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A machine learning approach for dynamic control of RTS/CTS in WLANs

Published: 05 November 2018 Publication History

Abstract

In this paper, we proposed a novel algorithm to dynamically enable and disable IEEE 802.11 DCF's RTS/CTS handshake. We start by conducting an experimental characterization of the performance of RTS/CTS as a function of packet size, transmission rate, and network contention, which complements existing work that evaluated RTS/CTS performance analytically and empirically. Motivated by our experimental evaluation of RTS/CTS performance, our algorithm uses current packet size and transmission rate, as well as an estimate of network contention to dynamically decide whether to use RTS/CTS or not. To the best of our knowledge, the proposed algorithm is the first to enable and disable the RTS/CTS handshake based on a set of current network conditions, and automatically adapt as these conditions change. Simulation results using a variety of WLAN scenarios, including synthetic and real traffic traces, demonstrate that the proposed approach consistently outperforms current best practices, such as never enabling RTS/CTS or setting the RTS Threshold (RT), which is used to decide whether to switch RTS/CTS on or off, to a static value.

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Cited By

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  • (2024)OrthcatterProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691897(1301-1314)Online publication date: 16-Apr-2024
  • (2022)Deep Learning Based MAC via Joint Channel Access and Rate Adaptation2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)10.1109/VTC2022-Spring54318.2022.9860617(1-7)Online publication date: Jun-2022
  • (2022)The ratio model between throughput and delay based on payload transmission time in wireless blockchain networkScientific Reports10.1038/s41598-022-19138-z12:1Online publication date: 12-Sep-2022
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      cover image ACM Other conferences
      MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
      November 2018
      490 pages
      ISBN:9781450360937
      DOI:10.1145/3286978
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

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      Published: 05 November 2018

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      Author Tags

      1. IEEE 802.11
      2. WLAN
      3. enabling/disabling RTS/CTS
      4. machine learning

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      MobiQuitous '18
      MobiQuitous '18: Computing, Networking and Services
      November 5 - 7, 2018
      NY, New York, USA

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      Overall Acceptance Rate 26 of 87 submissions, 30%

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      Cited By

      View all
      • (2024)OrthcatterProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691897(1301-1314)Online publication date: 16-Apr-2024
      • (2022)Deep Learning Based MAC via Joint Channel Access and Rate Adaptation2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)10.1109/VTC2022-Spring54318.2022.9860617(1-7)Online publication date: Jun-2022
      • (2022)The ratio model between throughput and delay based on payload transmission time in wireless blockchain networkScientific Reports10.1038/s41598-022-19138-z12:1Online publication date: 12-Sep-2022
      • (2019)Dynamically Tuning IEEE 802.11's Contention Window Using Machine LearningProceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems10.1145/3345768.3355920(19-26)Online publication date: 25-Nov-2019

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