Nothing Special   »   [go: up one dir, main page]

Skip to main content

Analysis on Relationship Between Fractional Calculus Fluid Model and Effective Capacity of Bursty Data Service in Multi-hop Wireless Networks

  • Conference paper
  • First Online:
Simulation Tools and Techniques (SIMUtools 2020)

Abstract

A fractional calculus fluid model can be used to better explain the traffic of bursty data service. It is long-range dependence and has a fractal-like feature of network data flow. This paper builds a fluid model to describe the traffic of multi-hop wireless networks with QoS constraint. We use effective capacity model to depict the performance of bursty data service in wireless networks with QoS constraint. Finally, experiment results show that the heavy-tailed delay distributions, the hyperbolically decay of the packet delay auto-covariance function and fractional differential equations are formally related. Our method is effective and feasible.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Huo, L., Jiang, D., Lv, Z., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Computational Intelligence, pp. 1–21 (2019)

    Google Scholar 

  2. Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. Chin. Commun. 7(1), 196–207 (2019)

    Google Scholar 

  3. Zhang, K., Chen, L., An, Y., et al.: A QoE test system for vehicular voice cloud services. Mobile Network Application (2019). 10.1007/s11036-019-01415-3

    Google Scholar 

  4. Chen, L., Jiang, D., Bao, R., Xiong, J., Liu, F., Bei, L.: MIMO scheduling effectiveness analysis for bursty data service from view of QoE. Chin. J. Electron. 26(5), 1079–1085 (2017)

    Article  Google Scholar 

  5. D. Jiang, Y. Wang, Z. Lv, et al.: Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics, 16(2):1310–1320, (2020)

    Google Scholar 

  6. Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 1(1), 1–12 (2018)

    Google Scholar 

  7. Chen, L., et al.: A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access 6(1), 15408–15419 (2018)

    Article  Google Scholar 

  8. Tan, J., Xiao, S., Han, S., Liang, Y., Leung, V.C.M.: QoS-aware user association and resource allocation in LAA-LTE/WiFi Coexistence systems. IEEE Trans. Wireless Commun. 18(4), 2415–2430 (2019)

    Article  Google Scholar 

  9. Wang, Y., Tang, X., Wang, T.: A unified QoS and security provisioning framework for wiretap cognitive radio networks: a statistical queueing analysis approach. IEEE Trans. Wireless Commun. 18(3), 1548–1565 (2019)

    Article  Google Scholar 

  10. Hassan, M.Z., Hossain, M.J., Cheng, J., Leung, V.C.M.: Hybrid RF/FSO backhaul networks with statistical-QoS-aware buffer-aided relaying. IEEE Trans. Wireless Commun. 19(3), 1464–1483 (2020)

    Article  Google Scholar 

  11. Zhang, Z., Wang, R., Yu, F.R., Fu, F., Yan, Q.: QoS aware transcoding for live streaming in edge-clouds aided hetnets: an enhanced actor-critic approach. IEEE Trans. Veh. Technol. 68(11), 11295–11308 (2019)

    Article  Google Scholar 

  12. Chen, L., Zhang, L.: Spectral efficiency analysis for wireless network system under QoS constraint: an effective capacity perspective. Mobile Network Application (2020). 10.1007/s11036-019-01414-4

    Google Scholar 

  13. Wang, F., Jiang, D., Qi, S., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mobile Networks and Applications Online Available (2019)

    Google Scholar 

  14. Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305–3319 (2018)

    Article  Google Scholar 

  15. Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet Things J. 3(6), 1437–1447 (2016)

    Article  Google Scholar 

  16. Lee, Y., Kim, Y., Park, S.: A machine learning approach that meets axiomatic properties in probabilistic analysis of LTE spectral efficiency. In: 2019 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea (South), pp. 1451–1453 (2019)

    Google Scholar 

  17. Ji, H., Sun, C., Shieh, W.: Spectral efficiency comparison between analog and digital RoF for mobile fronthaul transmission link. J. Lightwave Technol. 38(20), 5617–5623 (2020)

    Google Scholar 

  18. Hayati, M., Kalbkhani, H., Shayesteh, M.G.: Relay selection for spectral-efficient network-coded multi-source D2D communications. In: 2019 27th Iranian Conference on Electrical Engineering (ICEE), Yazd, Iran, pp. 1377–1381 (2019)

    Google Scholar 

  19. You, L., Xiong, J., Zappone, A., Wang, W., Gao, X.: Spectral efficiency and energy efficiency tradeoff in massive MIMO downlink transmission with statistical CSIT. IEEE Trans. Sign. Process. 68, 2645–2659 (2020)

    Article  MathSciNet  Google Scholar 

  20. Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220, 160–169 (2017)

    Google Scholar 

  21. Wiatr, P., Chen, J., Monti, P., Wosinska, L.: Energy efficiency versus reliability performance in optical backbone networks. IEEE/OSA J. Opt. Commun. Netw. 7(3), A482–A491 (2015)

    Article  Google Scholar 

  22. Jiang, D., Wang, Y., Lv, Z., et al.: Intelligent optimization-based reliable energy-efficient networking in cloud services for IIoT networks. In: IEEE Journal on Selected Areas in Communications, Online Available (2019)

    Google Scholar 

  23. Jiang, D., Wang, W., Shi, L., et al.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 5(3), 1–12 (2018)

    Google Scholar 

  24. Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. Plos One 13(5), 1–23 (2018)

    Google Scholar 

  25. Wang, Y., Jiang, D., Huo, L., et al.: A new traffic prediction algorithm to software defined networking. Mobile Networks and Applications, Online Available (2019)

    Google Scholar 

  26. Barakabitze, A.A., et al.: QoE management of multimedia streaming services in future networks: a tutorial and survey. IEEE Commun. Surv. Tutorials 22(1), 526–565 (2020)

    Article  Google Scholar 

  27. Orsolic, I., Skorin-Kapov, L.: A framework for in-network QoE monitoring of encrypted video streaming. IEEE Access 8, 74691–74706 (2020)

    Article  Google Scholar 

  28. Song, E., et al.: Threshold-oblivious on-line web QoE assessment using neural network-based regression model. IET Commun. 14(12), 2018–2026 (2020)

    Article  Google Scholar 

  29. Seufert, M., Wassermann, S., Casas, P.: Considering user behavior in the quality of experience cycle: towards proactive QoE-aware traffic management. IEEE Commun. Lett. 23(7), 1145–1148 (2019)

    Article  Google Scholar 

  30. Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mobile Networks and Applications, Online Available (2019)

    Google Scholar 

  31. Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mobile Networks and Applications, Online Available (2019)

    Google Scholar 

  32. Huo, L., Jiang, D., Zhu, X., et al.: An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. International Journal of Communication Systems, Online Available, pp. 1–12 (2019)

    Google Scholar 

  33. Zaborovsky, V., Meylanov, R.: Informational network traffic model based on fractional calculus. In: International Conferences on Info-tech & Info-net (2001)

    Google Scholar 

  34. Guo, C., Liang, L., Li, G.Y.: Resource allocation for low-latency vehicular communications: an effective capacity perspective. IEEE J. Sel. Areas Commun. 37(4), 905–917 (2019)

    Article  Google Scholar 

  35. Shehab, M., Alves, H., Latva-aho, M.: Effective capacity and power allocation for machine-type communication. IEEE Trans. Veh. Technol. 68(4), 4098–4102 (2019)

    Article  Google Scholar 

  36. Cui, Q., Gu, Y., Ni, W., Liu, R.P.: Effective capacity of licensed-assisted access in unlicensed spectrum for 5G: from theory to application. IEEE J. Sel. Areas Commun. 35(8), 1754–1767 (2017)

    Article  Google Scholar 

  37. Xiao, C., Zeng, J., Ni, W., Liu, R.P., Su, X., Wang, J.: Delay guarantee and effective capacity of downlink NOMA fading channels. IEEE J. Sel. Top. Sign. Process. 13(3), 508–523 (2019)

    Article  Google Scholar 

  38. Björnson, E., Larsson, E.G., Debbah, M.: Massive MIMO for maximal spectral efficiency: how many users and pilots should be allocated? IEEE Trans. Wireless Commun. 15(2), 1293–1308 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by Jiangsu technology project of Housing and Urban-Rural Development (No.2018ZD265) and Jiangsu major natural science research project of College and University (No. 19KJA470002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, L., Cui, P., Zhang, K., An, Y. (2021). Analysis on Relationship Between Fractional Calculus Fluid Model and Effective Capacity of Bursty Data Service in Multi-hop Wireless Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72795-6_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72794-9

  • Online ISBN: 978-3-030-72795-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics