Skip to main content
SpringerLink
Log in

Statistical RMS delay spread representation in 5G mm-Wave analysis using real-time measurements

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Any wireless communication system’s performance depends on channel parameters’ accuracy. The classical Rayleigh and Nakagami-m research subjects remain vital even in the most modern millimeter-wave (mm-wave) applications. This research aims to create a generalized cumulative distribution function for describing random changes in wireless channels. It is vital to have a suitable channel representation model to represent varied fifth-generation applications to ease network implementation. This study provides mm-wave measurement data at 28 GHz carrier frequencies in line of sight and non-line of sight propagation. Lognormal, Nakagami, Gaussian, Weibull, and Rayleigh distributions outperform the proposed model’s universal exponential density function. The experimental data verified the introduced method.

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. Aghababaiyan, K., Kebriaei, H., Shah-Mansouri, V., Maham, B., & Niyato, D. (2022). Enhanced Modulation for Multiuser Molecular Communication in Internet of Nano Things. IEEE Internet of Things Journal, 9(20), 19787–19802. https://doi.org/10.1109/JIOT.2022.3168658

    Article  Google Scholar 

  2. Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2017). Overview of millimeter wave communications for fifth-generation (5G) wireless networks—With a focus on propagation models. IEEE Transactions on antennas and propagation, 65(12), 6213–6230. https://doi.org/10.1109/TAP.2017.2734243

    Article  Google Scholar 

  3. Bangerter, B., Talwar, S., Arefi, R., & Stewart, K. (2014). Networks and devices for the 5G era. IEEE Communications Magazine, 52(2), 90–96. https://doi.org/10.1109/MCOM.2014.6736748

    Article  Google Scholar 

  4. Udayakumar, E., & Krishnaveni, V. (2019). Analysis of various interference in millimeter-wave communication systems: a survey. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICCCNT45670.2019.8944417.

  5. Hussain, R. (2021). Shared Aperture Slot-Based Sub-6 GHz and mm-Wave IoT Antenna for 5G Applications. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3050383

    Article  Google Scholar 

  6. Momo, S. H. A., & Mowla, M. M. (2019). Statistical analysis of an outdoor mmWave channel model at 73 GHz for 5G networks. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (pp. 1–4). IEEE. https://doi.org/10.1109/IC4ME247184.2019.9036692.

  7. Hasan, R., Mowla, M. M., Rashid, M. A., Hosain, M. K., & Ahmad, I. (2019, February). A statistical analysis of channel modeling for 5g mmwave communications. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1–6). IEEE. https://doi.org/10.1109/ECACE.2019.8679507.

  8. Aghababaiyan, K., & Maham, B. (2018). QoS-aware downlink radio resource management in OFDMA-based small cells networks. IET Communications, 12(4), 441–448. https://doi.org/10.1049/iet-com.2017.1222

    Article  Google Scholar 

  9. Liu, Y., Wang, C. X., & Huang, J. (2019). Recent developments and future challenges in channel measurements and models for 5G and beyond high-speed train communication systems. IEEE Communications Magazine, 57(9), 50–56. https://doi.org/10.1109/MCOM.001.1800987

    Article  Google Scholar 

  10. Basar, E., Yildirim, I., & Kilinc, F. (2021). Indoor and outdoor physical channel modeling and efficient positioning for reconfigurable intelligent surfaces in mmWave bands. IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2021.3113954

    Article  Google Scholar 

  11. Qamar, F., Hindia, M. N., Abbas, T., Dimyati, K. B., & Amiri, I. S. (2019). Investigation of QoS performance evaluation over 5G network for indoor environment at millimeter wave bands. International Journal of Electronics and Telecommunications, 65(1), 95–101. https://doi.org/10.24425/ijet.2019.126288

    Article  Google Scholar 

  12. Ge, Y., Kim, H., Wen, F., Svensson, L., Kim, S., & Wymeersch, H. (2020). Exploiting diffuse multipath in 5G SLAM. In GLOBECOM 2020–2020 IEEE Global Communications Conference (pp. 1–6). IEEE. https://doi.org/10.1109/GLOBECOM42002.2020.9322120.

  13. Witrisal, K., Meissner, P., Leitinger, E., Shen, Y., Gustafson, C., Tufvesson, F., & Win, M. Z. (2016). High-accuracy localization for assisted living: 5G systems will turn multipath channels from foe to friend. IEEE Signal Processing Magazine, 33(2), 59–70. https://doi.org/10.1109/MSP.2015.2504328

    Article  Google Scholar 

  14. Budhgaon, P. V. P. I. T. Multipath fading channel modeling and performance comparison of wireless channel models.

  15. Lee, J., Liang, J., Kim, M. D., Park, J. J., Park, B., & Chung, H. K. (2016). Measurement-based propagation channel characteristics for millimeter-wave 5G Giga communication systems. Etri Journal, 38(6), 1031–1041. https://doi.org/10.4218/etrij.16.2716.0050

    Article  Google Scholar 

  16. Debaenst, W., Feys, A., Cuiñas, I., Garcia Sanchez, M., & Verhaevert, J. (2020). RMS delay spread vs. coherence bandwidth from 5G indoor radio channel measurements at 3.5 GHz band. Sensors, 20(3), 750. https://doi.org/10.3390/s20030750

    Article  Google Scholar 

  17. Arslan, H., & Yucek, T. (2003). Delay spread estimation for wireless communication systems. In Proceedings of the Eighth IEEE Symposium on Computers and Communications. ISCC 2003 (pp. 282–287). IEEE. https://doi.org/10.1109/ISCC.2003.1214135.

  18. Priebe, S., Jacob, M., & Kürner, T. (2014). Angular and RMS delay spread modeling in view of THz indoor communication systems. Radio Science, 49(3), 242–251. https://doi.org/10.1002/2013RS005292

    Article  Google Scholar 

  19. Li, H., Liu, D., Li, J., & Stoica, P. (2003). Channel order and RMS delay spread estimation with application to AC power line communications. Digital Signal Processing, 13(2), 284–300. https://doi.org/10.1016/S1051-2004(02)00030-1

    Article  Google Scholar 

  20. Hashemi, H., & Tholl, D. (1994). Statistical modeling and simulation of the RMS delay spread of indoor radio propagation channels. IEEE Transactions on Vehicular Technology, 43(1), 110–120. https://doi.org/10.1109/25.282271

    Article  Google Scholar 

  21. Rissafi, Y., Talbi, L., & Ghaddar, M. (2011). Experimental characterization of an UWB propagation channel in underground mines. IEEE Transactions on Antennas and Propagation, 60(1), 240–246. https://doi.org/10.1109/TAP.2011.2167927

    Article  Google Scholar 

  22. Bernado, L., Zemen, T., Tufvesson, F., Molisch, A. F., & Mecklenbräuker, C. F. (2013). Delay and Doppler spreads of nonstationary vehicular channels for safety-relevant scenarios. IEEE Transactions on Vehicular Technology, 63(1), 82–93. https://doi.org/10.1109/TVT.2013.2271956

    Article  Google Scholar 

  23. Baz, A., Al-Naja, A. A., & Baz, M. (2015). Statistical model for IoT/5G networks. In 2015 Seventh International Conference on Ubiquitous and Future Networks (pp. 109–111). IEEE. https://doi.org/10.1109/ICUFN.2015.7182511.

  24. Al-Samman, A. M., Azmi, M. H., Al-Gumaei, Y. A., Al-Hadhrami, T., Fazea, Y., & Al-Mqdashi, A. (2020). Millimeter wave propagation measurements and characteristics for 5G system. Applied Sciences, 10(1), 335. https://doi.org/10.3390/app10010335

    Article  Google Scholar 

  25. Olutayo, A., Cheng, J., & Holzman, J. F. (2020). A new statistical channel model for emerging wireless communication systems. IEEE Open Journal of the Communications Society, 1, 916–926. https://doi.org/10.1109/OJCOMS.2020.3008161

    Article  Google Scholar 

  26. Zahedi, Y., Ngah, R., Nunoo, S., Mokayef, M., Alavi, S. E., & Amiri, I. S. (2016). Experimental measurement and statistical analysis of the RMS delay spread in time-varying ultra-wideband communication channel. Measurement, 89, 179–188. https://doi.org/10.1016/j.measurement.2016.04.009

    Article  Google Scholar 

  27. Al-Samman, A. M., Abd Rahman, T., Nunoo, S., Chude-Okonkwo, U. A., Ngah, R., Shaddad, R. Q., & Zahedi, Y. (2015). Experimental characterization and analysis for ultra-wideband outdoor channel. Wireless personal communications, 83(4), 3103–3118. https://doi.org/10.1007/s11277-015-2585-x

    Article  Google Scholar 

  28. Sánchez, J. D. V., Urquiza-Aguiar, L., & Paredes Paredes, M. C. (2021). Fading channel models for mm-wave communications. Electronics, 10(7), 798. https://doi.org/10.3390/electronics10070798

    Article  Google Scholar 

  29. Sadhanala, V., Wang, Y. X., Ramdas, A., & Tibshirani, R. J. (2019, April). A higher-order kolmogorov-smirnov test. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2621–2630). PMLR.

Download references

Author information

Authors and Affiliations

  1. Electrical and Electronic Engineering, Near East University, Mersin 10, Turkey

    Özlem Sabuncu & Bülent Bilgehan

Authors
  1. Özlem Sabuncu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bülent Bilgehan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bülent Bilgehan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Data Availability

The corresponding author’s data supporting this study's findings are available upon reasonable request.

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

Sabuncu, Ö., Bilgehan, B. Statistical RMS delay spread representation in 5G mm-Wave analysis using real-time measurements. Wireless Netw 29, 2539–2549 (2023). https://doi.org/10.1007/s11276-023-03332-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03332-6

Keywords

Search

Navigation