Skip to main content

Advertisement

SpringerLink
Log in

Joint Optimal Design of Sensing Time and Transmission Power for Maximizing Energy Efficiency in Cognitive Radio System

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The average throughput of a cognitive radio (CR) system can be increased by performing spectrum handoff, however, it may cause high energy consumption. Therefore, it is necessary to build an energy efficient CR system while performing spectrum handoff. In this paper, a joint optimization problem of sensing time and transmission power is formulated to maximize energy efficiency (EE) of a CR system subject to the constraint on sufficient protection to the primary users while considering spectrum handoff. Swarm intelligence techniques, namely particle swarm optimization (PSO), human behavior-based PSO (HPSO), and whale optimization algorithm (WOA) are used in this study to evaluate joint optimal sensing time and transmission power. The performance comparison of the three techniques is presented in terms of fitness function (EE) and average computational time. The simulation results show that the three techniques achieve a unique joint optimal sensing time and transmission power at which EE attains the highest peak; WOA outperforms PSO and HPSO in terms of average computational time. The impact of joint optimization of sensing time and transmission power, the impact of variation of target detection probability as well as the variation of number of channels on EE are discussed. A quantitative comparison is also performed between the proposed work and the existing work.

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies (ET docket no. 03-108). Technical report, Federal Communications Commission, Washington, DC, December 2003.

  2. Shin, K. G., Kim, H., Min, A. W., & Kumar, A. (2010). Cognitive radios for dynamic spectrum access: From concept to reality. IEEE Wireless Communications,17(6), 64–74.

    Article  Google Scholar 

  3. Wang, W. (2009). Spectrum sensing for cognitive radio. In 2009 third international symposium on intelligent information technology application workshops (pp. 410–412). IEEE.

  4. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication,4(1), 40–62.

    Article  Google Scholar 

  5. Lu, L., Zhou, X., Onunkwo, U., & Li, G. Y. (2012). Ten years of research in spectrum sensing and sharing in cognitive radio. EURASIP Journal on Wireless Communications and Networking. https://doi.org/10.1186/1687-1499-2012-28.

    Article  Google Scholar 

  6. Liang, Y. C., Zeng, Y., Peh, E. C., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications,7(4), 1326–1337.

    Article  Google Scholar 

  7. Li, X., Zhao, Q., Guan, X., & Tong, L. (2010). Sensing and communication tradeoff for cognitive access of continues-time markov channels. In 2010 IEEE wireless communications and networking conference (WCNC) (pp. 1–6).

  8. Song, F., Kan, C., Wu, Q., & Ding, G. (2015). Optimal cooperative spectrum sensing in interference-aware cognitive radio networks. Wireless Personal Communications,82(4), 2171–2184.

    Article  Google Scholar 

  9. Feng, D., Jiang, C., Lim, G., Cimini, L. J., Feng, G., & Li, G. Y. (2013). A survey of energy-efficient wireless communications. IEEE Communications Surveys & Tutorials,15(1), 167–178.

    Article  Google Scholar 

  10. Althunibat, S., Di Renzo, M., & Granelli, F. (2015). Towards energy-efficient cooperative spectrum sensing for cognitive radio networks: an overview. Telecommunication Systems,59(1), 77–91.

    Article  Google Scholar 

  11. Zhang, J., Zheng, F. C., Gao, X. Q., & Zhu, H. B. (2014). Sensing-energy efficiency tradeoff for cognitive radio networks. IET Communications,8(18), 3414–3423.

    Article  Google Scholar 

  12. Pei, Y., Liang, Y. C., Teh, K. C., & Li, K. H. (2011). Energy-efficient design of sequential channel sensing in cognitive radio networks: Optimal sensing strategy, power allocation, and sensing order. IEEE Journal on Selected Areas in Communications,29(8), 1648–1659.

    Article  Google Scholar 

  13. Kang, X., Liang, Y. C., Nallanathan, A., Garg, H. K., & Zhang, R. (2009). Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity. IEEE Transactions on Wireless Communications,8(2), 940–950.

    Article  Google Scholar 

  14. Kang, X., Zhang, R., Liang, Y. C., & Garg, H. K. (2011). Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint. IEEE Journal on Selected Areas in Communications,29(2), 374–383.

    Article  Google Scholar 

  15. Wu, Y., & Tsang, D. H. (2011). Energy-efficient spectrum sensing and transmission for cognitive radio system. IEEE Communications Letters,15(5), 545–547.

    Article  Google Scholar 

  16. Shi, Z., Teh, K. C., & Li, K. H. (2013). Energy-efficient joint design of sensing and transmission durations for protection of primary user in cognitive radio systems. IEEE Communications Letters,17(3), 565–568.

    Article  Google Scholar 

  17. Wang, S., Wang, Y., Coon, J. P., & Doufexi, A. (2012). Energy-efficient spectrum sensing and access for cognitive radio networks. IEEE Transactions on Vehicular Technology,61(2), 906–912.

    Article  Google Scholar 

  18. Qiu, T., Xu, W., Song, T., He, Z., & Tian, B. (2011). Energy-efficient transmission for hybrid spectrum sharing in cognitive radio networks. In 2011 IEEE 73rd vehicular technology conference (VTC Spring) (pp. 1–5).

  19. Wang, Y., Xu, W., Yang, K., & Lin, J. (2012). Optimal energy-efficient power allocation for OFDM-based cognitive radio networks. IEEE Communications Letters,16(9), 1420–1423.

    Article  Google Scholar 

  20. Ozcan, G., & Gursoy, M. C. (2014). Energy-efficient power adaptation for cognitive radio systems under imperfect channel sensing. In 2014 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 706–711).

  21. Li, L., Zhou, X., Xu, H., Li, G. Y., Wang, D., & Soong, A. (2010). Energy-efficient transmission in cognitive radio networks. In 2010 7th IEEE consumer communications and networking conference (CCNC) (pp. 1–5).

  22. Awin, F., Abdel-Raheem, E., & Ahmadi, M. (2017). Joint optimal transmission power and sensing time for energy efficient spectrum sensing in cognitive radio system. IEEE Sensors Journal,17(2), 369–376.

    Article  Google Scholar 

  23. Zhang, L., Xiao, M., Wu, G., Li, S., & Liang, Y. C. (2016). Energy-efficient cognitive transmission with imperfect spectrum sensing. IEEE Journal on Selected Areas in Communications,34(5), 1320–1335.

    Article  Google Scholar 

  24. Bayhan, S., & Alagoz, F. (2013). Scheduling in centralized cognitive radio networks for energy efficiency. IEEE Transactions on Vehicular Technology,62(2), 582–595.

    Article  Google Scholar 

  25. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural networks (ICNN) (pp. 1942–1948).

  26. Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings (pp. 69–73).

  27. Rao, S. S. (2009). Engineering optimization: Theory and practice. Hoboken: Wiley.

    Book  Google Scholar 

  28. Liu, H., Xu, G., Ding, G. Y., & Sun, Y. B. (2014). Human behavior-based particle swarm optimization. The Scientific World Journal. https://doi.org/10.1155/2014/194706.

    Article  Google Scholar 

  29. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software,95, 51–67.

    Article  Google Scholar 

  30. Pandit, S., & Singh, G. (2017). Power allocation for optimal energy efficiency in MAC protocol of cognitive radio communication systems. In Spectrum sharing in cognitive radio networks (pp. 145–157). Cham: Springer.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electronics and Communication Engineering Department, N.I.T. Silchar, Silchar, India

    Arifa Ahmed & Krishna Lal Baishnab

Authors
  1. Arifa Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krishna Lal Baishnab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arifa Ahmed.

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

Ahmed, A., Baishnab, K.L. Joint Optimal Design of Sensing Time and Transmission Power for Maximizing Energy Efficiency in Cognitive Radio System. Wireless Pers Commun 110, 1839–1857 (2020). https://doi.org/10.1007/s11277-019-06814-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06814-z

Keywords

Search

Navigation