Abstract
In this paper, the cognitive radio sensor node can harvest energy from the radio frequency signal which is transmitted by the primary user. A time switching protocol was used to divide cognitive users’ time into three phases: spectrum sensing mode, energy harvesting mode and data transmission mode. Therefore, the optimal energy harvesting mode time selection is a question to be solved. We consider a non-cooperative game model in which cognitive users are regarded as selfish players aiming to maximize their own energy efficiency. Then we prove the existence and uniqueness of Nash Equilibrium. A distributed best response algorithm is used to obtain the Nash Equilibrium. The simulation results prove that this algorithm can converge to the same equilibrium from different initial values. At last, we analysis the influence of various system parameters on the results of Nash Equilibrium and energy efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ku, M.L., Li, W., Chen, Y., et al.: Advances in energy harvesting communications: past, present, and future challenges. IEEE Commun. Surv. Tutorials 18(2), 1384–1412 (2016)
Nasir, A.A., Zhou, X., Durrani, S., et al.: Relaying protocols for wireless energy harvesting and information processing. IEEE Trans. Wirel. Commun. 12(7), 3622–3636 (2012)
Mousavifar, S.A., Liu, Y., Leung, C., et al.: Wireless Energy Harvesting and Spectrum Sharing in Cognitive Radio, pp. 1–5 (2014)
He, J., Guo, S., Wang, F., et al.: Relay selection and outage analysis in cooperative cognitive radio networks with energy harvesting. In: ICC 2016 IEEE International Conference on Communications, pp. 1–6. IEEE (2016)
Chen, H., Li, Y., Jiang, Y., et al.: Distributed power splitting for SWIPT in relay interference channels using game theory. IEEE Trans. Wirel. Commun. 14(1), 410–420 (2015)
Elmorshedy, L., Leung, C., Mousavifar, S.A.: RF energy harvesting in DF relay networks in the presence of an interfering signal. In: ICC 2016 IEEE International Conference on Communications, pp. 1–6. IEEE (2016)
Chai, B., Deng, R., Cheng, P., et al.: Energy-efficient power allocation in cognitive sensor networks: a game theoretic approach. In: Global Communications Conference, pp. 416–421. IEEE (2012)
Zhang, D., Chen, Z., Awad, M.K., et al.: Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks. IEEE J. Sel. Areas Commun. 34(12), 3552–3565 (2016)
Park, S., Heo, J., Kim, B., et al.: Optimal mode selection for cognitive radio sensor networks with RF energy harvesting. In: IEEE, International Symposium on Personal Indoor and Mobile Radio Communications, pp. 2155–2159. IEEE (2012)
Wei, Y., Ren, C., Song, M., Yu, R.: The offloading model for green base stations in hybrid energy networks with multiple objectives. Int. J. Commun Syst. 29(11), 1805–1816 (2016)
Wei, Y., Wang, X., Fialho, L., Bruschi, R., Ormond, O., Collier, M.: Hierarchical power management architecture and optimal local control policy for energy efficient networks. J. Commun. Netw. 18(4), 540–550 (2016)
Lasaulce, S., Debbah, M., Altman, E.: Methodologies for analyzing equilibria in wireless games. Signal Proc. Mag. IEEE 26(5), 41–52 (2009)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61571059), and the State Major Science and Technology Special Projects of China under Grant 2016ZX03001017-004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Zhao, M., Wei, Y., Li, Q., Song, M., Liu, N. (2018). Energy Harvesting Time Coefficient Analyze for Cognitive Radio Sensor Network Using Game Theory. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-74521-3_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74520-6
Online ISBN: 978-3-319-74521-3
eBook Packages: Computer ScienceComputer Science (R0)