A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

  • H. Abdullahi
  • A. J. OnumanyiEmail author
  • S. Zubair
  • A. M. Abu-Mahfouz
  • G. P. Hancke
Methodologies and Application


The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.


Adaptive threshold Autonomous Cognitive radio FCME Metaheuristic algorithm Parameter tuning 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Akpakwu GA, Silva BJ, Hancke GP, Abu-Mahfouz AM (2017) A survey on 5G networks for the internet of things: communication technologies and challenges. IEEE Access 3536(c):3619–3647Google Scholar
  2. Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50:2127–2159CrossRefGoogle Scholar
  3. Arienzo L, Tarchi D (2015) Statistical modeling of spectrum sensing energy in multi-hop cognitive radio networks. IEEE Signal Process Lett 22:356–360CrossRefGoogle Scholar
  4. Avila J, Thenmozhi K (2015) Adaptive double threshold with multiple energy detection technique in cognitive radio. Res J Appl Sci Eng Technol 10(11):1336–1342CrossRefGoogle Scholar
  5. Barnes SD, Maharaj BT (2014) Prediction based channel allocation performance for cognitive radio. AEU Int J Electron Commun 68:336–345CrossRefGoogle Scholar
  6. Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: Proceedings of the 4th annual conference on genetic and evolutionary computation, pp 11–18, Morgan Kaufmann Publishers IncGoogle Scholar
  7. Črepinšek M, Liu S-H, Mernik L, Mernik M (2016) Is a comparison of results meaningful from the inexact replications of computational experiments? Soft Comput 20(1):223–235CrossRefGoogle Scholar
  8. Eberhart R, Kennedy J (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948, CiteseerGoogle Scholar
  9. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  10. Gul N, Qureshi IM, Omar A, Elahi A, Khan S (2017) History based forward and feedback mechanism in cooperative spectrum sensing including malicious users in cognitive radio network. PloS One 12(8):1–21 CrossRefGoogle Scholar
  11. Gul N, Qureshi IM, Akbar S, Kamran M, Rasool I (2018a) One-to-many relationship based kullback leibler divergence against malicious users in cooperative spectrum sensing. Wirel Commun Mob Comput 2018:1–14CrossRefGoogle Scholar
  12. Gul N, Qureshi IM, Elahi A, Rasool I (2018b) Defense against malicious users in cooperative spectrum sensing using genetic algorithm. Int J Antennas Propag 2018:1–11CrossRefGoogle Scholar
  13. IEEE802.22 (2011) Enabling broadband wireless access using cognitive radio technology and spectrum sharing in white spaces. In: IEEE 802.22 working group on wireless regional area networksGoogle Scholar
  14. Iwata H, Umebayashi K, Tiiro S, Suzuki Y, Lehtomäki JJ (2016) A study on Welch FFT segment size selection method for spectrum awareness. In: 2016 IEEE wireless communications and networking conference workshops, WCNCW, vol 2016, no 8, pp 252–257Google Scholar
  15. Jia KX, He ZS (2011) Narrowband signal localization based on enhanced LAD method. J Commun Netw 13:6–11CrossRefGoogle Scholar
  16. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer engineering departmentGoogle Scholar
  17. Lehtomäki JJ, Salmenkaita S, Vartiainen J, Mäkelä JP, Vuohtoniemi R, Juntti M (2009) Measurement studies of a spectrum sensing algorithm based on double thresholding. In: 2009 2nd international workshop on cognitive radio and advanced spectrum management, CogART 2009, pp 69–73, IEEEGoogle Scholar
  18. Lehtomäki JJ, Vartiainen J, Juntti M, Saarnisaari H (2007) Spectrum sensing with forward methods. In: Proceedings—IEEE military communications conference MILCOM, IEEEGoogle Scholar
  19. Lehtomäki JJ, Vartiainen J, Saarnisaari H (2011) Adaptive FCME-based threshold setting for energy detectors. In: Proceedings of the 4th international conference on cognitive radio and advanced spectrum management, vol 33, pp 1–5Google Scholar
  20. Lehtomäki JJ, Vuohtoniemi R, Umebayashi K (2012) Duty Cycle and Channel Occupancy Rate Estimation with MED-FCME LAD ACC. In: Proceedings of the 7th international conference on cognitive radio oriented wireless networks, vol 248454, pp 236–241, IEEEGoogle Scholar
  21. Lehtomäki JJ, Juntti M, Saarnisaari H (2005) CFAR strategies for channelized radiometer. IEEE Signal Process Lett 12:13–16CrossRefGoogle Scholar
  22. Lehtomäki JJ, Vartiainen J, Juntti M, Saarnisaari H (2007) CFAR outlier detection with forward methods. IEEE Trans Signal Process 55:4702–4706 MathSciNetCrossRefGoogle Scholar
  23. Lehtomäki JJ, Vuohtoniemi R, Umebayashi K, Mäkelä JP (2012) Energy detection based estimation of channel occupancy rate with adaptive noise estimation. IEICE Trans Commun E95–B(4):1076–1084CrossRefGoogle Scholar
  24. Liu C, Li M, Jin M-L (2015) Blind energy-based detection for spatial spectrum sensing. IEEE Wirel Commun Lett 4:98–101CrossRefGoogle Scholar
  25. Malafaia D, Vieira J, Tomé A (2016) Adaptive threshold spectrum sensing based on expectation maximization algorithm. Phys Commun 21:60–69CrossRefGoogle Scholar
  26. Mucchi L, Carpini A, D’Anna T, Virk MH, Vuohtoniemi R, Hämäläinen M, Iinatti J (2015) Threshold setting for the evaluation of the aggregate interference in ISM band in hospital environments. In: International symposium on medical information and communication technology, ISMICT, vol 2015-May, pp 20–24, IEEEGoogle Scholar
  27. Nannen V, Eiben AE (2007) Efficient relevance estimation and value calibration of evolutionary algorithm parameters. In: 2007 IEEE congress on evolutionary computation, pp 103–110, IEEEGoogle Scholar
  28. Ntshabele K, Isong B, Abu-Mahfouz AM (2018) Analysis of energy inefficiency challenges in cognitive radio sensor network. In: The 44th annual conference of the IEEE industrial electronic society. Washington DC, USAGoogle Scholar
  29. Ogbodo EU, Dorrell DG, Abu-Mahfouz AM (Sept. 2017) Performance analysis of correlated multi-channels in cognitive radio sensor network based smart grid. In: The 13th IEEE AFRICON conference, (Cape Town, South Africa), pp 1653–1658Google Scholar
  30. Ogbodo EU, Dorrell D, Abu-Mahfouz AM (2017b) Cognitive radio based sensor network in smart grid: architectures, applications and communication technologies. IEEE Access 5(c):19084–19098CrossRefGoogle Scholar
  31. Onumanyi AJ (2018) Dataset for testing the performance of a cuckoo search optimization based forward consecutive mean excision model for threshold adaptation in cognitive radio. In: Mendeley data, v1.
  32. Onumanyi AJ, Onwuka EN, Aibinu AM, Ugweje OC, Salami MJE (2017) A modified Otsu’s algorithm for improving the performance of the energy detector in cognitive radio. AEU Int J Electron Commun 79:53–63CrossRefGoogle Scholar
  33. Onumanyi AJ, Abu-Mahfouz AM, Hancke GP (2018) A comparative analysis of local and global adaptive threshold estimation techniques for energy detection in cognitive radio. Phys Commun 29:1–11CrossRefGoogle Scholar
  34. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  35. Oyewobi SS, Hancke GP (2017) A survey of cognitive radio handoff schemes, challenges and issues for industrial wireless sensor networks (CR-IWSN). J Netw Comput Appl 97:140–156CrossRefGoogle Scholar
  36. Popoola JJ, van Olst R (2013) The performance evaluation of a spectrum sensing implementation using an automatic modulation classification detection method with a universal software radio peripheral. Expert Syst Appl 40(6):2165–2173CrossRefGoogle Scholar
  37. Puska H, Saarnisaari H, Iinatti J (2005) Comparison of antenna array algorithms in DS/SS code acquisition with jamming. In: Proceedings—IEEE military communications conference MILCOM, vol 2005, IEEEGoogle Scholar
  38. Saamisaari H, Henttu P (2003) Impulse detection and rejection methods for radio systems. In: IEEE military communications conference, 2003. MILCOM’03, vol 2, pp 1126–1131Google Scholar
  39. Schlain L, González G, Gregorio F, Cousseau J (oct 2016) Adaptive cyclostationary filtering for DGPS interference cancellation. In: 2015 16th workshop on information processing and control, RPIC 2015, IEEEGoogle Scholar
  40. Shen B, Zhao C, Huang L, Kwak K, Zhou Z (2008) Wideband primary user signal identification approaches for cognitive MB-OFDM UWB systems. In: 2008 third international conference on convergence and hybrid information technology, IEEEGoogle Scholar
  41. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefGoogle Scholar
  42. Vartiainen J (2012) Always one/zero malicious user detection in cooperative sensing using the FCME method. In: Proceedings of the 7th international conference on cognitive radio oriented wireless networks, IEEEGoogle Scholar
  43. Vartiainen J, Lehtomäki JJ, Saarnisaari H, Juntti M (2010) Analysis of the consecutive mean excision algorithms. J Electr Comput Eng 2010:1–13MathSciNetCrossRefGoogle Scholar
  44. Vartiainen J, Aromaa S, Saarnisaari H, Juntti M (2004) Performance evaluation of transform selective interference suppression. In: IEEE MILCOM 2004. Military communications conference, 2004. IEEE, vol 3, pp 1422–1428Google Scholar
  45. Vartiainen J, Henttu P (2004) Estimation of signal detection threshold by CME algorithms. In: IEEE 59th vehicular technology conference (VTC 2004-Spring), vol 3, no. 4, pp 1654–1658 (Volume 3)Google Scholar
  46. Vartiainen J, Lehtomäki JJ, Saarnisaari H (2005) Double-threshold based narrowband signal extraction. In: 2005 IEEE 61st vehicular technology conference, vol 2, no. 1, pp 5–9 Google Scholar
  47. Vartiainen J, Saarnisaari H, Lehtomäki JJ, Juntti M (2006) A blind signal localization and SNR estimation method. In: Proceedings of the IEEE military communications conference (MILCOM’06), pp. 1–7Google Scholar
  48. Vartiainen J, Sami A, Saarnisaari H, Juntti M (2004) Selection process of a transform selective interference suppression algorithm. In: Proceedings of the 6th nordic signal processing symposium, 2004. NORSIG 2004, pp 220–223, IEEEGoogle Scholar
  49. Veček N, Mernik M, Filipič B, Črepinšek M (2016) Parameter tuning with chess rating system (crs-tuning) for meta-heuristic algorithms. Inf Sci 372:446–469CrossRefGoogle Scholar
  50. Vuohtoniemi R, Lehtomäki JJ, Mäkelä JP (2016) Adaptive threshold based frequency exclusion algorithm for broadband PLC. In: 2016 International symposium on power line communications and its applications (ISPLC), IEEEGoogle Scholar
  51. Wang X, Cheng H, Huang M (2014) QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm. Expert Syst Appl 41(10):4513–4528CrossRefGoogle Scholar
  52. Wasonga F, Olwal TO, Abu-Mahfouz AM (June 2018) Efficient two stage spectrum sensing combination for cognitive radio. In: Proceedings of the 27th international symposium on industrial electronics (ISIE), pp 1308–1313Google Scholar
  53. Yang XS, Deb S (2009) Cuckoo search via \(L\dot{e}vy\) flights. In: 2009 World congress on nature and biologically inspired computing, NABIC 2009—proceedings, pp 210–214Google Scholar
  54. Zaharie D (2002) Critical values for the control parameters of differential evolution algorithms. In: Proceedings of MENDEL 2002, 8th international conference on soft computing, pp 62–67Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Telecommunication EngineeringFederal University of TechnologyMinnaNigeria
  2. 2.Department of Electrical, Electronic and Computer EngineeringUniversity of PretoriaPretoriaSouth Africa
  3. 3.Council for Scientific and Industrial ResearchPretoriaSouth Africa
  4. 4.Department of Computer ScienceCity University of Hong KongHong KongChina

Personalised recommendations