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Queue Regret Analysis Under Fixed Arrival Rate and Fixed Service Rates

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Simulation Tools and Techniques (SIMUtools 2020)

Abstract

In wireless communication, transmitter often need choose one channel from several available ones. Since the instantaneous channel rate is time-varying with unknown statistics, the channel selection is based on observation. Evaluating the lost of scheduling based on observation is an important for design scheduling policy. By adopting the concept of queue regret fact, we carry out simulation under different arrival rate and channel service rate. As arrival rate is approaching the service rate of the best channel, the queue regret has a shape increase in our simulation. However, even if the arrival rate is higher than best service rate, the transmitter have still chance to find the best channel, and the queue regret will converge. The relationship between arrival rate, service rate, queue length and queue regret is analyzed in the simulation.

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References

  1. Zhang, K., Chen, L., An, Y., Cui, P.: A QoE test system for vehicular voice cloud services. Mobile Netw. Appl. 1, 6 (2019). https://doi.org/10.1007/s11036-019-01415-3

    Article  Google Scholar 

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

    Google Scholar 

  3. Huo, L., Jiang, D., Lv, Z., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 36, 151–171 (2019)

    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. Chinese J. Electron. 26(5), 1079–1085 (2017)

    Article  Google Scholar 

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

    Article  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)

    MathSciNet  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. Chen, L., Zhang, L.: Spectral efficiency analysis for massive MIMO system under QoS constraint: an effective capacity perspective. Mobile Netw. Appl. 1, 9 (2020). https://doi.org/10.1007/s11036-019-01414-4

    Article  Google Scholar 

  9. Wang, F., Jiang, D., Qi, S., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mobile Netw. Appl. (2019)

    Google Scholar 

  10. 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 

  11. 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 

  12. 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)

    Article  Google Scholar 

  13. Jiang, D., Wang, Y., Lv, Z., et al.: Intelligent optimization-based reliable energy-efficient networking in cloud services for IIoT networks. IEEE J. Sel. Areas Commun. (2019)

    Google Scholar 

  14. Dataesatu, A., Boonsrimuang, P., Mori, K., Boonsrimuang, P.: Energy efficiency enhancement in 5G heterogeneous cellular networks using system throughput based sleep control scheme. In: 2020 22nd International Conference on Advanced Communication Technology (ICACT), Phoenix Park, PyeongChang, Korea (South), pp. 549–553 (2020)

    Google Scholar 

  15. 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), 1–2 (2018)

    Google Scholar 

  16. 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 

  17. Wang, Y., Jiang, D., Huo, L., et al.: A new traffic prediction algorithm to software defined networking. Mobile Netw. Appl. (2019)

    Google Scholar 

  18. Derrmann, T., Frank, R., Viti, F., Engel, T.: How road and mobile networks correlate: estimating urban traffic using handovers. IEEE Trans. Intell. Transp. Syst. 21(2), 521–530 (2020)

    Article  Google Scholar 

  19. Kamath, S., Singh, S., Kumar, M.S.: Multiclass queueing network modeling and traffic flow analysis for SDN-enabled mobile core networks with network slicing. IEEE Access 8, 417–430 (2020)

    Article  Google Scholar 

  20. Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mobile Netw. Appl. (2019)

    Google Scholar 

  21. Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mobile Netw. Appl. (2019)

    Google Scholar 

  22. Marí-Altozano, M.L., Luna-Ramírez, S., Toril, M., Gijón, C.: A QoE-driven traffic steering algorithm for LTE networks. IEEE Trans. Veh. Technol. 68(11), 11271–11282 (2019)

    Article  Google Scholar 

  23. Zhong, Y., Wang, G., Han, T., Wu, M., Ge, X.: QoE and cost for wireless networks with mobility under spatio-temporal traffic. IEEE Access 7, 47206–47220 (2019)

    Article  Google Scholar 

  24. Tian, F., Yu, Y., Li, D., Cui, J., Dong, Y.: QoE optimization for traffic offloading from LTE to WiFi. In: 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan, pp. 115–116 (2019)

    Google Scholar 

  25. Oliveira, T., Sargento, S.: QoE-based load balancing of OTT video content in SDN networks. 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, pp. 1–6 (2019)

    Google Scholar 

  26. 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 

  27. Ge, M., Chen, W., Zeng, Y.: A SDN-based QoE-aware routing algorithm on video. In: 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, pp. 147–152 (2019)

    Google Scholar 

  28. Kimura, T., Kimura, T., Matsumoto, A., Okamoto, J.: BANQUET: balancing quality of experience and traffic volume in adaptive video streaming. In: 2019 15th International Conference on Network and Service Management (CNSM), Halifax, NS, Canada, pp. 1–7 (2019)

    Google Scholar 

  29. Trakas, P., Adelantado, F., Verikoukis, C.: QoE-aware resource allocation for profit maximization under user satisfaction guarantees in HetNets with differentiated services. IEEE Syst. J. 13(3), 2664–2675 (2019)

    Article  Google Scholar 

  30. Saliba, D., Imad, R., Houcke, S.: Wifi channel selection based on load criteria. In: 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC), Bali, pp. 332–336 ( 2017)

    Google Scholar 

  31. Ozduran, V.: Leakage rate based hybrid untrustworthy relay selection with channel estimation error. In: 2017 25th Telecommunication Forum (TELFOR), Belgrade, pp. 1–4 (2017)

    Google Scholar 

  32. Odeyemi, K.O., Owolawi, P.A.: Performance analysis of cooperative NOMA with partial relay selection under outdated channel estimate. In: 2019 IEEE 2nd Wireless Africa Conference (WAC), Pretoria, South Africa, pp. 1–5 (2019)

    Google Scholar 

  33. Hasegawa, S., Kim, S., Shoji, Y., Hasegawa, M.: Performance evaluation of machine learning based channel selection algorithm implemented on IoT sensor devices in coexisting IoT networks. In: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, pp. 1–5 (2020)

    Google Scholar 

  34. Hussein, H.S., Hussein, S., Mohamed, E.M.: Efficient channel estimation techniques for MIMO systems with 1-bit ADC. China Commun. 17(5), 50–64 (2020)

    Article  Google Scholar 

  35. Zhao, Y., Zhao, W., Wang, G., Ai, B., Putra, H.H., Juliyanto, B.: AoA-based channel estimation for massive MIMO OFDM communication systems on high speed rails. China Commun. 17(3), 90–100 (2020)

    Article  Google Scholar 

  36. Miao, J., Chen, Y., Mai, Z.: A novel millimeter wave channel estimation algorithm based on IC-ELM. In: 2019 28th Wireless and Optical Communications Conference (WOCC), Beijing, China, pp. 1–5 (2019)

    Google Scholar 

  37. Rasheed, O.K., Surabhi, G.D., Chockalingam, A.: Sparse delay-doppler channel estimation in rapidly time-varying channels for multiuser OTFS on the uplink. In: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium,pp. 1–5 (2020)

    Google Scholar 

  38. Stahlbuhk, T., Shrader, B., Modiano, E.: Learning algorithms for mining queue length regret. In: 2018 IEEE International Symposium on Information (2018)

    Google Scholar 

  39. Bubeck, S., Cesa-Bianchi, N.: Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Found. Trends Mach. Learn. 5(1), 1–122 (2012)

    Article  Google Scholar 

  40. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)

    Article  Google Scholar 

  41. Krishnasamy, S., et al.: Regret of queueing bandits. In: Proceedings of Neural Information Processing Systems, pp. 1669–1677 (2016)

    Google Scholar 

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Acknowledgements

This work is partly supported by Jiangsu major natural science research project of College and University (No. 19KJA470002) and Jiangsu technology project of Housing and Urban-Rural Development (No. 2019ZD041).

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Correspondence to Lei Chen .

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Cui, P., Chen, L., Shi, Y., Zhang, K., An, Y. (2021). Queue Regret Analysis Under Fixed Arrival Rate and Fixed Service Rates. 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_45

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  • DOI: https://doi.org/10.1007/978-3-030-72795-6_45

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