Abstract
This chapter discusses the spectrum and, by extension, spectrum sensing as the most essential aspect of the cognitive radio network scheme. The chapter further establishes that, by simply improving spectrum sensing, the challenge of spectrum scarcity and underutilisation can be significantly mitigated in modern wireless communications. Traditional approaches to spectrum sensing, such as energy detection and matched filter detection are discussed, alongside new and improved approaches to spectrum sensing, such as cooperative and predictive spectrum sensing. Some recent measurement campaigns on the spectrum are discussed to illustrate the importance of the spectrum in the overall cognitive radio network realisation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Haykin, P. Setoodeh, Cognitive radio networks: the spectrum supply chain paradigm. IEEE Trans. Cogn. Commun. Netw. 1(1), 3–28 (2015)
S. Filin, H. Harada, M. Hasegawa, Performance evaluation of dynamic spectrum assignment and access technologies, in Proceedings of the IEEE 19th International Symposium on PIMRC (2008), pp. 1–5
J. Pastircak, J. Gazda, D. Kocur, A survey on the spectrum trading in dynamic spectrum access networks, in Proceedings of the 56th International Symposium on ELMAR (2014), pp. 1–4
J. Mitola, Cognitive radio: an integrated agent architecture for software defined radios. Ph.D. dissertation, KTH (2000)
J. Mitola, G.Q. Maguire, Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)
L.E. Doyle, Essentials of Cognitive Radio. The Cambridge Wireless Essentials Series, New York (Cambridge University Press, Cambridge, 2009)
D.M.M. Plata, Á. Gabriel, A. Reátiga, Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold, in Procedia Engineering, vol. 35 (2012). International Meeting of Electrical Engineering Research 2012, pp. 135–143. http://www.sciencedirect.com/science/article/pii/S1877705812018097
S.D. Barnes, A cooperative prediction based approach to spectrum management in cognitive radio networks, Ph.D. Dissertation, University of Pretoria (2016)
S. Dannana, B.P. Chapa, G.S. Rao, Spectrum sensing using matched filter detection, in Intelligent Engineering Informatics, ed. by V. Bhateja, C.A. Coello Coello, S.C. Satapathy, P.K. Pattnaik (Springer, Singapore, 2018), pp. 497–503
Q. Lv, F. Gao, Matched filter based spectrum sensing and power level recognition with multiple antennas, in 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (2015), pp. 305–309
D. Ghosh, S. Bagchi, Cyclostationary feature detection based spectrum sensing technique of cognitive radio in nakagami-m fading environment, in Computational Intelligence in Data Mining, vol. 2, ed. by L.C. Jain, H.S. Behera, J.K. Mandal, D.P. Mohapatra (Springer, New Delhi, 2015), pp. 209–219
R. Kishore, C.K. Ramesha, G. Joseph, E. Sangodkar, Waveform and energy based dual stage sensing technique for cognitive radio using RTL-SDR, in 2016 IEEE Annual India Conference (INDICON) (2016), pp. 1–6
S. Geirhofer, L. Tong, B.M. Sadler, A measurement-based model for dynamic spectrum access in WLAN channels, in MILCOM’06: Proceedings of the 2006 IEEE Conference on Military Communications (2006), pp. 1–7
S.M. Mishra, S. ten Brink, R. Mahadevappa, R.W. Brodersen, Cognitive technology for Ultra-Wideband/WiMax coexistence, in 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (2007), pp. 179–186
T. Yucek, H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutorials 11(1), 116–130 (2009)
J. Ma, G. Zhao, Y. Li, Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Trans. Wirel. Commun. 7(11), 4502–4507 (2008)
Y. Liang, Y. Zeng, E.C.Y. Peh, A. T. Hoang, Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wirel. Commun. 7(4), 1326–1337 (2008)
A. Pandharipande J.M.G. Linnartz, Performance analysis of primary user detection in a multiple antenna cognitive radio, in 2007 IEEE International Conference on Communications (2007), pp. 6482–6486
Y. Zeng, Y.-C. Liang, A. Hoang, R. Zhang, A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP J. Adv. Signal Process. 2010(1), 381465 (2010). http://asp.eurasipjournals.com/content/2010/1/381465
I.F. Akyildiz, B.F. Lo, R. Balakrishnan, Cooperative spectrum sensing in cognitive radio networks: a survey. Phys. Commun. 4(1), 40–62 (2011). http://www.sciencedirect.com/science/article/pii/S187449071000039X
D. Teguig, B. Scheers, V. Le Nir, Data fusion schemes for cooperative spectrum sensing in cognitive radio networks, in 2012 Military Communications and Information Systems Conference (MCC) (2012), pp. 1–7
P. Verma, B. Singh, On the decision fusion for cooperative spectrum sensing in cognitive radio networks. Wirel. Netw. 23(7), 2253–2262 (2017). https://doi.org/10.1007/s11276-016-1285-0
S. Nallagonda, Y.R. Kumar, P. Shilpa, Analysis of hard-decision and soft-data fusion schemes for cooperative spectrum sensing in Rayleigh fading channel, in 2017 IEEE 7th International Advance Computing Conference (IACC) (2017), pp. 220–225
B.S. Shawel, D. Hailemariam Woledegebre, S. Pollin, Deep-learning based cooperative spectrum prediction for cognitive networks, in 2018 International Conference on Information and Communication Technology Convergence (ICTC) (2018), pp. 133–137
Z. Jianli, W. Mingwei, Y. Jinsha, Based on neural network spectrum prediction of cognitive radio, in 2011 International Conference on Electronics, Communications and Control (ICECC) (2011), pp. 762–765
D. Das, D.W. Matolak, S. Das, Spectrum occupancy prediction based on functional link artificial neural network (flann) in ISM band. Neural Comput. Appl. 29(12), 1363–1376 (2018). https://doi.org/10.1007/s00521-016-2653-5
C. Yu, Y. He, T. Quan, Frequency spectrum prediction method based on EMD and SVR, in 2008 Eighth International Conference on Intelligent Systems Design and Applications 3, 39–44 (2008)
Y. Li, Y. Dong, H. Zhang, H. Zhao, H. Shi, X. Zhao, Spectrum usage prediction based on high-order Markov model for cognitive radio networks, in 2010 10th IEEE International Conference on Computer and Information Technology (2010), pp. 2784–2788
A. Saad, B. Staehle, R. Knorr, Spectrum prediction using hidden Markov models for industrial cognitive radio, in 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (2016), pp. 1–7
I. Sidi Mohamed Hadj, M. Hachemi, H.E. Adardour, M. Hadjila, Spectrum sensing with VSS-NLMS process in Femto/Macro-cell environments. Int. J. Elect. Comput. Eng. 8(12), 5185 (2018)
X. Tan, H. Zhang, Q. Chen, J. Hu, Opportunistic channel selection based on time series prediction in cognitive radio networks. Trans. Emerg. Telecomm. Technol. 25(11), 1126–1136 (2014). https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.2664
H. Marquez, Prediction of channel availability in cognitive radio networks using a logistic regression algorithm. Int. J. Eng. Technol. 9(10), 3813–3820 (2017)
A. Gorcin, H. Celebi, K.A. Qaraqe, H. Arslan, An autoregressive approach for spectrum occupancy modeling and prediction based on synchronous measurements, in 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications (2011), pp. 705–709
G.S. Uyanik, B. Canberk, S. Oktug, Predictive spectrum decision mechanisms in cognitive radio networks, in 2012 IEEE Globecom Workshops (2012), pp. 943–947
Z. Wen, T. Luo, W. Xiang, S. Majhi, Y. Ma, Autoregressive spectrum hole prediction model for cognitive radio systems, in IEEE International Conference on Communications Workshops (ICCW 2008) (2008), pp. 154–157
P. Kulkarni, T. Lewis, Z. Fan, Simple traffic prediction mechanism and its applications in wireless networks. Wirel. Pers. Commun. 59(2), 261–274 (2011). https://doi.org/10.1007/s11277-009-9916-8
H. Eltom, K. Sithamparanathan, R. Evans, Y. Chang Liang, B. Risti, Statistical spectrum occupancy prediction for dynamic spectrum access: a classification. EURASIP J. Wirel. Commun. Netw. 2018(12), 29 (2018)
C. Ghosh, S. Pagadarai, D.P. Agrawal, A.M. Wyglinski, A framework for statistical wireless spectrum occupancy modeling. IEEE Trans. Wirel. Commun. 9(1), 38–44 (2010)
Z. Chen, N. Guo, Z. Hu, R.C. Qiu, Experimental validation of channel state prediction considering delays in practical cognitive radio. IEEE Trans. Vehi. Technol. 60(4), 1314–1325 (2011)
R.I.C. Chiang, G.B. Rowe, K.W. Sowerby, A quantitative analysis of spectral occupancy measurements for cognitive radio, in 2007 IEEE 65th Vehicular Technology Conference – VTC2007-Spring (2007), pp. 3016–3020
M. Lopez-Benitez, F. Casadevall, A. Umbert, J. Perez-Romero, R. Hachemani, J. Palicot, C. Moy, Spectral occupation measurements and blind standard recognition sensor for cognitive radio networks, in 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications (2009), pp. 1–9
M. Matinmikko, M. Mustonen, M. HÃűyhtyÃd’, T. Rauma, H. Sarvanko, A. MÃd’mmelÃd’, Distributed and directional spectrum occupancy measurements in the 2.4 GHz ISM band, in 2010 7th International Symposium on Wireless Communication Systems (2010), pp. 676–980
T.M. Taher, R.B. Bacchus, K.J. Zdunek, D.A. Roberson, Long-term spectral occupancy findings in chicago, in 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) (2011), pp. 100–107
S. Barnes, P.J. van Vuuren, B. Maharaj, Spectrum occupancy investigation: measurements in South Africa. Measurement 46(9), 3098–3112 (2013). http://www.sciencedirect.com/science/article/pii/S0263224113002431
S. Barnes, P. Botha, B. Maharaj, Spectral occupation of TV broadcast bands: measurement and analysis. Measurement 93, 272–277 (2016). http://www.sciencedirect.com/science/article/pii/S0263224116303785
ICASA, Draft terrestrial broadcasting frequency plan 2013 (2013). Government Gazette, Republic of South Africa 574 (36321)
CSIR, Tv white space database (2014). http://whitespaces.meraka.org.za
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Maharaj, B.T., Awoyemi, B.S. (2022). Spectrum Resource for Cognitive Radio Networks. In: Developments in Cognitive Radio Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-64653-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-64653-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64652-3
Online ISBN: 978-3-030-64653-0
eBook Packages: EngineeringEngineering (R0)