Skip to main content
Log in

Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm

Soft Computing Aims and scope Submit manuscript

Abstract

In this paper, we present an efficient adaptive artificial bee colony (EA-ABC) algorithm that includes an adaptive mutation mechanism, guaranteed convergence mechanism and optimal tracking mechanism. The EA-ABC algorithm is applied to cooperative spectrum sensing for a cognitive radio field. An efficient sensing scheme can reduce the false alarm probability and enhance the detection probability, which can improve spectrum utilization. Simulations are conducted to compare the performance of the proposed cooperative spectrum sensing method based on the EA-ABC algorithm with that of the cooperative spectrum sensing method using the ABC, particle swarm optimization (PSO), and modified PSO algorithms. The simulation results validate the effectiveness and reliability of the proposed method and demonstrate that EA-ABC-based can achieve higher detection probability than other methods under the same false alarm probability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  • Alfi A (2011) PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Autom Sinica 37:541–549

    Article  MATH  Google Scholar 

  • Bergh VD (2002) Analysis of particle swarm optimizers [D]. Pretoria

  • Cabric D, Mishra SM, Brodersen R (2004) Implementation issues in spectrum sensing for cognitive radios. In: Proceeding 38th Asilomar Conference Signals. Systems and Computers, Pacific Grove, USA, vol 1, pp 772–776

  • Dereli T, Das GS (2011) A hybrid ’bee(s) algorithm’ for solving container loading problems. Appl Soft Comput 11:2854–2862

    Article  Google Scholar 

  • Haykin S (2005) Cognitive radios: brain-empowered wireless communications. IEEE J Sel Areas Commun 23:201–220

    Article  Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697

    Article  Google Scholar 

  • Karaboga D, Basturk B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    Article  MATH  MathSciNet  Google Scholar 

  • Karaboga D, Ozturk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw World 19(3):279–292

    Google Scholar 

  • Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11:652–657

    Article  Google Scholar 

  • Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18(7):847–860

    Article  Google Scholar 

  • Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inform Sci 209:1–15

    Article  Google Scholar 

  • Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrainde optimization problems. Advances in soft computing: Foundation of Fuzzy logic and soft computing, pp 789–798

  • Krink T, Vesterstrom JS, Riget J (2002) Particle swarm optimization with spatial particle extension. In: Proceedings of IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, pp 1474–1497

  • Kuo RJ, Yang CY (2011) Simulation optimization using particle swarm optimization algorthm with application to assembly line design. Appl Soft Comput 11:605–613

    Article  Google Scholar 

  • Li G, Niu P, Xiao X (2012) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12:320–332

    Article  Google Scholar 

  • Liang Y, Leung KS (2011) Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput 11:2017–2034

    Article  Google Scholar 

  • Ma J, Li GY, Juang BH (2009) Signal processing in cognitive radio. Proces IEEE 97:805–823

    Article  Google Scholar 

  • Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11:1679–1696

    Article  Google Scholar 

  • de Oliveira IMS, Schirru R (2011) Swarm intelligence of artificial bees applied to in-core fuel management optimization. Appl Soft Comput 38:1039–1045

    Google Scholar 

  • Ozturk C, Karaboga D, Gorkemli B (2011) Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm. Sensors 11(6):6056–6065

    Article  Google Scholar 

  • Peh E, Liang YC (2007) Optimization for cooperative sensing in cognitive radio networks. In: Processing IEEE International Wireless Communication and Networking Conference, Hong Kong, pp 27–32

  • Quan Z, Cui S, Sayed AH (2008) Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE J Sel Top Sign Proces 2:28–40

    Google Scholar 

  • Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9:625–631

    Article  Google Scholar 

  • Vistotsky E, Kuffner S, Peterson R (2005) On collaborative detection of TV transimissions in support of dynamic spectrum sharing. IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, MD, USA, pp 338–345

  • Yue WJ, Zheng BY (2010) Spectrum sensing algorithms for primary detection based on reliabiliy in cognitive radio systems. Comput Electr Eng 36:469–479

    Article  Google Scholar 

  • Zhang J-J, Wang Z-S, Yu H (2010) Matching pursuit based on hybrid particle swarm optimization algorithm. J Vib Shock 29:143–147

    Google Scholar 

  • Zheng S, Lou C, Yang X (2010) Cooperative spectrum sensing using particle swarm optimisation. Electr Lett 46:1525–1526

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the financial support of the Natural Science Foundation of China under Grant No.61172095, 61203104. Nature Science Foundation of Hebei Province under Grant No.F2012203138.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xinbin Li.

Additional information

Communicated by D. Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, X., Lu, L., Liu, L. et al. Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm. Soft Comput 19, 597–607 (2015). https://doi.org/10.1007/s00500-014-1280-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1280-2

Keywords

Navigation