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Year 2022, Volume 14, Issue 1, 322 - 337, 31.01.2022
https://doi.org/10.29137/umagd.1075903

Abstract

References

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  • Gabale, V., Raman, B., Dutta, P., & Kalyanraman, S. (2013). A classification framework for scheduling algorithms in wireless mesh networks. IEEE Communications Surveys and Tutorials, 15(1), 199–222. https://doi.org/10.1109/SURV.2012.022412.00068
  • Ghanem, W. R., Jamali, V., Sun, Y., & Schober, R. (2019). Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems. 2019 IEEE International Conference on Communications Workshops (ICC Workshops), 1–6. https://doi.org/10.1109/ICCW.2019.8756746
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Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Year 2022, Volume 14, Issue 1, 322 - 337, 31.01.2022
https://doi.org/10.29137/umagd.1075903

Abstract

Wireless mobile communications have experienced tremendous growth in the number of users, data rate requirements, and coverage in recent years. As the data rate and system throughput requirements increase, researchers and system designers need to develop efficient methods to meet these requirements with reasonable effort and cost. In this paper, we discuss an efficient approach to deal with diminishing the overhead of Downlink (DL) and feedback using Machine Learning (ML) for 802.11ax. In particular, the antennas that are related to the router were divided into two gatherings. We used good samples of Channel State Information (CSI) that were taken from an open-access dataset and used it to train our linear regression model. The first group of antennas was used as input to our model and the second group was used as the output of the model. In the online mode, we need to estimate only one group of antennas and for the second group, we can predict it using the trained linear regression model by using the estimated CSI group as input to the model. Therefore, the output of the model using as the CSI for the second group. In this way, we can reduce the overhead of the DL in the router as shown in the result table so the router will work more efficiently compared to the existing systems. From the results table in the last section, the average sum rate has increased between 20% and 30%.

References

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  • Afaqui, M. S., Garcia-Villegas, E., Lopez-Aguilera, E., Smith, G., & Camps, D. (2015). Evaluation of dynamic sensitivity control algorithm for IEEE 802.11ax. 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015, 1060–1065. https://doi.org/10.1109/WCNC.2015.7127616
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  • Baghi, S., & Daneshvar Farzanegan, M. (2015). A novel delay based scheduling algorithm for video traffic in LTE. 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 514–520. https://doi.org/10.1109/KBEI.2015.7436098
  • Bankov, D., Didenko, A., Khorov, E., Loginov, V., & Lyakhov, A. (2017). IEEE 802.11ax uplink scheduler to minimize, delay: A classic problem with new constraints. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017-Octob, 1–5. https://doi.org/10.1109/PIMRC.2017.8292382
  • Bankov, D., Didenko, A., Khorov, E., & Lyakhov, A. (2018). OFDMA Uplink Scheduling in IEEE 802.11ax Networks. 2018 IEEE International Conference on Communications (ICC), 1–6. https://doi.org/10.1109/ICC.2018.8422767
  • Bazzi, A., Zanella, A., Cecchini, G., & Masini, B. M. (2019). Analytical investigation of two benchmark resource allocation algorithms for LTE-v2v. IEEE Transactions on Vehicular Technology, 68(6), 5904–5916. https://doi.org/10.1109/TVT.2019.2909438
  • Bellalta, B., & Kosek-Szott, K. (2019). AP-initiated multi-user transmissions in IEEE 802.11ax WLANs. Ad Hoc Networks, 85, 145–159. https://doi.org/10.1016/j.adhoc.2018.10.021
  • Bhagwat, P., Bhattacharya, P., Krishna, A., & Tripathit, S. K. (1996). Enhancing throughput over wireless LANs using channel state dependent packet scheduling. Proceedings of IEEE INFOCOM ’96. Conference on Computer Communications, 1133–1140. https://doi.org/10.1109/INFCOM.1996.493057
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  • Bi, S., Zhang, R., Ding, Z., & Cui, S. (2015). Wireless Communications in the Era of Big Data. IEEE Communications Magazine, 53(10), 190–199. https://doi.org/10.1109/MCOM.2015.7295483
  • Bottigliengo, M., Casetti, C., Chiasserini, C. F., & Meo, M. (2004). Short-term fairness for TCP flows in 802.11b WLANs. Proceedings - IEEE INFOCOM, 1383–1392. https://doi.org/10.1109/INFCOM.2004.1357023
  • Cao, X., Ma, R., Liu, L., Shi, H., Cheng, Y., & Sun, C. (2018). A Machine Learning-Based Algorithm for Joint Scheduling and Power Control in Wireless Networks. IEEE Internet of Things Journal, 5(6), 4308–4318. https://doi.org/10.1109/JIOT.2018.2853661
  • Chao, I. F., Chiou, C. S., & Hsu, K. (2015). A group-oriented QoS-enhanced proportional fair scheduling algorithm over downlink OFDMA-based networks. 4th International Symposium on Next-Generation Electronics, IEEE ISNE 2015, 1–4. https://doi.org/10.1109/ISNE.2015.7131958
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  • Cui, W., Shen, K., & Yu, W. (2019). Spatial Deep Learning for Wireless Scheduling. IEEE Journal on Selected Areas in Communications, 37(6), 1248–1261. https://doi.org/10.1109/JSAC.2019.2904352
  • Das, P., Sen, S., & Banerjee, A. (2013). Surveying Best Suitable Scheduling Algorithm for Wimax- Wi-Fi Integrated Heterogeneous Network. March, 329–346.
  • Dogar, F. R., Steenkiste, P., & Papagiannaki, K. (2010). Catnap: Exploiting high bandwidth wireless interfaces to save energy for mobile devices. MobiSys’10 - Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, 107–122. https://doi.org/10.1145/1814433.1814446
  • Dong, P., Zhang, H., & Li, G. Y. (2018). Machine Learning Prediction based CSI Acquisition for FDD Massive MIMO Downlink. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE. https://doi.org/10.1109/GLOCOM.2018.8647328
  • Dovelos, K., & Bellalta, B. (2018). Optimal Resource Allocation in IEEE 802.11ax Uplink OFDMA with Scheduled Access. Networking and Internet Architecture, 1–17. http://arxiv.org/abs/1811.00957
  • Enayet, A., Mehajabin, N., Razzaque, M. A., Hong, C. S., & Hassan, M. M. (2016). PowerNap: a power-aware distributed Wi-Fi access point scheduling algorithm. Eurasip Journal on Wireless Communications and Networking, 2016(27), 1–13. https://doi.org/10.1186/s13638-016-0522-7
  • Gabale, V., Raman, B., Dutta, P., & Kalyanraman, S. (2013). A classification framework for scheduling algorithms in wireless mesh networks. IEEE Communications Surveys and Tutorials, 15(1), 199–222. https://doi.org/10.1109/SURV.2012.022412.00068
  • Ghanem, W. R., Jamali, V., Sun, Y., & Schober, R. (2019). Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems. 2019 IEEE International Conference on Communications Workshops (ICC Workshops), 1–6. https://doi.org/10.1109/ICCW.2019.8756746
  • Gopalan, A., Caramanis, C., & Shakkottai, S. (2012). On wireless scheduling with partial channel-state information. IEEE TRANSACTIONS ON INFORMATION THEORY, 58(1), 403–420. https://doi.org/10.1109/TIT.2011.2169543
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Details

Primary Language English
Subjects Engineering, Engineering, Electrical and Electronic
Journal Section Articles
Authors

İbrahim MASRİ
KIRIKKALE ÜNİVERSİTESİ
0000-0002-9804-7872
Türkiye


Erdal ERDAL This is me
KIRIKKALE UNIVERSITY
0000-0003-1174-1974
Türkiye

Publication Date January 31, 2022
Published in Issue Year 2022, Volume 14, Issue 1

Cite

APA Masri, İ. & Erdal, E. (2022). Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning . International Journal of Engineering Research and Development , 14 (1) , 322-337 . DOI: 10.29137/umagd.1075903

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