J Sel Top Signal Process 2(1):4–17. doi:10.1109/JSTSP.2007.914879CrossRefGoogle Scholar
  25. 25.
    Yu TH, Rodriguez-Parera S, Markovic D, Cabric D (2010) Cognitive radio wideband spectrum sensing using multitap windowing and power detection with threshold adaptation. In: 2010 IEEE International Conference on Communications, pp 1–6. doi:10.1109/ICC.2010.5502024Google Scholar
  26. 26.
    Yu TH, Sekkat O, Rodriguez-Parera S, Markovic D, Cabric D (2011) A wideband spectrum-sensing processor with adaptive detection threshold and sensing time. IEEE Trans Circuits Syst I Regul Pap 58(11):2765–2775. doi:10.1109/TCSI.2011.2143010MathSciNetCrossRefGoogle Scholar
  27. 27.
    Yu TH, Yang CH, Cabric D, Markovic D (2012) A 7.4-mW 200-MS/s wideband spectrum sensing digital baseband processor for cognitive radios. IEEE J Solid-State Circuits 47(9):2235–2245. doi:10.1109/JSSC.2012.2195933CrossRefGoogle Scholar
  28. 28.
    Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. Commun Surveys Tutor 11(1):116–130. doi:10.1109/SURV.2009.090109CrossRefGoogle Scholar
  29. 29.
    Zeng Y, Liang YC (2010) Robustness of the cyclostationary detection to cyclic frequency mismatch. In: 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp 2704–2709. doi:10.1109/PIMRC.2010.5671799Google Scholar
  30. 30.
    Zou Q, Mikhemar M, Sayed AH (2009) Digital compensation of cross-modulation distortion in software-defined radios. IEEE J Sel Top Signal Process 3(3):348–361. doi:10.1109/JSTSP.2009.2020266CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Electrical EngineeringUniversity of California, Los Angeles (UCLA)Los AngelesUSA

Section editors and affiliations

  • Wei Zhang
    • 1
  1. 1.School of Electrical Engineering and TelecommunicationsThe University of New South WalesSydneyAustralia

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