SpringerLink
Log in

An Improved Machine Learning Approach for Throughput Prediction in the Next Generation Wireless Networks

  • Conference paper
  • First Online:
Advanced Computing, Machine Learning, Robotics and Internet Technologies (AMRIT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1953))

  • 56 Accesses

Abstract

Currently evolving 5G telecom networks require different intelligent learning and decision mechanisms to adapt to the varying network conditions. Further, considering additional requirements of low-latency and ultra-reliability, newer resource allocation schemes are to be explored to find the most effective way to predict the amount of resources required by a system. In this paper, a unique resource allocation scheme is devised for the 5G network using the properties of first packet transmission and the subsequent retransmissions. The proposed model is shown to accurately predict the system-level as well as user-level throughput for a set of mobile users, while ensuring lower consumption of network resources. The simulations show an accuracy of 85% for the user-level throughput that is acceptable for the dynamic resource planning in future networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 58.84
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 74.89
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. **aodong, W.: OFDM and its application to 4G. In: 14th Annual International Conference on Wireless and Optical Communications (2005). WOCC 2005, Newark, NJ, USA, 2005, pp. 69-, doi: https://doi.org/10.1109/WOCC.2005.1553751

  2. Olga, B.-L., Victor, L., Branka, J., Aditya, S., Ehsaneh, S., Bryson, P.: Microwave and Wearable Technologies for 5G (2015). https://doi.org/10.1109/TELSKS.2015.7357765

  3. Chang, B., Zhang, L., Li, L., Zhao, G., Chen, Z.: Optimizing resource allocation in URLLC for real-time wireless control systems. IEEE Trans. Veh. Technol.Veh. Technol. 68(9), 8916–8927 (2019). https://doi.org/10.1109/TVT.2019.2930153

    Article  Google Scholar 

  4. Suganya, S., Maheshwari, S., Latha, Y.S., Ramesh, C.: Resource scheduling algorithms for LTE using weights. In: 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 264–269. IEEE (2016)

    Google Scholar 

  5. Maheshwari, S., Mahapatra, S., Cheruvu, K.: Measurement and forecasting of next generation wireless internet traffic (No. 525). EasyChair (2018)

    Google Scholar 

  6. Kulkarni, P., Lewis, T., Fan, Z.: Simple traffic prediction mechanism and its applications in wireless networks. Wireless Pers. Commun.Commun. 59(2), 261–274 (2011)

    Article  Google Scholar 

  7. Vasu, K., Maheshwari, S., Mahapatra, S., Kumar, C.S.: QoS aware fuzzy rule based vertical handoff decision algorithm for wireless heterogeneous networks. In: 2011 National Conference on Communications (NCC), pp. 1–5. IEEE (2011)

    Google Scholar 

  8. Mirza, M., Sommers, J., Barford, P., Zhu, X.: A machine learning approach to TCP throughput prediction. ACM SIGMETRICS Perform. Eval. Rev. 35(1), 97–108 (2007)

    Article  Google Scholar 

  9. Wang, Y., Narasimha, M., Heath, R.W.: MmWave beam prediction with situational awareness: a machine learning approach. In: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5. IEEE (2018)

    Google Scholar 

  10. Alsheikh, M.A., Lin, S., Niyato, D., Tan, H.P.: Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutorials 16(4), 1996–2018 (2014)

    Article  Google Scholar 

  11. Chen, M., Challita, U., Saad, W., Yin, C. and Debbah, M.,. Machine learning for wireless networks with artificial intelligence: a tutorial on neural networks. ar**v preprint ar**v:1710.02913, 9 2017

  12. Jagannath, J., Polosky, N., Jagannath, A., Restuccia, F., Melodia, T.: Machine learning for wireless communications in the internet of things: a comprehensive survey. Ad Hoc Netw.Netw. 93, 101913 (2019)

    Article  Google Scholar 

  13. Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.C., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Wirel. Commun.Wirel. Commun. 24(2), 98–105 (2016)

    Article  Google Scholar 

  14. Jiang, D., Wang, H., Malkamaki, E., Tuomaala, E.: Principle and performance of semi-persistent scheduling for VoIP in LTE systems. In: International Conference on Wireless Principle and performance of semi-persistent scheduling for VoIP in LTE system. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Sept 2007, pp. 2861–2864 (2007)

    Google Scholar 

  15. 3GPP: Study on latency reduction techniques for LTE. 3GPP TR 36.881 v14.0.0, Tech. Rep. (2016)

    Google Scholar 

  16. Elayoubi, S.E., Brown, P., Deghel, M., Galindo-Serrano, A., Elayoubi, S.: Radio resource allocation and retransmission schemes for URLLC over 5G networks. IEEE J. Sel. Areas Commun. Inst. Electr. Electron. Eng. 37(4), 896–904 (2019). https://doi.org/10.1109/jsac.2019.2898783. hal-02117082

Download references

Acknowledgement

The work is carried out using licensed version of MATLAB 19 which is available at the signal processing centre of excellence. We would like to acknowledge the support and guidance provided by the members of the CoE.

Author information

Authors and Affiliations

  1. Electronics and Communication Dept. (VTU RC), CMR Institute of Technology, Bengaluru, India

    Aritri Debnath

  2. Department of Information Technology, Sikkim Manipal Institute of Technology, Majitar East, Sikkim, India

    Barnali Dey

Authors
  1. Aritri Debnath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barnali Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Barnali Dey .

Editor information

Editors and Affiliations

  1. Assam University, Silchar, India

    Prodipto Das

  2. Assam University, Silchar, India

    Shahin Ara Begum

  3. The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Debnath, A., Dey, B. (2024). An Improved Machine Learning Approach for Throughput Prediction in the Next Generation Wireless Networks. In: Das, P., Begum, S.A., Buyya, R. (eds) Advanced Computing, Machine Learning, Robotics and Internet Technologies. AMRIT 2023. Communications in Computer and Information Science, vol 1953. Springer, Cham. https://doi.org/10.1007/978-3-031-47224-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47224-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47223-7

  • Online ISBN: 978-3-031-47224-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation