Skip to main content
SpringerLink
Log in

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

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Khalid, L., & Anpalagan, A. (2010). Emerging cognitive radio technology: Principles, challenges and opportunities. Computers & Electrical Engineering, 36(2), 358–366.

    Article  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  3. Rondeau, T. W., & Bostian, C. W. (2009). Artificial intelligence in wireless communications. Noorwood: Artech House.

    MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  8. Zhao, N., Li, S., & Wu, Z. (2012). Cognitive radio engine design based on ant colony optimization. Wireless Personal Communications, 65(1), 15–24.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. https://doi.org/10.1155/2016/9878475.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  13. Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274(2), 292–305.

    Article  MathSciNet  MATH  Google Scholar 

  14. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  17. Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29(5), 464–483.

    Article  Google Scholar 

  18. Yang, X. S. (2013). Bat algorithm: Literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  25. Mandal, J. K., Mukhopadhyay, S., & Pal, T. (2016). Handbook of research on natural computing for optimization problems. IGI Global, Pennsylvania: Information science reference.

    Book  Google Scholar 

  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.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, India

    Avneet Kaur, Surbhi Sharma & Amit Mishra

Authors
  1. Avneet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surbhi Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avneet Kaur.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, A., Sharma, S. & Mishra, A. A Novel Jaya-BAT Algorithm Based Power Consumption Minimization in Cognitive Radio Network. Wireless Pers Commun 108, 2059–2075 (2019). https://doi.org/10.1007/s11277-019-06509-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06509-5

Keywords

Search

Navigation