Skip to main content
Springer Nature Link
Log in

Optimal Mode Selection Policies in Cognitive Radio Networks with RF Energy Harvesting

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Applying energy harvesting technology in cognitive radio networks (CRNs) leads to a tradeoff between the time allocated for spectrum sensing followed by spectrum accessing and that for energy harvesting. This tradeoff can be formulated as a mode selection problem for the secondary users. In this paper, we consider a CRN working in the time-slotted manner. The secondary users powered by radio frequency energy harvesting can perform overlay transmission or cooperate with the primary users. To maximize the long-term throughput of the secondary network, we propose two optimal mode selection policies by formulating this problem under a partially observable Markov decision process framework. Numerical simulations show that both of our proposed policies achieve more throughput than the overlay-only policy. Finally, we also evaluate the effect of the cooperative threshold and the energy harvesting process on the optimal policies.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Shusen, Y., Yad, T., Po-yu, C., Alan, M., & Julie, M. (2016). Distributed optimization in energy harvesting sensor networks with dynamic in-network data processing. In IEEE International Conference on Computer Communications (INFOCOM). https://doi.org/10.1109/INFOCOM.2016.7524475.

  2. Cong, W., Ji, L., Yuanyuan, Y., & Ye, F. (2016). A hybrid framerwork combining solar energy harvesting and wireless charging for wireless sensor networks. In IEEE International Conference on Computer Communications (INFOCOM). https://doi.org/10.1109/INFOCOM.2016.7524337.

  3. Xiao, L., Ian, F., Dusit, N., & Nicolas, P. (2016). Self-sustainable communications with RF energy harvesting: Ginibre point process modeling and analysis. IEEE Journal on Selected Areas Communications, 34(5), 1518–1535.

    Article  Google Scholar 

  4. Mohjazi, L., Dianati, M., Karagiannidis, G. K., & Muhaidat, S. (2015). RF-powered cognitive radio networks: Technical challenges and limitations. IEEE communications Magazine, 53(4), 94–100.

    Article  Google Scholar 

  5. Xiao, L., Ping, W., Dusit, N., Dong, I. K., & Zhu, H. (2014). Wireless networks with RF energy harvesting: A contemporary survey. IEEE Communications Surveys & Tutorials, 17(2), 757–789.

    Google Scholar 

  6. Federal Communications Commission (FCC) (2003). Et docket No. 03-222 notice of proposed rule making and order.

  7. Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.

    Article  MATH  Google Scholar 

  8. Ahmed, S. (2012). Sensing and transmit energy optimization for an energy harvesting cognitive radio. IEEE Wireless Communications Letters, 1(5), 500–503.

    Article  Google Scholar 

  9. Hoang, A. T., Yingchang, L., Wong, D. T. C., Yonghong, Z., & Rui, Z. (2009). Opportunistic spectrum access for energy-constrained cognitive radios. IEEE Transactions on Wireless Communications, 8(3), 1206–1211.

    Article  Google Scholar 

  10. Mao, S., Cheung, M. H., & Wong, V. W. S. (2012). An optimal energy allocation algorithm for energy harvesting wireless sensor networks. IEEE International Conference on Communications, 11(18), 265–270.

    Google Scholar 

  11. Muhammad, U., & Insoo, K. (2014). Throughput maximization of the cognitive radio using hybrid (overlay–underlay) approach with energy harvesting. In International Conference on Frontiers of Information Technology (pp. 22–27).

  12. Muhammad, U., & Insoo, K. (2014). Access strategy for hybrid underlay–overlay cognitive radios with energy harvesting. IEEE Sensors Journal, 14(9), 3164–3173.

    Article  Google Scholar 

  13. Wenjun, X., Yan, W., Kai, N., Zhiqiang, H., & Jiaru, L. (2015). Joint overlay and underlay resource allocation with weighted fairness in OFDM-based cognitive radio systems. International Journal of Communication Systems, 28(10), 1692–1708.

    Article  Google Scholar 

  14. Juhi, G., Vikram, K., & Vivek, K. D. (2015). Joint overlay–underlay optimal power allocation in cognitive radio. Wireless Personal Communications, 83(3), 1–12.

    Google Scholar 

  15. Jin, Z., & Qian, Z. (2009). Stackelberg game for utility-based cooperative cognitive radio networks. In ACM International Symposium on Mobile Ad Hoc Networking & Computing (pp. 23–32).

  16. Sungsoo, P., & Daesik, H. (2014). Achievable throughput of energy harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 13(2), 1010–1022.

    Article  Google Scholar 

  17. Sungsoo, P., Jihaeng, H., Beomju, K., Wonsuk, C., Hano, W., Daesik, H. (2012). Optimal mode selection for cognitive radio sensor networks with RF energy harvesting. In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (Vol. 11(4), pp. 2155–2159).

  18. Wild, B., & Ramchandran, K. (2005). Detecting primary receivers for coginitive radio applications. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (pp. 124–130).

  19. Jeya, P. J., Sanket, S. K., & Adrish, B. (2014). Energy harvesting cognitive radio with channel-aware sensing strategy. IEEE Communications Letters, 18(7), 1171–1174.

    Article  Google Scholar 

  20. Sungsoo, P., Seokwon, L., Beomju, K., Daesik, H., Jemin, L. (2011). Energy-efficient opportunistic spectrum access in cognitive radio networks with energy harvesting. In International Conference on Cognitive Radio and Advanced Spectrum Management. https://doi.org/10.1145/2093256.2093318.

  21. Shixin, L., Rui, Z., & Tengjoon, L. (2012). Optimal save-then-transmit protocol for energy harvesting wireless transmitters. IEEE Transactions on Wireless Communications, 12(3), 1196–1207.

    Google Scholar 

  22. Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1–2), 99–134.

    Article  MathSciNet  MATH  Google Scholar 

  23. Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1), 99–134.

    Article  MathSciNet  MATH  Google Scholar 

  24. Bertsekas, D. P. (2001). Dynamic programming and optimal control (2nd ed., Vols. 1–2). Belmont, MA: Athena Scientific.

    MATH  Google Scholar 

  25. Cassandra, A., Littman, M. L., Zhang, N. L. (2013). Increnebtal prunting: A simple, fast, exact method for partially observable markov decision processes. In Thirteenth Conference on Uncertainty in Artificial Intelligence (pp. 54–61).

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China

    Tao Jing, Jinlu Ding, Fan Zhang & Yan Huo

Authors
  1. Tao Jing
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jinlu Ding
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yan Huo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jinlu Ding.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jing, T., Ding, J., Zhang, F. et al. Optimal Mode Selection Policies in Cognitive Radio Networks with RF Energy Harvesting. Wireless Pers Commun 98, 3319–3334 (2018). https://doi.org/10.1007/s11277-017-5016-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-5016-3

Keywords