Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
ATSC Digital Television Standard (2007) ATSC Std. A/53. http://www.atsc.org/standards.html
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. https://doi.org/10.1109/MSP.2012.2183771
Cabric D (2008) Addressing feasibility of cognitive radios. IEEE Signal Proc Mag 25(6):85–93. https://doi.org/10.1109/MSP.2008.929367
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 772–776
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 1–7
Chuinard G, Cabric D, Ghosh M (2006) Sensing thresholds. Technical report, EEE 802.22-06/005/r3
Dandawate AV, Giannakis GB (1994) Statistical tests for presence of cyclostationarity. IEEE Trans Signal Process 42(9):2355–2369. https://doi.org/10.1109/78.317857
Gardner W (1991) Exploitation of spectral redundancy in cyclostationary signals 8(2): 14–36
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. https://doi.org/10.1109/MCOM.2015.7295481
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. https://doi.org/10.1109/QBSC.2014.6841184
Hattab G, Ibnkahla M (2014) Multiband spectrum access: great promises for future cognitive radio networks. Proc IEEE 102(3):282–306. https://doi.org/10.1109/JPROC.2014.2303977
Hossain K, Champagne B (2011) Wideband spectrum sensing for cognitive radios with correlated subband occupancy. IEEE Signal Proc Lett 18(1):35–38. https://doi.org/10.1109/LSP.2010.2091405
Kay S (1993) Fundamentals of statistical signal processing, vol I – estimation theory. Prentice Hall
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. https://doi.org/10.1109/TSP.2009.2025152
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. https://doi.org/10.1109/TCOMM.2011.102011.100708
Paysarvi-Hoseini P, Beaulieu NC (2011) Optimal wideband spectrum sensing framework for cognitive radio systems. IEEE Trans Signal Process 59(3):1170–1182. https://doi.org/10.1109/TSP.2010.2096220
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. https://doi.org/10.1109/TWC.2009.090350
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. https://doi.org/10.1109/TSP.2008.2008540
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. https://doi.org/10.1109/TSP.2015.2401532
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 1031–1035
Rebeiz E, Urriza P, Cabric D (2013) Optimizing wideband cyclostationary spectrum sensing under receiver impairments. IEEE Trans Signal Process 61(15):3931–3943. https://doi.org/10.1109/TSP.2013.2262680
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. https://doi.org/10.1109/TCSI.2013.2278392
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. https://doi.org/10.1109/MWC.2013.6507397
Tandra R, Sahai A (2008) SNR walls for signal detection. IEEE J Sel Top Signal Process 2(1):4–17. https://doi.org/10.1109/JSTSP.2007.914879
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. https://doi.org/10.1109/ICC.2010.5502024
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. https://doi.org/10.1109/TCSI.2011.2143010
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. https://doi.org/10.1109/JSSC.2012.2195933
Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. Commun Surveys Tutor 11(1):116–130. https://doi.org/10.1109/SURV.2009.090109
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. https://doi.org/10.1109/PIMRC.2010.5671799
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. https://doi.org/10.1109/JSTSP.2009.2020266
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this entry
Cite this entry
Hattab, G., Cabric, D. (2019). Spectrum Sensing, Measurement, and Modeling. In: Zhang, W. (eds) Handbook of Cognitive Radio . Springer, Singapore. https://doi.org/10.1007/978-981-10-1394-2_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-1394-2_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1393-5
Online ISBN: 978-981-10-1394-2
eBook Packages: EngineeringReference Module Computer Science and Engineering