Skip to main content
SpringerLink
Log in

Effect of Relay-based Communication on Probability of Detection for Spectrum Sensing in LoRaWAN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The internet of things (IoT) may be considered as an emerging paradigm that has led to the transformation of the physical world into an advanced system comprising interconnected devices on an unparalleled scale. To accommodate the spectrum, need of these numerous IoT devices concept of cognitive radio technology may be considered a boon. The cognitive radio (CR) aims to alleviate the spectrum crunch problem through detection of the available spectrum holes and their efficient utilization. This paper incorporates the spectrum sensing concept in the LoRaWAN network for better utilization of the spectrum. A non-relay and relay-based type of communication has been investigated to analyze the enhancement in the detection probability of the sensing technique for a LoRaWAN network in terms of minimal sensing time, maximum coverage, and low signal-to-noise (SNR). The effect of sensing time, distance and SNR on detection probability for 915 MHz and 866.4 MHz LoRa bands has been analyzed and critically evaluated at uplink bandwidth of 125 kHz and 250 kHz respectively using MATLAB. The simulated results validate the performance improvement through multiple relays in a LoRaWAN communication model in context to a higher probability of detection for energy detection-based spectrum sensing.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Code availability

All the codes were executed using MATLAB software and can be available upon request.

References

  1. Ejaz, W., & Ibnkahla, M. (2018). Multiband spectrum sensing and resource allocation for IoT in cognitive 5G networks. IEEE Internet of Things Journal, 5(1), 150–163.

    Article  Google Scholar 

  2. Ahmed, R., et al. (2021). CR-IoTNet: Machine learning based joint spectrum sensing and allocation for cognitive radio enabled IoT cellular networks. Ad Hoc Networks, 112, 102390.

    Article  Google Scholar 

  3. Salahdine, F., & El Ghazi, H. (2017). A real time spectrum scanning technique based on compressive sensing for cognitive radio networks. In IEEE 8th annual ubiquitous computing, electronics and mobile communication conference (UEMCON) (pp. 506–511).

  4. Wan, R., et al. (2020). Energy-efficient cooperative spectrum sensing scheme based on spatial correlation for cognitive internet of things. IEEE Access, 8, 139501–139511.

    Article  Google Scholar 

  5. FCC, Federal Communications Commission Spectrum Policy Task Force, Report of the Spectrum Efficiency Working Group. Technical report. USA (2002).

  6. Lu, L., et al. (2012). Ten years of research in spectrum sensing and sharing in cognitive radio. EURASIP Journal on Wireless Communications and Networking, 28, 1–16.

    Google Scholar 

  7. Zeng, Y., et al. (2010). A review on spectrum sensing for cognitive radio: Challenges and solutions. EURASIP Journal on Advances in Signal Processing, 2010, 1–15.

    Article  Google Scholar 

  8. Shellhammer, S. J. (2008). Spectrum sensing in IEEE 802.22. IAPR workshop cognitive information processing (pp. 9–10).

  9. Sobron, I., et al. (2015). Energy detection technique for adaptive spectrum sensing. IEEE Transactions on Communications, 63(3), 617–627.

    Article  Google Scholar 

  10. Liu, X., et al. (2020). Cooperative spectrum sensing optimization in energy-harvesting cognitive radio networks. IEEE Transactions on Wireless Communications, 19(11), 7663–7676.

    Article  Google Scholar 

  11. Li, Z., et al. (2018). Dynamic compressive wide-band spectrum sensing based on channel energy reconstruction in cognitive internet of things. IEEE Transactions on Industrial Informatics, 14(6), 2598–2607.

    Article  Google Scholar 

  12. Hossain, M. A., Schukat, M., & Barrett, E. (2021). A reliable energy and spectral efficient spectrum sensing approach for cognitive radio based IoT networks. In 2021 IEEE 11th annual computing and communication workshop and conference (CCWC) (pp. 1569–1576).

  13. Zhang, L., Liang, Y.-C., & Xiao, M. (2018). Spectrum sharing for internet of things: A survey. IEEE Wireless Communications, 26(3), 132–139.

    Article  Google Scholar 

  14. Mekuria, F., & Mfupe, L. (2019). Spectrum sharing for unlicensed 5G networks. IEEE Wireless Communications and Networking Conference (WCNC), 2019, 1–5.

    Google Scholar 

  15. Bayhan, S., Gür, G., & Zubow, A. (2018). The future is unlicensed: Coexistence in the unlicensed spectrum for 5g. arXiv preprint arXiv:1801.04964.

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

    Article  Google Scholar 

  17. Hossain, M. A., et al. (2021). Spectrum sensing challenges and their solutions in cognitive radio based vehicular networks. International Journal of Communication Systems, 34(7), e4748.

    Article  Google Scholar 

  18. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1), 40–62.

    Article  Google Scholar 

  19. Subhedar, M., & Birajdar, G. (2011). Spectrum sensing techniques in cognitive radio networks: A survey. International Journal of Next-Generation Networks, 3(2), 37–51.

    Article  Google Scholar 

  20. Kumar, R. (2014). Analysis of spectrum sensing techniques in cognitive radio. International Journal of Information and Computation Technology, 4(4), 437–444.

    MathSciNet  Google Scholar 

  21. Eappen, G., & Shankar, T. (2020). Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network. Physical Communication, 40, 101091.

    Article  Google Scholar 

  22. Develi, I. (2020). Spectrum sensing in cognitive radio networks: Threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking, 1, 1–19.

    Google Scholar 

  23. Kumar, A., Pandit, S., & Singh, G. (2021). Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels. Wireless Networks, 1, 961–980.

    Article  Google Scholar 

  24. Raychowdhury, A., & Pramanik, A. (2020). Survey on LoRa technology: Solution for internet of things. In Intelligent systems, technologies and applications (pp. 259–271).

  25. Guo, Q., Yang, F., & Wei, J. (2021). Experimental evaluation of the packet reception performance of LoRa. Sensors, 21(4), 1071.

    Article  Google Scholar 

  26. Hoeller, A., et al. (2018). Analysis and performance optimization of LoRa networks with time and antenna diversity. IEEE Access, 6, 32820–32829.

    Article  Google Scholar 

  27. Georgiou, O., & Raza, U. (2017). Low power wide area network analysis: Can LoRa scale. IEEE Wireless Communications Letters, 6(2), 162–165.

    Article  Google Scholar 

  28. Nguyen, T. H., Jung, W. S., Tu, L. T., Van Chien, T., Yoo, D., & Ro, S. (2020). Performance analysis and optimization of the coverage probability in dual hop LoRa networks with different fading channels. IEEE Access, 8, 107087–107102.

    Article  Google Scholar 

  29. Mahendru, G., Shukla, A., & Banerjee, P. (2020). A novel mathematical model for energy detection based spectrum sensing in cognitive radio networks. Wireless Personal Communications, 110(3), 1237–1249.

    Article  Google Scholar 

  30. Salameh, H. A. B., et al. (2019). Spectrum assignment in hardware-constrained cognitive radio IoT networks under varying channel-quality conditions. IEEE Access, 7, 42816–42825.

    Article  Google Scholar 

Download references

Funding

Authors state no funding involved.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University, Sector-125, Noida, India

    Hafsa Rafiqi, Garima Mahendru & Sindhu Hak Gupta

Authors
  1. Hafsa Rafiqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garima Mahendru
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sindhu Hak Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Garima Mahendru.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rafiqi, H., Mahendru, G. & Gupta, S.H. Effect of Relay-based Communication on Probability of Detection for Spectrum Sensing in LoRaWAN. Wireless Pers Commun 130, 2345–2366 (2023). https://doi.org/10.1007/s11277-023-10273-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10273-y

Keywords

Search

Navigation