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Predictable 802.11 packet delivery from wireless channel measurements

Authors:

Daniel Halperin,

David WetherallAuthors Info & Claims

SIGCOMM '10: Proceedings of the ACM SIGCOMM 2010 conference

Pages 159 - 170

https://doi.org/10.1145/1851182.1851203

Published: 30 August 2010 Publication History

Abstract

RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (<2 dB for most links) similar to the near-ideal case of narrowband, frequency-flat channels. Unlike RSSI, this lets us predict the highest rate that will work for a link, trim transmit power, and more. We use trace-driven simulation to show that our rate prediction is as good as the best rate adaptation algorithms for 802.11a/g, even over dynamic channels, and extends this good performance to 802.11n.

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https://dl.acm.org/doi/10.1155/2023/1684490
Wang ZZhang JXu MGuo Y(2023)Single-Target Real-Time Passive WiFi TrackingIEEE Transactions on Mobile Computing10.1109/TMC.2022.314111522:6(3724-3742)Online publication date: 1-Jun-2023
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Index Terms

Predictable 802.11 packet delivery from wireless channel measurements
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

cover image ACM Conferences

SIGCOMM '10: Proceedings of the ACM SIGCOMM 2010 conference

August 2010

500 pages

ISBN:9781450302012

DOI:10.1145/1851182

General Chairs:
Shiv Kalyanaraman
IBM Research, India
,
Venkat Padmanabhan
Microsoft Research, India
,
K. K. Ramakrishnan
IBM Research, India
,
Rajeev Shorey
NIIT University, India
,
Program Chairs:
K. K. Ramakrishnan
IBM Research, India
,
Geoffrey M. Voelker
University of California, San Diego, USA

ACM SIGCOMM Computer Communication Review Volume 40, Issue 4
SIGCOMM '10
October 2010
481 pages
ISSN:0146-4833
DOI:10.1145/1851275
Issue’s Table of Contents

Copyright © 2010 ACM.

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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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 August 2010

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SIGCOMM '10: ACM SIGCOMM 2010 Conference

August 30 - September 3, 2010

New Delhi, India

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Overall Acceptance Rate 462 of 3,389 submissions, 14%

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https://doi.org/10.1145/3664196
Wang QYao QLi YZhang YXu C(2023)PLAPMobile Information Systems10.1155/2023/16844902023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/1684490
Wang ZZhang JXu MGuo Y(2023)Single-Target Real-Time Passive WiFi TrackingIEEE Transactions on Mobile Computing10.1109/TMC.2022.314111522:6(3724-3742)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TMC.2022.3141115
Du LTian XZhang LShang SNing X(2023)Device-Free Indoor Localization Based on Multidimensional CSI Features ClassificationIEEE Access10.1109/ACCESS.2023.326327711(32548-32563)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3263277
Armenta-Garcia JGonzalez-Navarro FCaro-Gutierrez JGalaviz-Yanez GIbarra-Esquer JFlores-Fuentes W(2023)Mining Wi-Fi Channel State Information for breathing and heart rate classificationPervasive and Mobile Computing10.1016/j.pmcj.2023.10176891:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2023.101768
Chang CChuang MTan JHsieh CChou C(2022)Indoor Safety Monitoring for Falls or Restricted Areas Using Wi-Fi Channel State Information and Deep Learning Methods in Mega Building Construction ProjectsSustainability10.3390/su14221503414:22(15034)Online publication date: 14-Nov-2022
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Wang ZMa MFeng XLi XLiu FGuo YChen D(2022)Skeleton-Based Human Pose Recognition Using Channel State Information: A SurveySensors10.3390/s2222873822:22(8738)Online publication date: 11-Nov-2022
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Jadhav NWang WZhang DKhatib OKumar SGil S(2022)A wireless signal-based sensing framework for roboticsInternational Journal of Robotics Research10.1177/0278364922109798941:11-12(955-992)Online publication date: 1-Sep-2022
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Onasis VKosasih AGu YHardjawana WQu XVucetic BChikkam K(2022)Improving The Minstrel Rate Adaptation Algorithm using Shallow Neural Networks in IEEE 802.11ah2022 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC51071.2022.9771795(1827-1832)Online publication date: 10-Apr-2022
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Chen SGong WLiu JWang ZZhao J(2022)Interference-Aware Mobile Backscatter Communication: A PHY-Assisted Rate Adaptive ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2022.3214533(1-12)Online publication date: 2022
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