Skip to main content
SpringerLink
Log in

Smart Channel Modelling for Cloud and Fog Attenuation Using ML for Designing of 6G Networks at D and G Bands

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In case of future telecommunication technologies, extremely high data rates above 100 Gbps is the expectations, which can be fulfilled by utilizing higher spectrum bands. Higher frequency bands like terahertz wave bands are expected to have far broader bandwidths then 5G, therefore 6G will need to encourage R&D activities to utilize so called terahertz waves with frequencies ranging from 100 to 200 GHz. The challenge in utilizing these higher frequency bands is their sensitive nature toward outdoor environmental conditions like cloud, Fog, dust and Rain. To address these issues, this paper proposes a Machine Learning Model based on Artificial Neural Network to predict the attenuation caused due to Clouds and Fog at D and G bands. The model was trained using AMSER–2 Satellite data. The trained model is further optimized using different optimizing techniques. Obtained results was compared with the different existing models.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Availability of data and material

All data used in this research are available. We can provide whenever ask to do so.

Code availability

(software application or custom code): all codes used in this research are available. We can provide whenever ask to do so.

References

  1. Perspectives, Theodore S. Rappaport for CNN business. Opinion: Think 5G is exciting? Just wait for 6G. CNN. https://news.knowledia.com/US/en/articles/opinion-think-5g-is-exciting-just-wait-for-6g-397fc91bf15b9de09d376a11e9267f9b1cd486b7.

  2. Kharpal, A. (2019). China starts development of 6G, having just turned on its 5G mobile network. CNBC. https://www.cnbc.com/2019/11/07/china-starts-6g-development-having-just-turned-on-its-5g-mobile-network.html.

  3. Boxall, A., Lacoma, T. (2021). What is 6G, how fast will it be, and when is it coming?. DigitalTrends. Retrieved February 18, 2021. https://www.digitaltrends.com/mobile/what-is-6g/.

  4. Li, J. Forget about 5G, China has kicked off its development of 6G. Quartz. https://qz.com/1743790/forget-5g-china-begins-development-of-6g/

  5. "6G: What It Is & When to Expect It". Lifewire. https://www.lifewire.com/6g-wireless-4685524.

  6. Dohler, M., Mahmoodi, T., Lema, M. A., Condoluci, M., Sardis, F., Antonakoglou, K., Aghvami, H. (2017). Internet of skills, where robotics meets AI, 5G and the Tactile internet. 2017 European Conference on Networks and Communications (EuCNC) 1–5 https://doi.org/10.1109/EuCNC.2017.7980645. ISBN 978-1-5386-3873-6. S2CID 32801348.

  7. Saad, W., Bennis, M., & Chen, M. (2020). A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Network, 34(3), 134–142. https://doi.org/10.1109/MNET.001.1900287.ISSN1558-156X.S2CID67856161

    Article  Google Scholar 

  8. De Alwis, C., Kalla, A., Pham, Q.-V., Kumar, P., Dev, K., Hwang, W.-J., & Liyanage, M. (2021). Survey on 6G frontiers: Trends, applications, requirements, technologies and future research. IEEE Open Journal of the Communications Society, 2, 836–886. https://doi.org/10.1109/OJCOMS.2021.3071496

    Article  Google Scholar 

  9. Yang, H., Alphones, A., Xiong, Z., Niyato, D., Zhao, J., Wu, K. (2020). Artificial-intelligence-enabled intelligent 6G networks. https://doi.org/10.1109/MNET.011.2000195. ISSN 1558–156X. S2CID 209324400.

  10. Xiao, Y., Shi, G., Li, Y., Saad, W., & Poor, H. V. (2020). Toward self-learning edge intelligence in 6G. IEEE Communications Magazine., 58(12), 34–40. https://doi.org/10.1109/MCOM.001.2000388. ISSN 1558-1896.

    Article  Google Scholar 

  11. Guo, W. (2020). Explainable artificial intelligence for 6G: Improving trust between human and machine. IEEE Communications Magazine., 58(6), 39–45. https://doi.org/10.1109/MCOM.001.2000050

    Article  Google Scholar 

  12. https://www.policytracker.com/blog/what-does-6g-mean-for-spectrum-policy/

  13. https://www.wilsoncenter.org/article/us-and-eu-approaches-6g

  14. Golovachev, Y., Etinger, A., Pinhasi, G. A., & Pinhasi, Y. (2019). Propagation properties of sub-millimeter waves in foggy conditions. Journal of Applied Physics, 125(15), 151612.

    Article  Google Scholar 

  15. Federici, J. F., Ma, J., & Moeller, L. (2016). Review of weather impact on outdoor terahertz wireless communication links. Nano Communication Networks, 10, 13–26.

    Article  Google Scholar 

  16. Su, K., Moeller, L., Barat, R. B., & Federici, J. F. (2012). Experimental comparison of terahertz and infrared data signal attenuation in dust clouds. JOSA A, 29(11), 2360–2366.

    Article  Google Scholar 

  17. Siles, G. A., Riera, J. M., & García-del-Pino, P. (2012). Atmospheric attenuation at 100 and 300 GHz estimated with radiosonde data. In 2012 6th European Conference on Antennas and Propagation (EUCAP) (pp. 567–571). IEEE.

  18. Suen, J. Y., Fang, M. T., & Lubin, P. M. (2014). Global distribution of water vapor and cloud cover–sites for high-performance THz applications. IEEE Transactions on Terahertz Science and Technology, 4(1), 86–100.

    Article  Google Scholar 

  19. Xing, Y., & Rappaport, T. S. (2021). Terahertz wireless communications: Co-sharing for terrestrial and satellite systems above 100 GHz. IEEE Communications Letters.

  20. Al-Saegh, A. M., Sali, A., Mandeep, J. S., Ismail, A., Al-Jumaily, A. H., & Gomes, C. (2014). Atmospheric propagation model for satellite communications. MATLAB Applications for the Practical Engineer, 2, 249–275.

    Google Scholar 

  21. Tamosiunaite, M., Tamosiunas, S., Zilinskas, M., & Valusis, G. (2017). Atmospheric attenuation of the terahertz wireless networks. Broadband Communications Networks-Recent Advances and Lessons from Practice, 143–156.

  22. Federici, J. F., Ma, J., & Moeller, L. (2015). Weather impact on outdoor terahertz wireless links. In Proceedings of the Second Annual International Conference on Nanoscale Computing and Communication (pp. 1–6).

  23. Moon, E. B., Jeon, T. I., & Grischkowsky, D. R. (2015). Long-path THz-TDS atmospheric measurements between buildings. IEEE Transactions on Terahertz Science and Technology, 5(5), 742–750.

    Article  Google Scholar 

  24. Zhang, Q., Wang, H., & Ma, J. (2015). On the extinction characteristics of terahertz wave in fog. In Proc. Of Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015).

  25. Kokkoniemi, J., Jornet, J. M., Petrov, V., Koucheryavy, Y., & Juntti, M. (2021). Channel modeling and performance analysis of airplane-satellite terahertz band communications. IEEE Transactions on Vehicular Technology, 70(3), 2047–2061.

    Article  Google Scholar 

  26. Etinger, A., Golovachev, Y., Pinhasi, G. A., & Pinhasi, Y. (2019). Dielectric characterization of fog in the terahertz regime. Acta Physica Polonica A, 136, 749–753.

    Article  Google Scholar 

  27. Lai, Z., Yi, H., Guan, K., Ai, B., Zhong, W., Dou, J., & Zhong, Z. (2020). Impact of meteorological attenuation on channel characterization at 300 GHz. Electronics, 9(7), 1115.

    Article  Google Scholar 

  28. Sheikh, F., Zarifeh, N., & Kaiser, T. (2016). Terahertz band: Channel modelling for short-range wireless communications in the spectral windows. IET Microwaves, Antennas & Propagation, 10(13), 1435–1444.

    Article  Google Scholar 

  29. Mondal, T., Maity, S., Ghatak, R., & Bhadra Chaudhuri, S. R. (2018). Design and analysis of a wideband circularly polarised perturbed psi-shaped antenna. IET Microwaves, Antennas & Propagation, 12(9), 1582–1586.

    Article  Google Scholar 

  30. Jing, Q., & Liu, D. (2018). Study of atmospheric attenuation characteristics of terahertz wave based on line-by-line integration. International Journal of Communication Systems, 31(12), e3718.

    Article  MathSciNet  Google Scholar 

  31. Piraquive, F. N. D., Martínez, O.S., Pérez, E. V., Crespo, R. G., Knowledge Cabrera-Mercader, C. R., & Staelin, D. H. (1995). Passive microwave relative humidity retrievals using feed forward neural networks. IEEE Transactions on Geoscience and Remote Sensing, 33(6), 1324-1328.

  32. da Silveira, R. B., & Holt, A. R. (2001). An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks. IEEE transactions on geoscience and remote sensing, 39(8), 1777–1788.

    Article  Google Scholar 

  33. Pazmany, A. L., Sekelsky, J. B. M. S. M., McLaughlin, D. J., & Bluestein, H. B. (2001). 2B. 1 multi-frequency radar estimation of cloud and precipitation properties using an artificial neural network. In Conference on radar meteorology of the american meteorological society. American Meteorological Society (Vol. 30, pp. 154–156).

  34. Choudhury, S., Mitra, S., & Pal, S. K. (2003). Neurofuzzy classification and rule generation of modes of radiowave propagation. IEEE Transactions on Antennas and Propagation, 51(4), 862–871.

    Article  Google Scholar 

  35. Barthes, L., Mallet, C., & Gole, P. (2003). Neural network model for atmospheric attenuation retrieval between 20 and 50 GHz by means of dual-frequency microwave radiometers. Radio science, 38(5), 3–1.

    Article  Google Scholar 

  36. Barthes, L., Mallet, C., & Brisseau, O. (2006). A neural network model for the separation of atmospheric effects on attenuation: Application to frequency scaling. Radio science, 41(04), 1–11.

    Article  Google Scholar 

  37. Singh, H et al. Proposed model for radio wave attenuation due to rain (RWAR) (vol 115, pg 791, 2020)." Wireless Personal Communications (2021).

  38. Singh, H., et al. (2020). An empirical model for prediction of environmental attenuation of millimeter waves. Wireless Personal Communications, 115(1), 809–826.

    Article  Google Scholar 

  39. Singh, H. et al. An intelligent model for prediction of attenuation caused by rain based on machine learning techniques. 2020 International Conference on Contemporary Computing and Applications (IC3A). IEEE, 2020.

  40. Singh, H., et al. (2022). A smart model for prediction of radio wave attenuation due to clouds and fog (SMRWACF). Wireless Personal Communications, 122(4), 3227–3245.

    Article  MathSciNet  Google Scholar 

  41. Kumar, V., et al. (2021). Soft clustering for enhancing ITU rain model based on machine learning techniques. Wireless Personal Communications, 120(1), 287–305.

    Article  Google Scholar 

  42. Singh, H. et al. Prediction of radio wave attenuation due to clouds using ANN and its business aspects. In 2021 29th National Conference with International Participation (TELECOM). IEEE, 2021.

  43. Singh, H., et al. Prediction of radio wave attenuation due to cloud using machine learning techniques. 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST). IEEE, 2021.

  44. Kumar, V., et al. Approximations for ITV rain model using machine learning. 2021 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST). IEEE, 2021.

  45. Kumar, Vivek, et al. An ANN model for predicting radio wave attenuation due to rain and its business aspect. 2021 29th National Conference with International Participation (TELECOM). IEEE, 2021.

  46. Singh, H., Kumar, V., Saxena, K., & Bonev, B. (2021). Computational intelligent techniques for prediction of environmental attenuation of millimeter waves. Security and Privacy Issues in IoT Devices and Sensor Networks, 263–284.

  47. Singh, H., Prasad, R., & Bonev, B. (2018). The studies of millimeter waves at 60 GHz in outdoor environments for IMT applications: A state of art. Wireless Personal Communications, 100(2), 463–474.

    Article  Google Scholar 

  48. Singh, H., Bonev, B., & Chandra, A. (2018). Effects of atmospheric impairments of satellite link operating in Ka band. Wireless Personal Communications, 101(1), 425–437.

    Article  Google Scholar 

  49. Attenuation due to cloud and fog, Recommendation ITU-R P.840–5, P Series Radio wave propagation.

  50. Wentz, F. J., Meissner, T., Gentemann, C., Hilburn, K. A. Scott, J. 2014: remote sensing systems GCOM-W1 AMSR2 [Daily data] Environmental Suite on 0.25 deg grid, Remote Sensing Systems, Santa Rosa, CA.

Download references

Funding

This work was not supported by the financial Grant from any organization.

Author information

Authors and Affiliations

  1. Noida Institute of Engineering and Technology affiliated to AKT University Lucknow, Lucknow, India

    Hitesh Singh, Vivek Kumar & Kumud Saxena

  2. Aarhus University, Aarhus, Denmark

    Hitesh Singh, Vivek Kumar, Kumud Saxena, Vinod M. Kapse & Ramjee Prasad

  3. Technical University of Sofia, Sofia, Bulgaria

    Boncho Bonev

Authors
  1. Hitesh Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vivek Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kumud Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vinod M. Kapse
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Boncho Bonev
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ramjee Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Did work for machine learning model for cloud attenuation which will be helpful in 6G implementation. Add state of art for cloud attenuation in last few years. Implement machine and AI based model for radio wave propagation particular for terahertz waves.

Corresponding author

Correspondence to Vivek Kumar.

Ethics declarations

Conflict of interest

There is no any conflicts 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

Singh, H., Kumar, V., Saxena, K. et al. Smart Channel Modelling for Cloud and Fog Attenuation Using ML for Designing of 6G Networks at D and G Bands. Wireless Pers Commun 129, 1669–1692 (2023). https://doi.org/10.1007/s11277-023-10201-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10201-0

Keywords

Search

Navigation