Skip to main content
Log in

Use of a fuzzy logic scheduler to improve quality of service on cellular networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

An efficient and fair packet scheduler is very important to guaranty the cellular network quality of service (QoS). In general, classic packet schedulers follow static rules based on priorities. Unfortunately, static rules may cause unfair resource distribution. We present and analyze FuzSy, a cellular network packet scheduler based on the quality of service and fuzzy logic. Instead of following static prioritization rules, it prioritizes traffic classes that have a higher risk of not reaching QoS requirements. Its dynamic priority changes any traffic class in case of performance decrease risk. Based on network metadata, FuzSy uses fuzzy logic to decide about prioritization. We conducted simulations in a 4G network. FuzSy achieved QoS requirements and network fairness for all classes. One of FuzSy’s significant advantages is the fairness guaranty that solutions contained in other works do not garantee. We show that FuzSy can be used in any kind of cellular network because it is straightforward to have its parameters adjusted. In the future 5G networks, for example, FuzSy may be helpful due to a large number of heterogeneous connected devices and different kinds of applications. The FuzSy scheduler use may achieve the QoS, and the fairness guarantees since its concept is independent of technology, and it is usable in any kind of network.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

References

  1. Turk, Y., Zeydan, E., & Akbulut, C. A. (2019). Experimental performance evaluations of comp and ca in centralized radio access networks. Telecommunication Systems,. https://doi.org/10.1007/s11235-019-00553-z.

    Article  Google Scholar 

  2. Shukla, S., & Bhatia, V. (2018). Packet scheduling algorithm in lte/lte-advanced-based cellular networks. IETE Technical Review, 35(6), 551–561. https://doi.org/10.1080/02564602.2017.1342573.

    Article  Google Scholar 

  3. de L. P. Duarte-Figueiredo, F., & Loureiro, A. A. F. (2007). An end-to-end wireless qos architecture evaluation. In 12th IEEE symposium on computers and communications, 2007. ISCC 2007 (pp. 715–720). https://doi.org/10.1109/ISCC.2007.4381535.

  4. Karthikeyan, N. K., & Narayanasamy, P. (2010). An integrated system for qos provisioning in cellular networks. In 2010 IEEE conference on cybernetics and intelligent systems (pp. 218–224). https://doi.org/10.1109/ICCIS.2010.5518555.

  5. Lanzillotti, R. S. (2014). Logica Fuzzy: Uma Abordagem Para Reconhecimento de Padrão. PACO EDITORIAL.

  6. Piro, G., Grieco, L. A., Boggia, G., Capozzi, F., & Camarda, P. (2011). Simulating lte cellular systems: An open-source framework. IEEE Transactions on Vehicular Technology, 60(2), 498–513. https://doi.org/10.1109/TVT.2010.2091660.

    Article  Google Scholar 

  7. Chayon, H. R., Dimyati, K., & Ramiah, H. (2017). An efficient packet scheduling algorithm to improve the performance of cell-edge user in lte network. In 2017 IEEE 13th Malaysia international conference on communications (MICC) (pp. 130–135). https://doi.org/10.1109/MICC.2017.8311746.

  8. Jalali, A., Padovani, R., & Pankaj, R. (2000). ata throughput of cdma-hdr a high efficiency-high data rate personal communication wireless system. In Vehicular technology conference proceedings, 2000. VTC 2000-Spring Tokyo. 2000 IEEE 51st (Vol. 3, pp. 1854–1858). https://doi.org/10.1109/VETECS.2000.851593.

  9. Andrews, M., Kumaran, K., Ramanan, K., Stolyar, A., Whiting, P., & Vijayakumar, R. (2001). Providing quality of service over a shared wireless link. IEEE Communications Magazine, 39(2), 150–154. https://doi.org/10.1109/35.900644.

    Article  Google Scholar 

  10. Piro, G., Grieco, L. A., Boggia, G., Fortuna, R., & Camarda, P. (2011). Two-level downlink scheduling for real-time multimedia services in lte networks. IEEE Transactions on Multimedia, 13(5), 1052–1065. https://doi.org/10.1109/TMM.2011.2152381.

    Article  Google Scholar 

  11. Rhee, J. H., Holtzman, J. M., & Kim, D. K. (2004). Performance analysis of the adaptive exp/pf channel scheduler in an amc/tdm system. IEEE Communications Letters, 8(8), 497–499. https://doi.org/10.1109/LCOMM.2004.833786.

    Article  Google Scholar 

  12. Sadiq, B., Baek, S. J., & de Veciana, G. (2011). Delay-optimal opportunistic scheduling and approximations: The log rule. IEEE/ACM Transactions on Networking, 19(2), 405–418. https://doi.org/10.1109/TNET.2010.2068308.

    Article  Google Scholar 

  13. Shakkottai, S., & Stolyar, A. L. (2002). Scheduling for multiple flows sharing a time-varying channel: The exponential rule. Translations of the American Mathematical Society-Series, 2(207), 185–202.

    Article  Google Scholar 

  14. Wallenius, E., & Hamalainen, T. (2002). Pricing model for 3g/4g networks. In The 13th IEEE international symposium on personal, indoor and mobile radio communications, 2002 (Vol. 1, pp. 187–191). https://doi.org/10.1109/PIMRC.2002.1046686.

  15. Lai, Y. L., & Jiang, J. R. (2014). Pricing resources in lte networks through multiobjective optimization. p. 8. https://doi.org/10.1155/2014/394082.

  16. Lai, W. K., & Tang, C. L. (2013). Qos-aware downlink packet scheduling for LTE networks. Computer Networks, 57(7), 1689–1698.

    Article  Google Scholar 

  17. 3GPP: Lte release 10. http://www.3gpp.org/technologies/keywords-acronyms/97-lte-advanced (2013). Accessed on 10.04.15.

  18. Tostes Ribeiro, A. I. J., Zarate, L. E., & Duarte Figueiredo, F. d. L. P. (2013). Dynamic fuzzy cellular admission control. In 5th IEEE Latin-American conference on communications (IEEE LATINCOM 2013).

  19. de Souza, F. R. (2016). Fuzsy: A cellular network packet scheduler based on quality of service and fuzzy logic. Master’s thesis, Pontifícia Universidade Católica de Minas Gerais.

  20. Jain, R., Chiu, D.M., & Hawe, W. (1998). A quantitative measure of fairness and discrimination for resource allocation in shared computer systems. arXiv:cs.NI/9809099

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by FRS and FD-F. FRS wrote the first draft of the manuscript, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fabrício R. de Souza.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

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

The authors would like to thank the Pontifical Catholic University of Minas Gerais (PUC Minas), the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Microsoft Azure for Research Program for the grants and incentives that have turned possible this work development (Grant No. X88SEV2L03HE)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Souza, F.R., Duarte-Figueiredo, F. & Gouvêa Meireles, M.R. Use of a fuzzy logic scheduler to improve quality of service on cellular networks. Telecommun Syst 74, 451–465 (2020). https://doi.org/10.1007/s11235-020-00668-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-020-00668-8

Keywords

Navigation