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.
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References
Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196–207 (2019)
Huo, L., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 36(1), 151–171 (2020)
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
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)
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)
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)
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)
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)
Orsolic, I., Skorin-Kapov, L.: A framework for in-network QoE monitoring of encrypted video streaming. IEEE Access 8, 74691–74706 (2020)
Song, E., et al.: Threshold-oblivious on-line web QoE assessment using neural network-based regression model. IET Commun. 14(12), 2018–2026 (2020)
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)
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
Wang, F., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mob. Netw. Appl. 26, 597–608 (2021)
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)
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)
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)
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)
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)
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)
Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. Plos One 13(5), 1–23 (2018)
Wang, Y., et al.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. 26, 716–725 (2021)
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)
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)
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)
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)
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)
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Acknowledgement
This work was supported in part by Xu Zhou Science and Technology Plan Project (Grant No. KC21309).
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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
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DOI: https://doi.org/10.1007/978-3-030-97124-3_33
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