Skip to main content

Advertisement

SpringerLink
Log in

Multiple Access Control in a Centralized Full-Duplex Cognitive Machine Type Network with RF Energy Harvesting

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Machine type communication connecting machines in the Internet of Things (IoT) brings massive traffic and rapidly increases the demand for radio spectrum in wireless networks. To transmit the traffic in limited spectra, a time division multiple access (TDMA) based interface with a reservation scheme, which is spectrum-efficient, energy-efficient, and deployment-economical, was proposed in cognitive radio based machine type communication. Since radio frequency (RF) energy harvesting is a way to charge stand-alone or low-power machines in IoT and full-duplex communication potentially doubles spectral efficiency, this paper proposes a multiple access control that incorporates RF energy harvesting and full-duplex communication into the TDMA based interface with a reservation scheme. The proposed multiple access control elaborately uses the full-duplex functionality with a mechanism of detecting primary users to appropriately turn on/off RF charging such that the trade-off between the interference of primary users and RF charging is balanced. Extensive simulation results show that the proposed multiple access control produces high system throughput while the interference of primary users is kept at a low level.

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

Access this article

Log in via an institution

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
Fig. 8

Similar content being viewed by others

References

  1. Xia, N., Chen, H.-H., & Yang, C.-S. (2018). Radio resource management in machine-to-machine communications—A survey. IEEE Communications Surveys and Tutorials, 20(1), 791–828.

    Article  Google Scholar 

  2. Ali, A., Hamouda, W., & Uysal, M. (2015). Next generation M2M cellular networks: Challenges and practical considerations. IEEE Communications Magazine, 53(9), 18–24.

    Article  Google Scholar 

  3. Elbayoumi, M., Kamel, M., Hamouda, W., & Youssef, A. (2020). NOMA-assisted machine-type communications in UDN: State-of-the-art and challenges. IEEE Communications Surveys and Tutorials, 22(2), 1276–1304.

    Article  Google Scholar 

  4. Ding, H., et al. (2017). Cognitive capacity harvesting networks: Architectural evolution towards future cognitive radio networks. IEEE Communications Surveys and Tutorials, 19(3), 1902–1923.

    Article  Google Scholar 

  5. Kumar, A., & Kumar, K. (2020). Multiple access schemes for cognitive radio networks: A survey. Physical Communication, 38, 100953.

    Article  Google Scholar 

  6. Rawat, P., Singh, K. D., & Bonnin, J. M. (2016). Cognitive radio for M2M and Internet of Things: A survey. Computer Communications, 94(15), 1–29.

    Article  Google Scholar 

  7. Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive radio based Internet of Things: Applications, architectures, spectrum related functionalities, and future research directions. IEEE Wireless Communications, 24(3), 17–25.

    Article  Google Scholar 

  8. Mahmood, M. R., & Matin, M. A. (2020). Current research trends on cognitive radio based Internet of Things (IoT). Towards Cognitive IoT Networks, 5–17, 2020.

    Google Scholar 

  9. Aijaz, A., & Aghvami, A. H. (2015). Cognitive machine-to-machine communications for Internet-of-Things: A protocol stack perspective. IEEE Internet of Things Journal, 2(2), 103–112.

    Article  Google Scholar 

  10. Khan, A. U., Abbas, G., Abbas, Z. H., Waqas, M., & Hassan, A. K. (2020). Spectrum utilization efficiency in the cognitive radio enabled 5G-based IoT. Journal of Network and Computer Applications, 164, 102686.

    Article  Google Scholar 

  11. Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2016). Wireless charging technologies: Fundamentals, standards, and network applications. IEEE Communications Surveys and Tutorials, 18(2), 1413–1452.

    Article  Google Scholar 

  12. Ku, M.-L., Li, W., Chen, Y., & Liu, K. J. R. (2016). Advances in energy harvesting communications: Past, present, and future challenges. IEEE Communications Surveys and Tutorials, 18(2), 1384–1412.

    Article  Google Scholar 

  13. Soyata, T., Copeland, L., & Heinzelman, W. (2016). RF energy harvesting for embedded systems: A survey of tradeoffs and methodology. IEEE Circuits and Systems Magazine, 16(1), 22–57.

    Article  Google Scholar 

  14. Ren, J., et al. (2018). RF energy harvesting and transfer in cognitive radio sensor networks: Opportunities and challenges. IEEE Communications Magazine, 56(1), 104–110.

    Article  Google Scholar 

  15. Ma, D., Lan, G., Hassan, M., Hu, W., & Das, S. K. (2020). Sensing, computing, and communications for energy harvesting IoTs: A survey. IEEE Communications Surveys and Tutorials, 22(2), 1.

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Villalonga, D. A. U., Gómez, J. T., & García, M. J. F. (2020). Optimal sensing policy for energy harvesting cognitive radio systems. IEEE Transactions on Wireless Communications, 19(6), 3826–3838.

    Article  Google Scholar 

  18. Zhang, Z., Long, K., Vasilakos, A. V., & Hanzo, L. (2016). Full-duplex wireless communications: Challenges, solutions, and future research directions. Proceedings of the IEEE, 104(7), 1369–1409.

    Article  Google Scholar 

  19. Dibaei, M., & Ghaffari, A. (2020). Full-duplex medium access control protocols in wireless networks: A survey. Wireless Networks, 26, 2825–2843.

    Article  Google Scholar 

  20. Bharadia, D., Mcmilin, E., & Katti, S. (2013). Full duplex radios. In Proceedings of the ACM SIGCOMM (pp. 375–386).

  21. Sharma, S. K., et al. (2018). Dynamic spectrum sharing in 5G wireless networks with full-duplex technology: Recent advances and research challenges. IEEE Communications Surveys and Tutorials, 20(1), 674–707. https://doi.org/10.1109/COMST.2017.2773628.

    Article  Google Scholar 

  22. Amjad, M., Akhtar, F., Rehmani, M. H., Reisslein, M., & Umer, T. (2017). Full-duplex communication in cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 19(4), 2158–2191.

    Article  Google Scholar 

  23. Rao, A. K., Singh, R. K., & Srivastava, N. (2020). Full-duplex wireless communication in cognitive radio networks: A survey. Advances in VLSI, Communication, and Signal Processing, 261–277, 2020.

    Google Scholar 

  24. Perera, T. D. P., Jayakody, D. N. K., Sharma, S. K., Chatzinotas, S., & Li, J. (2018). Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges. IEEE Communications Surveys and Tutorials, 20(1), 264–302.

    Article  Google Scholar 

  25. Varshney, L. (2008). Transporting information and energy simultaneously. In Proceedings of the IEEE international symposium on information theory (pp. 1612–1616).

  26. Zhou, X., Zhang, R., & Ho, C. K. (2013). Wireless information and power transfer: Architecture design and rate-energy tradeoff. IEEE Transactions on Communications, 61, 4754–4767.

    Article  Google Scholar 

  27. Boshkovska, E., Ng, D., Zlatanov, N., & Schober, R. (2015). Practical non-linear energy harvesting model and resource allocation for SWIPT systems. IEEE Communications Letters, 19, 2082–2085.

    Article  Google Scholar 

  28. Xu, J., Liu, L., & Zhang, R. (2014). Multiuser MISO beamforming for simultaneous wireless information and power transfer. IEEE Transactions on Signal Processing, 62, 4798–4810.

    Article  MathSciNet  Google Scholar 

  29. Lu, X., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2014). Wireless networks with RF energy harvesting: A contemporary survey. IEEE Communications Surveys and Tutorials, 17(2), 757–789.

    Article  Google Scholar 

  30. Chen, X., et al. (2019). Analysis and design of an ultra-low-power Bluetooth low-energy transmitter with ring oscillator-based ADPLL and 4\({\times }\) frequency edge combiner. IEEE Journal of Solid-State Circuits, 54(5), 1339–1350.

    Article  Google Scholar 

Download references

Funding

This research was partially supported by the Ministry of Science and Technology, Taiwan, under Grants MOST 105-2221-E-017-008.

Author information

Authors and Affiliations

  1. Department of Optoelectronics and Communication Engineering, National Kaohsiung Normal University, Kaohsiung, 802, Taiwan

    Show-Shiow Tzeng & Han-Wen Chou

Authors
  1. Show-Shiow Tzeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han-Wen Chou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Show-Shiow Tzeng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tzeng, SS., Chou, HW. Multiple Access Control in a Centralized Full-Duplex Cognitive Machine Type Network with RF Energy Harvesting. Wireless Pers Commun 118, 949–960 (2021). https://doi.org/10.1007/s11277-020-08053-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-08053-z

Keywords

Search

Navigation