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Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism

  • Green and Human Information Technology 2019
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Abstract

Quality of service (QoS) implementation in a wireless local area network (WLAN) enables the prediction of network performance and utilization of effective bandwidth for multimedia applications. In QoS-supported WLAN, enhanced distributed channel access (EDCA) adjusts back-off parameters to implement priority-based channel access at the medium access control (MAC) layer. Although conventional QoS-supported EDCA in WLANs can provide a certain degree of QoS guarantee, the performance of best effort data (low-priority) traffic is sacrificed owing to the blind use of a binary exponential back-off (BEB) mechanism for collision avoidance among WLAN stations (STAs). In EDCA, the BEB mechanism exponentially increases the contention window (CW[AC]) for any specific priority access category (AC) when collision occurs and resets it to its initial size after successful data transmission. This increase and reset of CW[AC] is performed regardless of the network density inference, i.e., a scarce WLAN does not require an unnecessary exponential increase in CW[AC]. Similarly, a dense WLAN causes more collisions if CW[AC] is reset to its initial minimum size. Machine-learning algorithms can scrutinize an STA’s experience for WLAN inference. Therefore, in this study, we propose a machine-learning-enabled EDCA (MEDCA) mechanism for QoS-supported MAC layer channel access in dense WLANs. This mechanism utilizes a Q-learning algorithm, which is one of the prevailing models of machine learning, to infer the network density and adjust its back-off CW[AC] accordingly. Simulation results show that MEDCA performs better as compared to the conventional EDCA mechanism in QoS-supported dense WLANs.

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References

  1. Ali R, Kim SW, Kim B-S, Park Y (2016) Design of MAC layer resource allocation schemes for IEEE 802.11ax: future directions. IETE Techn Rev 35(1):28–56. https://doi.org/10.1080/02564602.2016.1242387

    Article  Google Scholar 

  2. Alpaydm E (2014) Introduction to machine learning, 3rd edn. The MIT Press, Cambridge

    Google Scholar 

  3. Challita U, Dong L, Saad W (2017) Proactive resource management in LTE-U systems: a deep learning perspective. arXiv:1702.07031. Accessed on 20 Dec 2018

  4. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction, 2nd edn. MIT Press, Cambridge

    MATH  Google Scholar 

  5. Afzal MK, Kim B, Kim SW (2014) A leader-based reliable multicast MAC protocol for multimedia applications. KSII Trans Internet Inf Syst 8(1):183–195. https://doi.org/10.1007/978-3-642-34883-9_9

    Article  Google Scholar 

  6. Shahin N, Ali R, Kim SW, Kim Y (2019) Cognitive backoff mechanism for IEEE802.11ax high-efficiency WLANs. KICS J Commun Netw 21(2):158–167. https://doi.org/10.1109/JCN.2019.000022

    Article  Google Scholar 

  7. Zhu H, Li M, Chlamtac I, Prabhakaran B (2004) A survey of quality of service in IEEE 802.11 networks. IEEE Wirel Commun 11(4):6–14. https://doi.org/10.1109/MWC.2004.1325887

    Article  Google Scholar 

  8. Ni Q, Romdhani L, Turletti T (2004) A survey of QoS enhancements for IEEE 802.11 wireless LAN. J Wirel Commun Mobile Comput 4:1–20. https://doi.org/10.1002/wcm.196

    Article  Google Scholar 

  9. Romdhani L, Ni Q, Turletti T (2003) Adaptive EDCF: enhanced service differentiation for IEEE 802.11 wireless ad-hoc networks. IEEE Wireless Communications and Networking (WCNC 2003). New Orleans, LA, USA 2:1373–1378. https://doi.org/10.1109/WCNC.2003.1200574

  10. Wang C, Li B, Li L (2004) A new collision resolution mechanism to enhance the performance of IEEE 802.11 DCF. IEEE Trans Veh Technol 53(4):1235–1246. https://doi.org/10.1109/TVT.2004.830951

    Article  Google Scholar 

  11. Zhu H, Cao G, Yener A, Mathias AD (2004) EDCF-DM: a novel enhanced distributed coordination function for wireless ad hoc networks. In: IEEE international conference on communications, Paris, France, vol 7, pp 3886–3890. https://doi.org/10.1109/ICC.2004.1313280

  12. Kwon Y, Fang Y, Latchman H (2003) A novel MAC protocol with fast collision resolution for wireless LANs. In: IEEE INFOCOM 2003, vol 2, San Francisco, CA, pp 853–862. https://doi.org/10.1109/INFCOM.2003.1208923

  13. Malli M, Ni Q, Turletti T, Barakat C (2004) Adaptive fair channel allocation for QoS enhancement in IEEE 802.11 wireless LANs. In: IEEE international conference on communications (IEEE ICC), Paris, France, vol 6, pp 3470–3475. https://doi.org/10.1109/ICC.2004.1313189

  14. Ali R, Shahin N, Kim Y, Kim B, Kim SW (2018) Channel observation-based scaled backoff mechanism for high-efficiency WLANs. IET Electron Lett 54(10):663–665. https://doi.org/10.1049/el.2018.0617

    Article  Google Scholar 

  15. Ali R, Shahin N, Bajracharya R, Kim B, Kim SW (2018) A self-scrutinized backoff mechanism for IEEE 802.11ax in 5G unlicensed networks. Sustainability 10(4):1–15. https://doi.org/10.3390/su10041201

    Article  Google Scholar 

  16. The Network Simulator—ns-3. Available: https://www.nsnam.org/. Accessed on 10 Jan 2019

  17. Even-Dar E, Mansour Y (2003) Learning rates for Q-learning. J Mach Learn Res 5:1–25

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the 2019 Yeungnam University Research Grant.

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea

    Rashid Ali, Ali Nauman, Yousaf Bin Zikria & Sung Won Kim

  2. Department of Computer and Information Communication Engineering, Hongik University, Seoul, 04066, Republic of Korea

    Byung-Seo Kim

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  1. Rashid Ali
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  2. Ali Nauman
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Correspondence to Sung Won Kim.

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Ali, R., Nauman, A., Zikria, Y.B. et al. Performance optimization of QoS-supported dense WLANs using machine-learning-enabled enhanced distributed channel access (MEDCA) mechanism. Neural Comput & Applic 32, 13107–13115 (2020). https://doi.org/10.1007/s00521-019-04416-1

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