Spectrum Resource for Cognitive Radio Networks



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.


Radio-frequency spectrum Spectrum scarcity Dynamic spectrum access Spectrum sensing Cooperative spectrum sensing Predictive spectrum sensing Cognitive radio networks 


  1. 1.
    S. Haykin, P. Setoodeh, Cognitive radio networks: the spectrum supply chain paradigm. IEEE Trans. Cogn. Commun. Netw. 1(1), 3–28 (2015)CrossRefGoogle Scholar
  2. 2.
    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. 1–5Google Scholar
  3. 3.
    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. 1–4Google Scholar
  4. 4.
    J. Mitola, Cognitive radio: an integrated agent architecture for software defined radios. Ph.D. dissertation, KTH (2000)Google Scholar
  5. 5.
    J. Mitola, G.Q. Maguire, Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6(4), 13–18 (1999)CrossRefGoogle Scholar
  6. 6.
    L.E. Doyle, Essentials of Cognitive Radio. The Cambridge Wireless Essentials Series, New York (Cambridge University Press, Cambridge, 2009)CrossRefGoogle Scholar
  7. 7.
    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.
  8. 8.
    S.D. Barnes, A cooperative prediction based approach to spectrum management in cognitive radio networks, Ph.D. Dissertation, University of Pretoria (2016)Google Scholar
  9. 9.
    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. 497–503CrossRefGoogle Scholar
  10. 10.
    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. 305–309Google Scholar
  11. 11.
    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. 209–219Google Scholar
  12. 12.
    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. 1–6Google Scholar
  13. 13.
    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. 1–7Google Scholar
  14. 14.
    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. 179–186Google Scholar
  15. 15.
    T. Yucek, H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutorials 11(1), 116–130 (2009)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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. 6482–6486Google Scholar
  19. 19.
    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).
  20. 20.
    I.F. Akyildiz, B.F. Lo, R. Balakrishnan, Cooperative spectrum sensing in cognitive radio networks: a survey. Phys. Commun. 4(1), 40–62 (2011). CrossRefGoogle Scholar
  21. 21.
    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. 1–7Google Scholar
  22. 22.
    P. Verma, B. Singh, On the decision fusion for cooperative spectrum sensing in cognitive radio networks. Wirel. Netw. 23(7), 2253–2262 (2017). CrossRefGoogle Scholar
  23. 23.
    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. 220–225Google Scholar
  24. 24.
    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. 133–137Google Scholar
  25. 25.
    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. 762–765Google Scholar
  26. 26.
    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). CrossRefGoogle Scholar
  27. 27.
    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)CrossRefGoogle Scholar
  28. 28.
    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. 2784–2788Google Scholar
  29. 29.
    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. 1–7Google Scholar
  30. 30.
    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)Google Scholar
  31. 31.
    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). CrossRefGoogle Scholar
  32. 32.
    H. Marquez, Prediction of channel availability in cognitive radio networks using a logistic regression algorithm. Int. J. Eng. Technol. 9(10), 3813–3820 (2017)CrossRefGoogle Scholar
  33. 33.
    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. 705–709Google Scholar
  34. 34.
    G.S. Uyanik, B. Canberk, S. Oktug, Predictive spectrum decision mechanisms in cognitive radio networks, in 2012 IEEE Globecom Workshops (2012), pp. 943–947Google Scholar
  35. 35.
    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. 154–157Google Scholar
  36. 36.
    P. Kulkarni, T. Lewis, Z. Fan, Simple traffic prediction mechanism and its applications in wireless networks. Wirel. Pers. Commun. 59(2), 261–274 (2011). CrossRefGoogle Scholar
  37. 37.
    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)Google Scholar
  38. 38.
    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)CrossRefGoogle Scholar
  39. 39.
    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)CrossRefGoogle Scholar
  40. 40.
    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. 3016–3020Google Scholar
  41. 41.
    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. 1–9Google Scholar
  42. 42.
    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. 676–980Google Scholar
  43. 43.
    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. 100–107Google Scholar
  44. 44.
    S. Barnes, P.J. van Vuuren, B. Maharaj, Spectrum occupancy investigation: measurements in South Africa. Measurement 46(9), 3098–3112 (2013). CrossRefGoogle Scholar
  45. 45.
    S. Barnes, P. Botha, B. Maharaj, Spectral occupation of TV broadcast bands: measurement and analysis. Measurement 93, 272–277 (2016). CrossRefGoogle Scholar
  46. 46.
    ICASA, Draft terrestrial broadcasting frequency plan 2013 (2013). Government Gazette, Republic of South Africa 574 (36321)Google Scholar
  47. 47.
    CSIR, Tv white space database (2014).

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Authors and Affiliations

  1. 1.University of PretoriaPretoriaSouth Africa

Personalised recommendations