Wireless Personal Communications

, Volume 108, Issue 4, pp 2059–2075 | Cite as

A Novel Jaya-BAT Algorithm Based Power Consumption Minimization in Cognitive Radio Network

  • Avneet KaurEmail author
  • Surbhi Sharma
  • Amit Mishra


The aim of this paper is to propose a new hybrid optimization technique, namely Jaya-BAT algorithm (JBA) and to demonstrate its application for constrained power consumption minimization in cognitive radio network considering Class B power amplifier. JBA is motivated by recently developed Jaya algorithm (JA) having good exploration ability and nature inspired BAT algorithm (BA) with good exploitation feature. In JBA, both JA and BA help each other to get away from local optimum solution and converge towards best optimal solution. The proposed algorithm when applied to different benchmark functions shows enhanced performance in comparison to other state-of-the-art metaheuristic techniques available in literature. Reconfiguration of transmission parameters for cognitive radio (CR) user supporting data transmission mode is carried out with a purpose of minimizing the power consumption while supporting different QoS requirements. The solutions show that the constrained optimization by cognitive decision module using JBA provides better results as compared to BA and JA based optimization techniques. It proves the potential of JBA as an efficient technique to be used for power consumption minimization problem in CR networks.


BAT algorithm Class B power amplifier Cognitive radio Jaya algorithm Metaheuristic optimization 



  1. 1.
    Khalid, L., & Anpalagan, A. (2010). Emerging cognitive radio technology: Principles, challenges and opportunities. Computers & Electrical Engineering, 36(2), 358–366.CrossRefGoogle Scholar
  2. 2.
    Akyildiz, I. F., Lee, W. L., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.zbMATHCrossRefGoogle Scholar
  3. 3.
    Rondeau, T. W., & Bostian, C. W. (2009). Artificial intelligence in wireless communications. Noorwood: Artech House.zbMATHGoogle Scholar
  4. 4.
    Tsiropoulos, G. I., Dobre, O. A., Ahmed, M. H., & Baddou, K. E. (2016). Radio resource allocation techniques for efficient spectrum access in cognitive radio networks. IEEE Communications Surveys & Tutorials, 18(1), 824–845.CrossRefGoogle Scholar
  5. 5.
    Pradhan, P. M., & Panda, G. (2014). Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey. Ad Hoc Networks, 17, 129–146.CrossRefGoogle Scholar
  6. 6.
    Paraskevopoulos, A., Dallas, P. I., Siakavara, K., & Goudo, S. K. (2017). Cognitive radio engine design for IoT using real-coded biogeography-based optimization and fuzzy decision making. Wireless Personal Communications, 97(2), 1–21.CrossRefGoogle Scholar
  7. 7.
    Tan, X., Zhang, H., & Hu, J. (2014). A genetic-based cognitive link decision algorithm for OFDM system. International Journal of Communication Systems, 27(10), 2309–2323.CrossRefGoogle Scholar
  8. 8.
    Zhao, N., Li, S., & Wu, Z. (2012). Cognitive radio engine design based on ant colony optimization. Wireless Personal Communications, 65(1), 15–24.CrossRefGoogle Scholar
  9. 9.
    He, A., Amanna, A., Tsou, T., Chen, X., Datla, D., Gaeddert, J., et al. (2011). Green communications: A call for power efficient wireless systems. Journal of Communications, 6(4), 340–351.CrossRefGoogle Scholar
  10. 10.
    El Misilmani, H. M., Abou-Shahine, M. Y., Nasser, Y., & Kabalan, K. Y. (2016). Recent advances on radio-frequency design in cognitive radio. International Journal of Antenna Propagation, 1–16, 9878475. Scholar
  11. 11.
    He, A., Srikanteswara, S., Bae, K. K., Newman, T. R., Reed, J. H., Tranter, W. H., Sajadieh, M., & Verhelst, M. (2009). System power consumption minimization for multichannel communications using cognitive radio. In IEEE international conference on microwaves, communications, antennas and electronic systems, Israel.Google Scholar
  12. 12.
    Pao, W. C., Chen, Y. F., & Chuang, S. Y. (2011). Efficient power allocation schemes for OFDM-based cognitive radio systems. AEU International Journal of Electronics and Communication, 65(12), 1054–1060.CrossRefGoogle Scholar
  13. 13.
    Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274(2), 292–305.MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.zbMATHCrossRefGoogle Scholar
  15. 15.
    Kaur, A., Sharma, S., & Mishra, A. (2017). Sensing period adaptation for multiobjective optimization in cognitive radio using Jaya algorithm. Electronics Letters, 53(19), 1335–1336.CrossRefGoogle Scholar
  16. 16.
    Bedeer, E., Dobre, O. A., Ahmed, M. H., & Baddour, K. E. (2014). A multiobjective optimization approach for optimal link adaptation of OFDM-based cognitive radio systems with imperfect spectrum sensing. IEEE Transactions on Wireless Communications, 13(4), 2339–2351.CrossRefGoogle Scholar
  17. 17.
    Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29(5), 464–483.CrossRefGoogle Scholar
  18. 18.
    Yang, X. S. (2013). Bat algorithm: Literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.CrossRefGoogle Scholar
  19. 19.
    Tsai, P. W., Pan, J. S., Liao, B. Y., Tsai, M. J., & Istanda, V. (2012). Bat algorithm inspired algorithm for solving numerical optimization problems. Applied Mechanics and Materials, 148–49, 134–137.Google Scholar
  20. 20.
    Rao, R. V. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7, 19–34.Google Scholar
  21. 21.
    Singh, S. P., Prakash, T., Singh, V. P., & Babu, M. G. (2017). Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Engineering Applications of Artificial Intelligence, 60(4), 35–44.CrossRefGoogle Scholar
  22. 22.
    Rao, R. V., & More, K. C. (2017). Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm. Energy Conversion and Management, 140(10), 24–35.CrossRefGoogle Scholar
  23. 23.
    Rao, R. V., & Saroj, A. (2017). Economic optimization of shell-and-tube heat exchanger using Jaya algorithm with maintenance consideration. Applied Thermal Engineering, 116(6), 473–487.CrossRefGoogle Scholar
  24. 24.
    Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Mandal, J. K., Mukhopadhyay, S., & Pal, T. (2016). Handbook of research on natural computing for optimization problems. IGI Global, Pennsylvania: Information science reference.CrossRefGoogle Scholar
  26. 26.
    Jamil, M., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimization problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150–194.zbMATHCrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringThapar Institute of Engineering and TechnologyPatialaIndia

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