Spectrum Sensing in Cognitive Radio Using Actor–Critic Neural Network with Krill Herd-Whale Optimization Algorithm

  • Mahua Bhowmik
  • P. Malathi


Spectrum sensing is the active research area in the Cognitive radio networks that initiates the effective data sharing between the licensed and the unlicensed users of Cognitive Network. Maximizing the detection probability for a provided false alarm rate is a hectic challenge of most of the spectral sensing methods. The paper proposes a spectral sensing method, termed as Krill-Herd Whale optimization-based actor critic neural network. The unoccupied spectrum is optimally determined using the proposed method that allocates the free spectrum bands to the primary users instantly such that the delay is minimized due to the effective functioning of the fusion center. For the effective sensing, the Eigen-value-based cooperative sensing is activated in the cognitive radio. The analysis of the proposed method is progressed based on the performance metrics, such as false alarm probability and detection probability. The proposed spectral sensing method outperforms the existing methods that yield a maximum probability of detection and minimum probability of false alarm at a rate of 0.9805 and of 0.009.


Spectrum sensing Cognitive radio Actor–critic neural network Krill Herd optimization Whale optimization algorithm 



  1. 1.
    Pandi, N., & Kumar, A. (2017). A review on cognitive radio for next generation cellular network and its challenges. American Journal of Engineering and Applied Sciences, 10(2), ​334–347.CrossRefGoogle Scholar
  2. 2.
    Marinho, J., Granjal, J., & Monteiro, E. (2015). A survey on security attacks and countermeasures with primary user detection in cognitive radio networks. EURASIP Journal on Information Security, 2015, 1–14.CrossRefGoogle Scholar
  3. 3.
    Zhang, D., Chen, Z., Ren, J., Zhang, N., Awad, M. K., Zhou, H., & Shen, X. (2015). Energy harvesting-aided spectrum sensing and data transmission in heterogeneous cognitive radio sensor network. IEEE Transactions on Vehicular Technology, 66(1), ​831–843.CrossRefGoogle Scholar
  4. 4.
    Supraja, P., & Jayashri, S. (2016). Optimized neural network for spectrum prediction scheme in cognitive radio. Wireless Personal Communication, 94(4), ​2597–2611.CrossRefGoogle Scholar
  5. 5.
    Roy, P., & Muralidhar, M. (2015). Channel state prediction in a cognitive radio network using neural network Levenberg–Marquardt algorithm. International Journal of Wireless Communications and Networking Technologies, 4(2), 24–29.Google Scholar
  6. 6.
    Venkatesan, M., & Kulkarni, A. V. (2014). Spectrum predictor model for cognitive radio. In International conference on advances in engineering & technology (ICAET) (pp. 10–14).Google Scholar
  7. 7.
    Sadough, S. S., & Ivrigh, S. S. (2012). Spectrum sensing for cognitive radio systems through primary user activity prediction. Radio Engineering, 21(4), 1092–1100.Google Scholar
  8. 8.
    Gavrilovska, L., & Atanasovski, V. (2011). Spectrum sensing framework for cognitive radio networks. Wireless Personal Communications, 59(3), 447–469.CrossRefGoogle Scholar
  9. 9.
    Christopher Clement, J., Bharath Reddy, B., & Emmanuel, D. S. (2016). An energy-efficient cooperative spectrum sensing strategy with robustness against noise uncertainty for cognitive radio networks. Arabian Journal for Science and Engineering, 41(9), 3399–3405.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chen, H., Zhou, M., Xie, L., & Jin, X. (2013). Fault-tolerant cooperative spectrum sensing scheme for cognitive radio networks. Wireless Personal Communications, 71(4), 2379–2397.CrossRefGoogle Scholar
  11. 11.
    Yu, H., Tang, W., & Li, S. (2011). Optimization of cooperative spectrum sensing with sensing user selection in cognitive radio networks. EURASIP Journal on Wireless Communications and Networking. Scholar
  12. 12.
    Akyildiz, I., Lo, B., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1), 40–62.CrossRefGoogle Scholar
  13. 13.
    Li, H., Xing, X., Zhu, J., Cheng, X., Li, K., Bie, R., Jing, T. (2015). Utility-based cooperative spectrum sensing scheduling in cognitive radio networks. IEEE Transactions on Vehicular Technology, 66​(1), 645–655.Google Scholar
  14. 14.
    Alom, Md. Z., Godder, T. K., Morshed, M. N., & Maali, A. (2017). Enhanced spectrum sensing based on energy detection in cognitive radio network using adaptive threshold.  In Proceedings of the IEEE International Conference on Networking, Systems and Security (NSysS). Google Scholar
  15. 15.
    Tumuluru, V. K., Wang, P., & Niyato, D. (2010). A neural network based spectrum prediction scheme for cognitive radio. In IEEE international conference on communications (ICC) (pp. 1–5).Google Scholar
  16. 16.
    Ferreira, P. V. R., Paffenrothy, R., Wyglinski, A. M., Hackettz, T. M., Bilénz, S. G., Reinhartx, R. C., & Mortensenx, D. J. (2017). Multi-objective reinforcement learning-based deep neural networks for cognitive space communications. In Proceedings of cognitive communications for aerospace applications workshop (CCAA) (pp. 1–8).Google Scholar
  17. 17.
    Surampudi, A., & Kalimuthu, K. (2016). An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks. In Proceedings of information processing (IICIP) (pp. 1–5).Google Scholar
  18. 18.
    Ratre, A., & Pankajakshan, V. (2017). Tucker visual search-based hybrid tracking model and Fractional Kohonen self-organizing map for anomaly localization and detection in surveillance videos. The Imaging Science Journal, 66, 1–16.Google Scholar
  19. 19.
    Dhumane, A. V., & Prasad, R. S. (2017). Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT. Wireless Networks, 1–15.Google Scholar
  20. 20.
    Nipanikar, S. I., Hima Deepthi, V., & Kulkarni, N. (2017). A sparse representation based image steganography using particle swarm optimization and wavelet transform. Alexandria Engineering Journal. Scholar
  21. 21.
    Shelke, P. M., & Prasad, R. S. (2018). An improved anti-forensics JPEG compression using least Cuckoo search algorithm. The Imaging Science Journal, 66(3), 169–183.CrossRefGoogle Scholar
  22. 22.
    Krishnamoorthy, N., & Asokan, R. (2014). Optimized resource selection to promote grid scheduling using hill climbing algorithm. International Journal of Computer Science and Telecommunications, 5(2), 14–19.Google Scholar
  23. 23.
    Liu, K. (2016). Optimization algorithm of cognitive radio spectrum sensing based on quantum neural network. Automatic Control and Computer Sciences, 50(5), 324–331.CrossRefGoogle Scholar
  24. 24.
    Sun, M., Zhao, C., Yan, S., & Li, B. (2017). A novel spectrum sensing for cognitive radio networks with noise uncertainty. IEEE Transactions on Vehicular Technology, 66(5), 4424–4429.CrossRefGoogle Scholar
  25. 25.
    Yang, H., Liang, Y., Miao, J., & Zhao, D. (2017). Radio spectrum management for cognitive radio based on fuzzy neural methodology. Part of the Advances in Intelligent Systems and Computing book series (AISC), 611, 609–616.CrossRefGoogle Scholar
  26. 26.
    Huang, H., & Yuan, C. (2018). Cooperative spectrum sensing over generalized fading channels based on energy detection. China Communications, 15(5), 128–137.CrossRefGoogle Scholar
  27. 27.
    Lee, K., Yoon, C., Jo, O., & Lee, W. (2018). Joint optimization of spectrum sensing and transmit power in energy harvesting-based cognitive radio networks. IEEE Access, 6, 30653–30662.CrossRefGoogle Scholar
  28. 28.
    Ivanov, A., Mihovska, A., & Tonchev, K. (2018). Real-time adaptive spectrum sensing for cyclostationary and energy detectors. IEEE Aerospace and Electronic Systems Magazine, 33(5–6), 20–33.CrossRefGoogle Scholar
  29. 29.
    Zhao, D., Wang, B., & Liu, D. (2013). A supervised actor–critic approach for adaptive cruise control. Soft Computing, 17(11), 2089–2099.CrossRefGoogle Scholar
  30. 30.
    Du, D., & Fei, M. (2008). A two-layer networked learning control system using actor–critic neural network. Applied Mathematics and Computation, 205(1), 26–36.MathSciNetCrossRefGoogle Scholar
  31. 31.
    Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17, 4831–4845.MathSciNetCrossRefGoogle Scholar
  32. 32.
    Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67.CrossRefGoogle Scholar
  33. 33.
    Guimarães, D. A., da Silva, C. R. N., & de Souza, R. A. A. (2013). Cooperative spectrum sensing using eigenvalue fusion for OFDMA and other wideband signals. Journal of Sensor and Actuator Networks, 2, 1–24.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.PES’s Modern College of Engineering, SPPUPuneIndia
  2. 2.D Y Patil College of Engineering, SPPUAkurdi, PuneIndia

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