Skip to main content
Log in

Optimized Flexible Power Selection for Opportunistic Underlay Cognitive Radio Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

A flexible transmit power selection concept for underlay cognitive users is proposed in this paper. We have employed an opportunistic, sensing based spectrum sharing method. Besides the power constraint to avoid interference at PU, the transmit power constraints of secondary user is also considered. Received Signal Strength Indicator based carrier selection method has been adopted. To resolve hidden terminal problem, twin scan concept is used at both ends (secondary transmitter and receiver) with same carrier frequency. Secondary transmitter selects suitable carrier frequency to initiate communication with the minimum power level as defined by the proposed algorithm. If received signal strength at the corresponding secondary receiver is below the predefined required receiver threshold, then power level is stepped up automatically. To maximize secondary user channel capacity, we have considered flexible power selection strategy as per channel state information. If the cognitive receiver is unable to recover the received information, even with the peak transmit power, it will again perform the frequency scanning operation. This is repeated till the best result is achieved. A power control circuit is designed to check the power selection concept.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Mitola, J. (2000). Cognitive radioan integrated agent architecture for software defined radio. PhD Dissertation, KTH, Stockholm, Sweden.

  2. Zhao, Q., & Sadler, B. M. (2007). A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 24(3), 79–89.

    Article  Google Scholar 

  3. Le, L. B., & Hossain, E. (2008). Resource allocation for spectrum underlay in cognitive radio networks. IEEE Transactions on Wireless Communications, 7(12), 5306–5315.

    Article  Google Scholar 

  4. Kang, X., Liang, Y. C., Garg, H. K., & Zhang, L. (2009). Sensing-based spectrum sharing in cognitive radio networks. IEEE Transactions on Vehicular Technology, 58(8), 4649–4654.

    Article  Google Scholar 

  5. Badawy, A., & Khattab, T. (2013, October). A hybrid spectrum sensing technique with multiple antenna based on GLRT. In Wireless and Mobile Computing, Networking and Communications (WiMob), 2013 IEEE 9th International Conference on (pp. 736–742). Lyon, France: IEEE. doi:10.1109/WiMOB.2013.6673438.

  6. Yongjun, X., & Xiaohui, Z. (2013). Optimal power allocation for multiuser underlay cognitive radio networks under QoS and interference temperature constraints. China Communications, 10(10), 91–100.

    Article  Google Scholar 

  7. Liu, Z., Wang, P., Xia, Y., Yang, H., & Guan, X. (2016). Chance-constraint optimization of power control in cognitive radio networks. Peer-to-Peer Networking and Applications, 9(1), 245–253.

    Article  Google Scholar 

  8. Yao, H., Zhou, Z., Liu, H., & Zhang, L. (2009, June). Optimal power allocation in joint spectrum underlay and overlay cognitive radio networks. In Cognitive Radio Oriented Wireless Networks and Communications, 2009 (CROWNCOM’09) 4th International Conference on (pp. 1–5) Germany: IEEE, Courtyard Hannover Maschsee. doi:10.1109/CROWNCOM.2009.5189342.

  9. Wang, Y., Ren, P., Du, Q., & Sun, L. (2015). Optimal power allocation for underlay-based cognitive radio networks with primary user’s statistical delay QoS provisioning. IEEE Transactions on Wireless Communications, 14(12), 6896–6910.

    Article  Google Scholar 

  10. Gu, J., & Jeon, W. S. (2013). Optimal power allocation in an “off” spectrum sensing interval for cognitive radio. IEEE Communications Letters, 17(10), 1908–1911.

    Article  Google Scholar 

  11. Benaya, A. M., Shokair, M., El-Rabaie, E. S., & Elkordy, M. F. (2015). Optimal power allocation for sensing-based spectrum sharing in MIMO cognitive relay networks. Wireless Personal Communications, 82(4), 2695–2707.

    Article  Google Scholar 

  12. Chen, Y., Lei, Q., & Yuan, X. (2014). Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks. Wireless Personal Communications, 79(3), 1647–1664.

    Article  Google Scholar 

  13. Oh, J., & Choi, W. (2010, September). A hybrid cognitive radio system: A combination of underlay and overlay approaches. In Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd (pp. 1–5). Ottawa, Ontario, Canada: IEEE. doi: 10.1109/VETECF.2010.5594302.

  14. Qiu, T., Xu, W., Song, T., He, Z., & Tian, B. (2011, May). Energy-efficient transmission for hybrid spectrum sharing in cognitive radio networks. In Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd (pp. 1–5). Budapest, Hungary: IEEE. doi: 10.1109/VETECS.2011.5956224.

  15. Lan, P., Sun, F., Chen, L., Xue, P., & Hou, J. (2013). Power allocation and relay selection for cognitive relay networks with primary QoS constraint. IEEE Wireless Communications Letters, 2(6), 583–586.

    Article  Google Scholar 

  16. Lee, C. H., & Haenggi, M. (2012). Interference and outage in Poisson cognitive networks. IEEE Transactions on Wireless Communications, 11(4), 1392–1401.

    Article  Google Scholar 

  17. Xing, Y., Mathur, C. N., Haleem, M. A., Chandramouli, R., & Subbalakshmi, K. P. (2007). Dynamic spectrum access with QoS and interference temperature constraints. IEEE Transactions on Mobile Computing, 6(4), 423–433.

    Article  Google Scholar 

  18. 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 

  19. Bepari, D., & Mitra, D. (2014, February). GA based optimal power allocation for underlay cognitive radio networks. In Electronics and Communication Systems (ICECS), 2014 International Conference on (pp. 1–6). Coimbatore, India: IEEE. doi:10.1109/ECS.2014.6892554.

  20. Rosas, A. A., Shokair, M., & El_dolil, S. A. (2015). Proposed optimization technique for maximization of throughput under using different multicarrier systems in cognitive radio networks. In The Proceedings of Second International Conference on Electronics Engineering, Clean Energy and Green Computing (EEECEGC) (pp. 25–33). Konya, Turkey: Mevlana University, ISBN: 978-1-941968-12-3©2015 SDIWC.

  21. Hou, L., Yeung, K. H., & Wong, K. Y. (2015). SEER: Spectrum-and energy-efficient routing protocol for cognitive radio ad hoc networks. Wireless Networks, 21(7), 2357–2368.

    Article  Google Scholar 

  22. Lan, P., Chen, L., Zhang, G., & Sun, F. (2015). Optimal resource allocation for cognitive radio networks with primary user outage constraint. EURASIP Journal on Wireless Communications and Networking, 2015(1), 239.

    Article  Google Scholar 

  23. 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 

  24. Bala, I., Bhamrah, M. S., & Singh, G. (2015). Capacity in fading environment based on soft sensing information under spectrum sharing constraints. Wireless Networks, 23(2), 1–13.

    Google Scholar 

  25. Ozcan, G., & Gursoy, M. C. (2015). Optimal power control for underlay cognitive radio systems with arbitrary input distributions. IEEE Transactions on Wireless Communications, 14(8), 4219–4233.

    Article  Google Scholar 

  26. Quan, Z., Cui, S., & Sayed, A. H. (2008). Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal of Selected Topics in Signal Processing, 2(1), 28–40.

    Article  Google Scholar 

  27. Liu, X., Jia, M., & Tan, X. (2013). Threshold optimization of cooperative spectrum sensing in cognitive radio networks. Radio Science, 48(1), 23–32.

    Article  Google Scholar 

  28. Zhao, Y., Li, S., Zhao, N., & Wu, Z. (2010). A novel energy detection algorithm for spectrum sensing in cognitive radio. Information Technology Journal, 9(8), 1659–1664.

    Article  Google Scholar 

  29. Atapattu, S., Tellambura, C., & Jiang, H. (2011, June). Spectrum sensing via energy detector in low SNR. In Communications (ICC), 2011 IEEE International Conference on (pp. 1–5). Japan: IEEE, Kyoto International Conference Centre. doi: 10.1109/icc.2011.5963316.

  30. Zhang, W., Mallik, R. K., & Letaief, K. B. (2009). Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Transactions on Wireless Communications, 8(12), 5761–5766.

    Article  Google Scholar 

  31. Lee, W. C. (2010). Mobile communications design fundamentals (Vol. 25). Hoboken: Wiley.

    Google Scholar 

  32. Semiconductor Components Industries, LLC. (2003). 3.3V/5V Programmable PLL Synthesized Clock Generator, NBC12429 (2003–Rev. 2) NBC12429/D, 1–2.

  33. Philips Semiconductors, SA636. (1997). Low voltage high performance mixer FM IF system with high speed RSSI, IC17 Data Handbook, Nov 07.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sabyasachi Chatterjee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chatterjee, S., Banerjee, P. & Nasipuri, M. Optimized Flexible Power Selection for Opportunistic Underlay Cognitive Radio Networks. Wireless Pers Commun 96, 1193–1213 (2017). https://doi.org/10.1007/s11277-017-4231-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4231-2

Keywords

Navigation