[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

An Online Algorithm for Effective Capacity Estimation

  • Conference paper
  • First Online:
Simulation Tools and Techniques (SIMUtools 2021)

Abstract

Effective Capacity is an important metric to measure the capacity of a wireless channel. However, the estimation algorithm cost a lot of computation time. The current estimation algorithm thus cannot predict the real-time Effective Capacity for online service. An online estimation algorithm is proposed to reduce computation time cost in this paper. A simulation is designed with QoS constraint. The simulation results illustrate that the proposed algorithm save a lot of computation time.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /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
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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. Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196–207 (2019)

    Google Scholar 

  2. Huo, L., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 36(1), 151–171 (2020)

    Google Scholar 

  3. Zhang, K., Chen, L., An, Y., Cui, P.: A QoE test system for vehicular voice cloud services. Mob. Netw. Appl. 26(2), 700–715 (2019). https://doi.org/10.1007/s11036-019-01415-3

    Article  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. Jiang, D., et al.: Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans. Ind. Inform. 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. Barakabitze, A.A., et al.: QoE management of multimedia streaming services in future networks: a tutorial and survey. IEEE Commun. Surv. Tutor. 22(1), 526–565 (2020)

    Article  Google Scholar 

  9. 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 

  10. 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 

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

    Article  Google Scholar 

  12. Chen, L., Zhang, L.: Spectral efficiency analysis for massive MIMO system under QoS constraint: an effective capacity perspective. Mob. Netw. Appl. 26(2), 691–699 (2020). https://doi.org/10.1007/s11036-019-01414-4

    Article  Google Scholar 

  13. Wang, F., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mob. Netw. Appl. 26, 597–608 (2021)

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

    Article  Google Scholar 

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

    Google Scholar 

  18. Jiang,  D., Wang, Y.,  Lv, Z., Wang, W., Wang, H.: An energy-efficient networking approach in cloud services for IIoT networks. IEEE J. Sel. Areas Commun. 38(5), 928–941 (2020)

    Google Scholar 

  19. 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 

  20. 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 

  21. Wang, Y., et al.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. 26, 716–725 (2021)

    Google Scholar 

  22. 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), pp. 1451–1453. Jeju Island, Korea (South) (2019)

    Google Scholar 

  23. 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 

  24. 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), pp. 1377–1381. Yazd, Iran (2019)

    Google Scholar 

  25. 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. Signal Process. 68, 2645–2659 (2020)

    Article  MathSciNet  Google Scholar 

  26. Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. 26, 726–735 (2021)

    Google Scholar 

  27. Huo, L., et al.: An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int. J. Commun. Syst. 1–12 (2019, early access). https://doi.org/10.1002/dac.4092

  28. 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. Wirel. Commun. 18(4), 2415–2430 (2019)

    Article  Google Scholar 

  29. 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. Wirel. Commun. 18(3), 1548–1565 (2019)

    Article  Google Scholar 

  30. 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. Wirel. Commun. 19(3), 1464–1483 (2020)

    Article  Google Scholar 

  31. Wu, D., Negi, R.: Effective capacity: a wireless link model for support of quality of service. IEEE Trans. Wirel. Commun. 2(4), 630–643 (2003)

    Google Scholar 

  32. Gao, X., Edfors, O., Rusek, F., Tufvesson, F.: Massive MIMO performance evaluation based on measured propagation data. IEEE Trans. Wirel. Commun. 14(7), 3899–3911 (2015)

    Article  Google Scholar 

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

    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. Signal 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. Wirel. Commun. 15(2), 1293–1308 (2016)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported in part by Xu Zhou Science and Technology Plan Project (Grant No. KC21309).

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

© 2022 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. (2022). An Online Algorithm for Effective Capacity Estimation. In: Jiang, D., Song, H. (eds) Simulation Tools and Techniques. SIMUtools 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-97124-3_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-97124-3_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97123-6

  • Online ISBN: 978-3-030-97124-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics