research-article

Channel-Aware Rate Adaptation for Backscatter Networks

Authors:

Jiangchuan Liu,

Jiangchuan Liu,

Xiaoyu JiAuthors Info & Claims

IEEE/ACM Transactions on Networking (TON), Volume 26, Issue 2

Pages 751 - 764

https://doi.org/10.1109/TNET.2018.2802323

Published: 01 April 2018 Publication History

Abstract

Backscatter communication networks receive much attention recently due to the small size and low power of backscatter nodes. As backscatter communication is often influenced by the dynamic wireless channel quality, rate adaptation becomes necessary. Most existing approaches share a common drawback: they fail to take both spatial and frequency diversity into consideration at the same time. Consequently, the transmission rate may be improperly selected, resulting in low network throughput. In this paper, we propose a channel-aware rate adaptation framework CARA for backscatter networks. CARA incorporates three essential modules, a lightweight channel probing scheme that differentiates collisions from packet losses, a burstiness-aware channel selection mechanism benefiting as many backscatter nodes as possible, a rate selection method choosing the optimal rate, and a mobility detection that discovers location changes. We implement CARA on commercial readers, and the experiment results show that CARA achieves up to $4 \times$ goodput gain compared with the state-of-the-art rate adaptation scheme.

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Cited By

Jiang MGong W(2024)Bidirectional Bluetooth Backscatter With EdgesIEEE Transactions on Mobile Computing10.1109/TMC.2023.324120223:2(1601-1612)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3241202
Chen SGong WLiu JWang ZZhao J(2023)Interference-Aware Mobile Backscatter Communication: A PHY-Assisted Rate Adaptive ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2022.321453322:12(7498-7508)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3214533
Yang YYuan LZhao JGong WBulusu NAryafar EBalasubramanian ASong J(2022)Content-agnostic backscatter from thin airProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services10.1145/3498361.3538930(343-356)Online publication date: 27-Jun-2022
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Channel-Aware Rate Adaptation for Backscatter Networks
1. Networks
  1. Network types
    1. Mobile networks

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 26, Issue 2

April 2018

377 pages

ISSN:1063-6692

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Copyright © 2018.

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IEEE Press

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Published: 01 April 2018

Published in TON Volume 26, Issue 2

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Cited By

Jiang MGong W(2024)Bidirectional Bluetooth Backscatter With EdgesIEEE Transactions on Mobile Computing10.1109/TMC.2023.324120223:2(1601-1612)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2023.3241202
Chen SGong WLiu JWang ZZhao J(2023)Interference-Aware Mobile Backscatter Communication: A PHY-Assisted Rate Adaptive ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2022.321453322:12(7498-7508)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TMC.2022.3214533
Yang YYuan LZhao JGong WBulusu NAryafar EBalasubramanian ASong J(2022)Content-agnostic backscatter from thin airProceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services10.1145/3498361.3538930(343-356)Online publication date: 27-Jun-2022
https://dl.acm.org/doi/10.1145/3498361.3538930
Song GWang WYang HZhang DGao PJiang T(2022)Exploiting Channel Polarization for Reliable Wide-Area Backscatter NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2021.307554921:12(4338-4351)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1109/TMC.2021.3075549
Wang QChen SZhao JGong W(2021)RapidRider: Efficient WiFi Backscatter with Uncontrolled Ambient SignalsIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488716(1-10)Online publication date: 10-May-2021
https://dl.acm.org/doi/10.1109/INFOCOM42981.2021.9488716
Liu HHe JWensowitch JRajan DCamp J(2020)Architecture and experimental evaluation of context-aware adaptation in vehicular networksEURASIP Journal on Wireless Communications and Networking10.1186/s13638-020-01668-72020:1Online publication date: 24-Feb-2020
https://dl.acm.org/doi/10.1186/s13638-020-01668-7
Gong WYuan LWang QZhao JHan DFeldmann A(2020)Multiprotocol backscatter for personal IoT sensorsProceedings of the 16th International Conference on emerging Networking EXperiments and Technologies10.1145/3386367.3432883(261-273)Online publication date: 23-Nov-2020
https://dl.acm.org/doi/10.1145/3386367.3432883
Anh TLuong NNiyato DLiang YKim D(2019)Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885426(1-7)Online publication date: 15-Apr-2019
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Al-Makhadmeh ZTolba A(2019)An Intelligence-Based Recurrent Learning Scheme for Optimal Channel Allocation and Selection in Device-to-Device CommunicationsCircuits, Systems, and Signal Processing10.1007/s00034-019-01056-739:2(997-1018)Online publication date: 13-Feb-2019
https://dl.acm.org/doi/10.1007/s00034-019-01056-7

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