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Joint Power Control and Adaptive Sleeping Policy for Cooperative Spectrum Sensing in CRN

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Abstract

In Cognitive Radio Network (CRN), as maximizing the throughput may increase the energy consumption, the spectrum sensing policy should balance the energy and throughput. So as to reserve the special right of the Primary User (PU), the secondary user (SU) is confronted with the tasks of the time to perceive a channel, how long would the sensing operation take, the time to shift from the channel and the time to transfer through an assigned channel. Hence in this paper, a joint power control and adaptive sleeping policy (JPCASP) is proposed for CRN. In this method, the maximum admissible transmit power is estimated from the signal to noise ratio values measured at the PU. Using the energy detection method with double bounds, a Local Decision Rule at each SU is gathered and checked. Finally, adaptive threshold regulation technique based on the estimated throughput is applied. Experimental results show that the proposed JPCASP has higher delivery ratio with reduced delay and energy consumption.

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References

  1. Yao, C., Wu, Q., & Zhou, L. (2014). Traffic based optimization of spectrum sensing in cognitive radio networks. Hindawi Publishing Corporation, Mathematical Problems in Engineering, 2014, 349350.

    Google Scholar 

  2. Lee, H. S., Ahmed, M. E., & Kim, D. I. (2018) Traffic-aware optimal spectrum sensing policy in wireless-powered cognitive radio networks, In: Proceedings of Še 12th International Conference on Ubiquitous Information Management and Communication, Langkawi, Malaysia, J 5–7, 2018 (IMCOM ‘18), pp 7.

  3. Jin, Z., Qiao, Y., Liu, A., & Zhang, L. (2018). EESS: an energy-efficient spectrum sensing method by optimizing spectrum sensing node in cognitive radio sensor networks. Hindawi, Wireless Communications and Mobile Computing, 2018, 9469106.

    Google Scholar 

  4. Durowoju, O., Arshad, K., & Moessner, K. (2012). Distributed power control algorithm for cognitive radios with primary protection via spectrum sensing under user mobility. Elsevier, Ad Hoc Networks, 10(2012), 740–751.

    Article  Google Scholar 

  5. Gao, Y., Deng, Z., Choi, D., & Choi, C. (2017). Combined pre-detection and sleeping for energy-efficient spectrum sensing in cognitive radio network. Journal of Parallel Distributed Computing, 114, 85–94.

    Article  Google Scholar 

  6. Zhang, F., Jing, T., Huo, Y., & Ma, L. (2018), Optimal spectrum sensing-access policy in energy harvesting cognitive radio sensor networks., In: Elsevier, 2017 International Conference on Identification, Information and Knowledge in the Internet of Things.

  7. Shah, H. A., & Koo, I. (2018). Reliable machine learning based spectrum sensing in cognitive radio networks. Hindawi, Wireless Communications and Mobile Computing, 2018, 5906097.

    Google Scholar 

  8. Ramchandran, M., & Ganesh, E. N. (2020). Energy efficient and interference-aware spectrum sensing technique for improving the throughput in cognitive radio networks. IOP Conference Series: Materials Science and Engineering, 993, 012092.

    Article  Google Scholar 

  9. Develi, I. (2020). Spectrum sensing in cognitive radio networks: threshold optimization and analysis. Wireless Com Network., 1, 1–9.

    Google Scholar 

  10. Ramchandran, M., & Ganesh, E. N. (2021). MBSO algorithm for handling energy-throughput trade-off in cognitive radio networks. The Computer Journal, 65(7), 1717–1725.

    Article  MathSciNet  Google Scholar 

  11. Liu, X., Lu, W., Ye, L., Li, F., & Zou, D. (2017). Joint resource allocation of spectrum sensing and energy harvesting in an energy-harvesting-based cognitive sensor network. Sensors, 17(3), 600. https://doi.org/10.3390/s17030600

    Article  Google Scholar 

  12. Li, Z., Liu, B., Si, J., & Zhou, F. (2017). Optimal spectrum sensing interval in energy-harvesting cognitive radio networks. IEEE Transactions on Cognitive Communications and Networking, 3(2), 190–200.

    Article  Google Scholar 

  13. Gulzar, W., Waqas, A., Dilpazir, H., Khan, A., Alam, A., & Mahmood, H. (2022). Power control for cognitive radio networks: a game theoretic approach. Wireless Personal Communications, 123, 745–759. https://doi.org/10.1007/s11277-021-09156-x

    Article  Google Scholar 

  14. Lee, K. (2021). Low-complexity transmit power control for secure communications in wireless- powered cognitive radio networks. Sensors, 21, 7837. https://doi.org/10.3390/s21237837

    Article  Google Scholar 

  15. Wang, Z., Xiao, W., Wan, X., & Fan, Z. (2019). Price-Based Power Control Algorithm in Cognitive Radio Networks via Branch and Bound. IEICE Transaction of Information & Systems, 102(3), 505–511.

    Article  Google Scholar 

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Correspondence to G. Indumathi.

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Indumathi, G., Vaithianathan, V. Joint Power Control and Adaptive Sleeping Policy for Cooperative Spectrum Sensing in CRN. Wireless Pers Commun 133, 1–13 (2023). https://doi.org/10.1007/s11277-023-10656-1

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