A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks

Abstract

Spectrum sensing, through efficient channel estimation methods utilizing cognitive radio networks, has become an increasingly researched area in recent times. This has become more pronounced in recent times, especially with increasing scarcity in availability of radio frequency spectrum. Wireless state of the art communication standards demand high bandwidth to provide seamless connectivity and high degree of mobility, which requires more of radio frequency band for functioning. Hence, intelligent methods of spectrum allocation have been an increasing challenge in recent times. This paper proposes a hybrid learning based-Ada Boost classifier model for efficient spectrum allocation through channel state estimation through a learning and double classification approach. The proposed algorithm has been experimented in a high bandwidth characterized 5G communication simulation settings and observed for its performance measures namely collision rate analysis, throughput, probability of detection, false alarm detection and bit error rate. The proposed technique has been compared against benchmark techniques such as conventional fast Fourier transform based energy detector, fuzzy cognitive engine and adaptive neuro fuzzy inference model without Ada Boost and found to exhibit superior performance in all performance measures. The proposed technique exhibits a spectral efficiency of nearly 90% and considered to be a suitable spectrum sensing scheme for high bandwidth and narrowband utilities.

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References

  1. 1.

    Zeng Y, Liang Y-C, Hoang AT, Zhang R (2010) A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process 2010:1–15

    Article  Google Scholar 

  2. 2.

    Zhang S, Wu T, Lau VKN (2009) A low-overhead energy detection based cooperative sensing protocol for cognitive radio systems. IEEE Trans Wirel Commun 8(11):5575–5581

    Article  Google Scholar 

  3. 3.

    Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio networks. IEEE Commun Surv Tutor 11(1):116–129

    Article  Google Scholar 

  4. 4.

    Liang YC, Zeng Y, Peh ECY, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wirel Commun 7(4):1326–1337

    Article  Google Scholar 

  5. 5.

    Kalai AT, Servedio RA (2005) Boosting in presence of noise. J Comput Syst Sci 71(3):266–290

    MathSciNet  Article  Google Scholar 

  6. 6.

    Lee WY, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wirel Commun 7(10):3845–3857

    Article  Google Scholar 

  7. 7.

    Sutton PD, Nolan KE, Doyle LE (2008) Cyclostationary signatures in practical cognitive radio applications. IEEE J Sel Areas Commun 26(1):13–24

    Article  Google Scholar 

  8. 8.

    Tumuluru VK, Wang P, Niyato D (2010) A neural network based spectrum prediction scheme for cognitive radio. In: IEEE international conference on communications, pp 1–5

  9. 9.

    Tumuluru VK, Wang P, Niyato D (2012) Channel status prediction, n for cognitive radio networks. J Wirel Commun Mob Comput 12(10):862–874

    Article  Google Scholar 

  10. 10.

    Xing X, Jing T, Cheng W, Huo Y, Cheng X (2013) Spectrum prediction in cognitive radio networks. IEEE Wirel Commun 20(2):90–96

    Article  Google Scholar 

  11. 11.

    He A (2010) A survey of artificial intelligence for cognitive radios. IEEE Trans Veh Technol 59(4):1578–1592

    Article  Google Scholar 

  12. 12.

    Subhedar M, Birajdar G (2011) Spectrum sensing techniques in cognitive radio networks—a survey. Int J Next Gener Netw 3(2):37–51

    Article  Google Scholar 

  13. 13.

    Wan X, Hu P, Wang Z (2016) ISM band prediction algorithm based on two dimensional LMBP neural network. J Telecommun Sci 32(3):52–59

    Google Scholar 

  14. 14.

    Atapattu S, Tellambura C, Jiang H (2001) Energy detection based cooperative spectrum sensing in cognitive radio networks. IEEE Trans Wirel Commun 10(4):1–10

    Google Scholar 

  15. 15.

    Mannor S, Meir R, Zhang T (2003) Greedy algorithms for classification—consistency convergence rates and adaptivity. J Mach Learn Res 4:713–742

    MathSciNet  MATH  Google Scholar 

  16. 16.

    Lin G, Cheng Y, Jiang H et al. (2016) Performance analysis of three-state HMM in shortwave channel estimation. J Commun Technol 49(3):44–59

    Google Scholar 

  17. 17.

    Sharma SK, Chatzinotas S, Ottersten B (2013) Eigen value based sensing and SNR estimation for cognitive radio in presence of noise correlation. IEEE Trans Veh Technol 62(8):3671–3684

    Article  Google Scholar 

  18. 18.

    Li H (2015) Cognitive radio based on the support vector machine to estimate the spectrum of leisure. J Mod Electron Technol 38(7):617–627

    Google Scholar 

  19. 19.

    Zhao Q, Tong L, Swami A, Chen Y (2007) Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: a POMDP framework. IEEE J Sel Areas Commun 25(3):589–600

    Article  Google Scholar 

  20. 20.

    Che Y, Zhang R, Gong Y (2013) On design of opportunistic spectrum access in the presence of reactive primary users. IEEE Trans Commun 61(7):2678–2691

    Article  Google Scholar 

  21. 21.

    Xu Y, Lu H, Chen X et al. (2014) Prediction method of cognitive radio spectrum based on support vector machine. J Telecommun Sci 6(17):2855–2863

    Google Scholar 

  22. 22.

    Noh J, Oh S (2014) Cognitive radio channel with cooperative multi-antenna secondary systems. IEEE J Sel Areas Commun 32(3):539–549

    Article  Google Scholar 

Download references

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Correspondence to S. Vadivukkarasi.

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Vadivukkarasi, S., Santhi, S. A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks. Int. J. Dynam. Control 9, 299–307 (2021). https://doi.org/10.1007/s40435-020-00633-y

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Keywords

  • Channel state estimation
  • Spectrum sensing
  • Cognitive radio networks
  • Ada Boost algorithms
  • Supervised learning
  • Back propagation learning