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

Institut für Telekooperation

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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.

Originalsprache	Englisch
Titel	IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Mai 2022
Verlag	IEEE
Seiten	1-2
Seitenumfang	2
ISBN (elektronisch)	9781665409261
DOIs	https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798281
Publikationsstatus	Veröffentlicht - Juni 2022

Wissenschaftszweige

202038 Telekommunikation
102 Informatik
102002 Augmented Reality
102006 Computer Supported Cooperative Work (CSCW)
102013 Human-Computer Interaction
102015 Informationssysteme
102021 Pervasive Computing
102025 Verteilte Systeme
102027 Web Engineering

JKU-Schwerpunkte

Digital Transformation
Sustainable Development: Responsible Technologies and Management

Zugriff auf Dokument

10.1109/INFOCOMWKSHPS54753.2022.9798281

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Verknüpfung zur Publikation in Scopus

Dieses zitieren

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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",

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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.

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