Abstract
In this paper, a challenge issue ‘sensing and data transmission trade-off’ is addressed. Different from existing efforts, penalty is taken as a rule of regulation for spectrum sensing and access in order to possibly protect primary’s activity. Specifically, secondary user needs to pay rental fee to primary user for opportunistic access. However, penalty will be charged from secondary user as well if it has transmitted on the miss estimated spectrum, in which, an negative secondary raw utility will be generated when the penalty value is properly decided. Thus the scheme will further force secondary user to improve its sensing accuracy and select the best transmission strategy when compared with conventional sensing access model. Based on the scheme, in this research, the bounds for proper rental and penalty prices are derived. Further, within both proper prices region, an optimization problem for setting sensing duration and transmission power is formulated. Through investigation, convexity of the problem is hard to be obtained at global view. But thanks to the special property that the problem is local convex when fixing any one of targeted variables, a Lagrange method based iterative algorithm is presented to find solution within a decent convergence of no more than 10 iterations.






Similar content being viewed by others
Notes
In this research work, we assume the inventory of channels is sufficient and sensing access of each channel is independent. Based on this, for the ease of analysis, the case as one primary owner and one secondary renter with a single licensed channel is employed as a typical example.
Assuming there exists a common channel between coordinator and secondary user for information interaction.
References
Chandler, D., & Munday, R. (2011). A dictionary of media and communication (first ed. ed.). Oxford: Oxford University Press.
Mitola, J. (1999). Cognitive radio for flexible mobile multimedia communications. In Proceedings of IEEE International Workshop on Mobile Multimedia Communications.
Molisch, A. F. (2011). Wireless communications (second edition ed.). Hoboken: Wiley Press.
Liang, Y. C., Chen, K. C., Li, G. Y., & Mahonen, P. (2011). Cognitive radio networking and communications: An overview. IEEE Transactions on Vehicular Technology, 60(7), 3386–3407.
Mitola, J., & Maguire, G. Q, Jr. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.
Sofotasios, P. C., Rebeiz, E., Zhang, L., Tsiftsis, T. A., Cabric, D., & Freear, S. (2013). Energy detection based spectrum sensing over k \(-\mu\) and k \(-\mu\) extreme fading channels. IEEE Transactions on Vehicular Technology, 62(3), 1031–1040.
Quan, Z., Zhang, W., Shellhammer, S. J., & Sayed, A. H. (2012). Optimal spectral feature detection for spectrum sensing at very low SNR. IEEE Transactions on Wireless Communications, 11(1), 97–107.
Li, S., Zheng, Z., Ekici, E., & Shroff, N. (2014). Maximizing system throughput by cooperative sensing in cognitive radio networks. IEEE/ACM Transactions on Networking, 22(4), 1245–1256.
Soltanmohammadi, E., Orooji, M., & Naraghi-Pour, M. (2013). Improving sensing throughput tradeoff for cognitive radio in Rayleigh fading channels. IEEE Transactions on Vehicular Technology, 62(5), 2118–2130.
Cacciapuoti, A. S., Akyildiz, I. F., & Paura, L. (2013). Optimal primary-user mobility aware spectrum sensing design for cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2161–2172.
Hamdi, K., & Letaief, K. B. (2009). Power, sensing time, and throughput tradeoffs in cognitive radio systems: A cross-layer approach. In IEEE Wireless Communications and Networking Conference, pp. 1–5.
Cao, Y., Shi, Q., Wang, X., Tian, X. & Cheng, Y. (2012). Two-dimensional contract theory in Cognitive Radio networks. In IEEE Global Communication Conference, 1156–1161.
Wang, Z., Jiang, L., & He, C. (2014). Optimal price-based power control algorithm in cognitive radio networks. IEEE Transactions on Wireless Communications, 13(11), 5909–5920.
Pang, J., Scutari, G., Palomar, D. P., & Facchinei, F. (2010). Design of cognitive radio systems under temperature-interference constraints: A variational inequality approach. IEEE Transactions on Signal Processing, 58(8), 3251–3271.
Sengupta, S., & Chatterjee, M. (2009). An economic framework for dynamic spectrum access and service pricing. IEEE/ACM Transactions on Networking, 17(4), 1200–1213.
Zhang, Y., Zheng, J., & Chen, H. H. (2010). Cognitive radio networks: architectures, protocols, and standards. Boca Raton: CRC press.
Wang, J., Ghosh, M., & Challapali, K. (2011). Emerging cognitive radio applications: A survey. IEEE Communications Magazine, 49(3), 74–81.
Fan, R., Jiang, H., Guo, Q., & Zhang, Z. (2011). Joint optimal cooperative sensing and resource allocation in multichannel cognitive radio networks. IEEE Transactions on Vehicular Technology, 60(2), 722–729.
Zhu, K., Niyato, D., Wang, P., & Han, Z. (2012). Dynamic spectrum leasing and service selection in spectrum secondary market of cognitive radio networks. IEEE Transactions on Wireless Communications, 11(3), 1136–1145.
Tran, N. H., Hong, C. S., Han, Z., & Lee, S. (2013). Optimal pricing effect on equilibrium behaviors of delay-sensitive users in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2566–2579.
Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.
Liang, Y. C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.
Kobayashi, H., Mark, B. L., & Turin, W. (2012). Probability, Random Processes, and Statistical Analysis. Cambridge: United Kingdom at the University Press.
Gao, L., Wang, X., Xu, Y., & Zhang, Q. (2011). Spectrum trading in cognitive radio networks: A contract-theoretic modeling approach. IEEE Journal on Selected Areas in Communications, 29(4), 843–855.
Fan, R., & Jiang, H. (2010). Optimal multi-channel cooperative sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 9(3), 1128–1138.
Boyd, S. P., & Vandenberghe, L. (2004). Convex Optimization. Cambridge: Cambridge University Press.
Mark, J. W., & Zhuang, W. (2003). Wireless Communications and Networking. Prentice Hall, ISBN 0-13-040905-7.
Webb, J. N. (2007). Game theory: decisions, interaction and Evolution. Berlin: Springer Science & Business Media.
Acknowledgments
The authors would like to thank Dr. H. Jiang from University of Alberta as well as reviewers for their suggestions to improve quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zheng, Y., Zheng, L. Sensing Transmission Tradeoff Over Penalty for Miss Detection in Cognitive Radio Network. Wireless Pers Commun 92, 1089–1105 (2017). https://doi.org/10.1007/s11277-016-3594-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-016-3594-0