Differentiating Losses in Wireless Networks: A Learning Approach

Yuhao Chen; Jinyao Yan; Yuan Zhang; Karin Anna Hummel

doi:10.1109/INFOCOMWKSHPS54753.2022.9798281

Differentiating Losses in Wireless Networks: A Learning Approach

Yuhao Chen, Jinyao Yan, Yuan Zhang, Karin Anna Hummel

Institute of Telecooperation

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

This paper proposes a learning-based loss differentiation method (LLD) for wireless congestion control. LLD uses a neural network to distinguish between wireless packet loss and congestion packet loss in wireless networks. It can work well in combination with classical packet loss-based congestion control algorithms, such as Reno and Cubic. Preliminary results show that our method can effectively differentiate losses and thus improve throughput in wireless scenarios while maintaining the characteristics of the original algorithms.

Original language	English
Title of host publication	IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Mai 2022
Publisher	IEEE
Pages	1-2
Number of pages	2
DOIs	https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798281
Publication status	Published - Jun 2022

Fields of science

202038 Telecommunications
102 Computer Sciences
102002 Augmented reality
102006 Computer supported cooperative work (CSCW)
102013 Human-computer interaction
102015 Information systems
102021 Pervasive computing
102025 Distributed systems
102027 Web engineering

JKU Focus areas

Digital Transformation
Sustainable Development: Responsible Technologies and Management

Access to Document

10.1109/INFOCOMWKSHPS54753.2022.9798281

Cite this

@inproceedings{3377ddf9e902454987fa4e6e0d59c2f2,

title = "Differentiating Losses in Wireless Networks: A Learning Approach",

abstract = "This paper proposes a learning-based loss differentiation method (LLD) for wireless congestion control. LLD uses a neural network to distinguish between wireless packet loss and congestion packet loss in wireless networks. It can work well in combination with classical packet loss-based congestion control algorithms, such as Reno and Cubic. Preliminary results show that our method can effectively differentiate losses and thus improve throughput in wireless scenarios while maintaining the characteristics of the original algorithms.",

author = "Yuhao Chen and Jinyao Yan and Yuan Zhang and Hummel, {Karin Anna}",

year = "2022",

month = jun,

doi = "10.1109/INFOCOMWKSHPS54753.2022.9798281",

language = "English",

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booktitle = "IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Mai 2022",

publisher = "IEEE",

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AU - Chen, Yuhao

AU - Yan, Jinyao

AU - Zhang, Yuan

AU - Hummel, Karin Anna

PY - 2022/6

Y1 - 2022/6

N2 - This paper proposes a learning-based loss differentiation method (LLD) for wireless congestion control. LLD uses a neural network to distinguish between wireless packet loss and congestion packet loss in wireless networks. It can work well in combination with classical packet loss-based congestion control algorithms, such as Reno and Cubic. Preliminary results show that our method can effectively differentiate losses and thus improve throughput in wireless scenarios while maintaining the characteristics of the original algorithms.

AB - This paper proposes a learning-based loss differentiation method (LLD) for wireless congestion control. LLD uses a neural network to distinguish between wireless packet loss and congestion packet loss in wireless networks. It can work well in combination with classical packet loss-based congestion control algorithms, such as Reno and Cubic. Preliminary results show that our method can effectively differentiate losses and thus improve throughput in wireless scenarios while maintaining the characteristics of the original algorithms.

U2 - 10.1109/INFOCOMWKSHPS54753.2022.9798281

DO - 10.1109/INFOCOMWKSHPS54753.2022.9798281

M3 - Conference proceedings

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EP - 2

BT - IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Mai 2022

PB - IEEE

ER -