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Throughput Maximization of Cognitive Radio Under the Optimization of Sensing Duration

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Abstract

For a CR system operating under the energy detection scheme of spectrum sensing, it is much important to have low values of probability of false alarm \(P_{fa}\) and probability of missed detection \(P_{md}\). However, due to inherent tradeoff between \(P_{fa}\) and \(P_{md}\), it is challenging to simultaneously achieve high value of throughput while maintaining a sufficient level of protection to the licensed users. To overcome this challenge, this paper proposes a CR system which while operating under the double threshold scheme does not only promise a high value of throughput but it also ensures a target level of protection to the licensed users. We further study the problem of designing a sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected. We formulate the sensing-throughput tradeoff problem mathematically, and prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput for the secondary network. To overcome uncertainties of the wireless communication channel, the proposed approach was further been implemented under the cooperation of \(n - out - of - k\) CRs. It is observed that, under the proposed approach, the CR user while ensuring a target level of protection to the licensed users achieves a significant gain in throughput than the CR systems based on the single and double thresholds both.

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Notes

  1. The partial underlay scheme is different from the basic underlay scheme [10, 11] of spectrum access as it does not always transmit using low power. But, unlike to underlay scheme where no sensing is needed, under the proposed system, the sensing is performed in alternate slots using the frame structure of Fig. 1. It is also different from the sensing based spectrum sharing (SSS) scheme [12, 13] where communication is performed under the low and high transmission powers based on the sensed status (active/idle) of the channel. But, in the proposed system, the low power transmissions are done for the confused region only, while, for the busy status of the channel, the channel search operation is initiated.

  2. Transmit with a power which is needed to ensure a sufficient data rate. The transmissions are performed as if SU itself was a licensed user.

  3. The transmissions are done at such a low power which do not interfere any ongoing communication of the PU. In other words, the spectrum access resembles to the underlay approach of spectrum sharing [10, 11].

References

  1. Federal Communications Commission. (2002). Spectrum policy task force report. FCC 02-155.

  2. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.

    Article  Google Scholar 

  3. Mitola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Ph.D. diss., Royal Institute of Technology, Stockholm, Sweden.

  4. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23, 201–220.

    Article  Google Scholar 

  5. Liang, Y. C., Zeng, Y., et al. (2007). Sensing throughput tradeoff for cognitive radio networks. IEEE International Conference on Communications (ICC), 7, 5330–5335.

    Google Scholar 

  6. Liang, Y. C., Zeng, Y., et al. (2008). Sensing throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.

    Article  Google Scholar 

  7. Stotas, S., & Nallanathan, A. (2010). Overcoming the sensing-throughput tradeoff in cognitive radio networks. IEEE International Conference on Communications (ICC) (pp. 1–5). doi:10.1109/ICC.2010.5502792.

  8. Stotas, S., & Nallanathan, A. (2012). On the throughput and spectrum sensing enhancement of opportunistic spectrum access cognitive radio networks. IEEE Transactions on Wireless Communications, 11(1), 97–101.

    Article  Google Scholar 

  9. Pandit, S., & Singh, G. (2013). Throughput maximization with reduced data loss rate in cognitive radio network. Telecommunication Systems. doi:10.1007/s11235-013-9858-z.

    Google Scholar 

  10. Zhang, S., et al. (2009). A low-overhead energy detection based cooperative sensing protocol for cognitive radio systems. IEEE Transactions on Wireless Communications, 8(11), 5575–5581.

    Article  Google Scholar 

  11. Shen, J., et al. (2008). Optimisation of cooperative spectrum sensing in cognitive radio network. IET Communications, 3(7), 1170–1178.

    Article  Google Scholar 

  12. Ghasemi, A., & Sousa, E. S. (2007). Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communications, 6(2), 649–658.

    Article  Google Scholar 

  13. Musavian, L., & Aissa, S. (2007). Ergodic and outage capacities of spectrum-sharing systems in fading channels. In Proceedings of IEEE global telecommunications (GLOBECOM) conference (pp. 3327–3331).

  14. Stotas, S., & Nallanathan, A. (2011). Enhancing the capacity of spectrum sharing cognitive radio networks. IEEE Transactions on Vehicular Technology, 60(8), 3768–3779.

    Article  Google Scholar 

  15. Kang, X., et al. (2009). Sensing based spectrum sharing in cognitive radio networks. IEEE Transactions on Vehicular Technology, 58(8), 4649–4654.

    Article  Google Scholar 

  16. Wu, J. et al. (2009). An energy detection algorithm based on double-threshold in cognitive radio systems. In IEEE 1st international conference on information science and engineering (ICISE) (pp. 493–496). doi:10.1109/ICISE.2009.257.

  17. Xie, J. Q., & Chen, J. (2012). An adaptive double-threshold spectrum sensing algorithm under noise uncertainty. In IEEE 1st international conference on information science and engineering (ICISE) (pp. 824–827). doi:10.1109/CIT.2012.171.

  18. Liu, S.-Q., et al. (2012). Hierarchical cooperative spectrum sensing based on double thresholds energy detection. IEEE Communications Letters, 16(7), 1096–1099.

    Article  Google Scholar 

  19. Zhu, J. & et al. (2008). Double threshold energy detection of cooperative spectrum sensing in cognitive radio. In IEEE, cognitive radio oriented wireless networks and communications (CROWNCOM) (pp. 1–5). doi:10.1109/CROWNCOM.2008.4562451.

  20. Jafarian, J. & Hamdi, K., A. (2012). Throughput optimization in a cooperative double-threshold sensing scheme. In IEEE, wireless communications and networks (WCNC) (pp. 1034-1038). doi:10.1109/WCNC.2012.6213925.

  21. Zhang, W., & Lataief, K. B. (2008). Cooperative spectrum sensing with transmit and relay diversity in cognitive radio networks. IEEE Transactions on Wireless Communications, 7(12), 4761–4766.

    Article  Google Scholar 

  22. Ghasemi. A. et al. (2005). Collaborative spectrum sensing for opportunistic spectrum access in fading environments. In Proceedings of 1st IEEE international symposium on new frontiers in dynamic spectrum access networks, Baltimore, USA (pp. 131–136).

  23. Wang, Y., et al. (2014). Optimization of cluster-based cooperative spectrum sensing scheme in cognitive radio networks with soft data fusion. Wireless Personal Communications. doi:10.1007/s11277-014-1673-7.

    Google Scholar 

  24. Sun, C. et al. (2007). Cooperative spectrum sensing for cognitive radios under bandwidth constraints. In Proceedings of IEEE wireless communications and networking conference, Hong Kong, China (pp. 1–5).

  25. Zhang, W., et al. (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 

  26. Peh, E. C. Y., et al. (2009). Optimization of cooperative sensing in cognitive radio networks: a sensing-throughput tradeoff view. IEEE Transactions on Vehicular Technology, 58(9), 5294–5299.

    Article  Google Scholar 

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Authors and Affiliations

  1. Electronics and Communication Engineering, National Institute of Technology Kurukshetra (NITKKR), Kurukshetra, Haryana, India

    Gaurav Verma & O. P. Sahu

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  1. Gaurav Verma
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Correspondence to Gaurav Verma.

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Verma, G., Sahu, O.P. Throughput Maximization of Cognitive Radio Under the Optimization of Sensing Duration. Wireless Pers Commun 97, 1251–1266 (2017). https://doi.org/10.1007/s11277-017-4564-x

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