Integral sliding-mode robust observer-based congestion control for wireless access networks

Khoshnevisan, Ladan; Salmasi, Farzad Rajaei; Liu, Xinzhi

doi:10.1080/23307706.2021.1929526

Cited by 2 publications

(1 citation statement)

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“…The Transmission Control Protocol (TCP) [2] foundational congestion method guarantees system reliability and fairness in the utilization of resources. Congestion Control (CC) [3] mechanisms for wired networks were developed in the 1980s and are still used today. There are predefined rules that govern how the Congestion Window (CW) [4] is reduced when packet reduction is identified, and how it is adjusted based on Round-Trip Time (RTT) measurements.…”

Section: Fig 1 Types Of MLmentioning

confidence: 99%

Application of Machine Learning Technologies for Transport layer Congestion Control

Dong

Yortsos

2022

JCNS

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Due to the advent of technology, humans now live in the modern age of information and data. In this form of world, different objects are interlinked to data sources, and every aspect of human’s lives are recorded in a digital form. For example, the present electronic globe has an abundance of distinct forms of data e.g., health data, social media fata, smartphone data, business data, smart city data, cybersecurity data and Internet of Things (IoT) data, including Covid-19 data. Data can be unstructured, semi-structured and structured, and this is increasing on a daily basis. Machine Learning (ML) is significantly employed in different aspects of real-life e.g., Congestion Control (CC). This paper provides an evaluation of the aspect ML employed in CC. CC has emerged as a fundamental viewpoint in communications system infrastructure in the recent years, since network operations, and network capacity have enhanced at a rapid rate.

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Section: Fig 1 Types Of MLmentioning

confidence: 99%

Application of Machine Learning Technologies for Transport layer Congestion Control

Dong

Yortsos

2022

JCNS

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Computationally efficient congestion controller for data transmission networks with variable packet loss

Latosiński

2021

2021 25th International Conference on System Theory, Control and Computing (ICSTCC)

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Integral sliding-mode robust observer-based congestion control for wireless access networks

Cited by 2 publications

References 50 publications

Application of Machine Learning Technologies for Transport layer Congestion Control

Application of Machine Learning Technologies for Transport layer Congestion Control

Computationally efficient congestion controller for data transmission networks with variable packet loss

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