research-article

Frequency-aware rate adaptation and MAC protocols

Authors:

Hariharan Rahul,

Farinaz Edalat,

Charles G. SodiniAuthors Info & Claims

MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking

Pages 193 - 204

https://doi.org/10.1145/1614320.1614342

Published: 20 September 2009 Publication History

Abstract

There has been burgeoning interest in wireless technologies that can use wider frequency spectrum. Technology advances, such as 802.11n and ultra-wideband (UWB), are pushing toward wider frequency bands. The analog-to-digital TV transition has made 100-250 MHz of digital whitespace bandwidth available for unlicensed access. Also, recent work on WiFi networks has advocated discarding the notion of channelization and allowing all nodes to access the wide 802.11 spectrum in order to improve load balancing. This shift towards wider bands presents an opportunity to exploit frequency diversity. Specifically, frequencies that are far from each other in the spectrum have significantly different SNRs, and good frequencies differ across sender-receiver pairs.

This paper presents FARA, a combined frequency-aware rate adaptation and MAC protocol. FARA makes three departures from conventional wireless network design: First, it presents a scheme to robustly compute per-frequency SNRs using normal data transmissions. Second, instead of using one bit rate per link, it enables a sender to adapt the bitrate independently across frequencies based on these per-frequency SNRs. Third, in contrast to traditional frequency-oblivious MAC protocols, it introduces a MAC protocol that allocates to a sender-receiver pair the frequencies that work best for that pair. We have implemented FARA in FPGA on a wideband 802.11-compatible radio platform. Our experiments reveal that FARA provides a 3.1x throughput improvement in comparison to frequency-oblivious systems that occupy the same spectrum.

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https://doi.org/10.1109/TMC.2022.3214533
Chen SLi CChiu C(2021)An Experience Driven Design for IEEE 802.11ac Rate Adaptation based on Reinforcement LearningIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488876(1-10)Online publication date: 10-May-2021
https://doi.org/10.1109/INFOCOM42981.2021.9488876
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Index Terms

Frequency-aware rate adaptation and MAC protocols
1. Networks
  1. Network protocols
    1. Network protocol design

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cover image ACM Conferences

MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking

September 2009

368 pages

ISBN:9781605587028

DOI:10.1145/1614320

General Chairs:
Kang G. Shin
University of Michigan, USA
,
Yongguang Zhang
Microsoft Research Asia, China
,
Program Chairs:
Rajive Bagrodia
University of California, Los Angeles, USA
,
Ramesh Govindan
University of Southern California, USA

Copyright © 2009 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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Published: 20 September 2009

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MobiCom'09: Annual International Conference on Mobile Computing and Networking

September 20 - 25, 2009

Beijing, China

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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https://doi.org/10.1109/TMC.2022.3214533
Chen SLi CChiu C(2021)An Experience Driven Design for IEEE 802.11ac Rate Adaptation based on Reinforcement LearningIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488876(1-10)Online publication date: 10-May-2021
https://doi.org/10.1109/INFOCOM42981.2021.9488876
Kurniawati ASutisna NLanante Jr. LNagao YKurosaki MOchi H(2020)Retransmission Diversity with Channel Selectivity for High Reliable and Low Latency Industrial Wireless Control SystemJournal of Signal Processing10.2299/jsp.24.8124:3(81-90)Online publication date: 15-May-2020
https://doi.org/10.2299/jsp.24.81
Li CChen SKuo CChiu C(2020)Practical Machine Learning-Based Rate Adaptation Solution for Wi-Fi NICs: IEEE 802.11ac as a Case StudyIEEE Transactions on Vehicular Technology10.1109/TVT.2020.300447169:9(10264-10277)Online publication date: Sep-2020
https://doi.org/10.1109/TVT.2020.3004471
He SWang WYang HCao YJiang TZhang Q(2020)State-Aware Rate Adaptation for UAVs by Incorporating On-Board SensorsIEEE Transactions on Vehicular Technology10.1109/TVT.2019.295028569:1(488-496)Online publication date: Jan-2020
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https://doi.org/10.1109/TMC.2019.2924000
Yin WHu PIndulska JPortmann MMao Y(2020)MAC-layer rate control for 802.11 networks: a surveyWireless Networks10.1007/s11276-020-02295-2Online publication date: 14-Mar-2020
https://doi.org/10.1007/s11276-020-02295-2
ZHAO JLI YLI DWU HZHU B(2019)ORRIS: Throughput Optimization for Backscatter Link on Physical and MAC LayersIEICE Transactions on Communications10.1587/transcom.2018EBP3339E102.B:10(2082-2090)Online publication date: 1-Oct-2019
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Jang JAdib FWu JHall W(2019)Underwater backscatter networkingProceedings of the ACM Special Interest Group on Data Communication10.1145/3341302.3342091(187-199)Online publication date: 19-Aug-2019
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