DeepRS: Deep-learning Based Network-Adaptive FEC for Real-Time Video Communications

Cheng, Sheng; Hu, Han; Zhang, Xinggong; Guo, Zongming

doi:10.48550/arxiv.2001.07852

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…However, BEC, which is based on re-transmissions, may lead to undesirable communication delays. For example, authors in [101] proposed Deep Reed-Solomon (DeepRS) coding, as a novel FEC algorithm which predicts packet loss using deep NNs to determine the amount of redundant packets. While there is research in the literature attempting to combine event-based control and ML in order to deal with packet loss, the majority of these works make assumptions that limit the applicability of the proposed methods.…”

Section: A Communication Errorsmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

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Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

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Section: A Communication Errorsmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

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“…Automatic Repeat reQuest, ARQ). For example, authors in [63] proposed Deep Reed-Solomon (DeepRS) coding, as a novel FEC algorithm which predicts packet loss with deep NNs to determine the amount of redundant packets. Although there is work in the literature that tries to combine event based control or ML to cope with packet loss, most of these make assumptions that limit the applicability of the proposed methods.…”

Section: A Communication Unreliabilitymentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Sedghi¹,

Ijaz²,

Noor-A-Rahim³

et al. 2020

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Network Control Systems (NCSs) have attracted much interest over the past decade as part of a move towards more decentralised control applications and the rise of cyberphysical system applications. Many practical NCSs face the challenges of limited communication bandwidth resources, reliability and lack of knowledge of network dynamics, particularly when wireless networks are involved. Machine learning (ML) combined with event-triggered control (ETC) has the potential to ease some of these challenges. For example, ML can be used to overcome the problem of a lack of network models by learning system behaviour or adapt to dynamically changing models by continually learning model dynamics. ETC can help to conserve bandwidth resources by communicating only when needed or when resources are available. Here, we present a review of the literature on work that combines ML and ETC. The literature on supervised, semi-supervised, unsupervised and reinforcement learning based approaches such as deep reinforcement learning and statistical learning in combination with ETC is explored. Furthermore, the difference between the application of these learning algorithms on model-based and model-free systems are discussed. Following the analysis of the literature, we highlight open research questions and challenges related to ML-based ETC and propose approaches to possible solutions to these challenges.

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DeepRS: Deep-learning Based Network-Adaptive FEC for Real-Time Video Communications

Cited by 2 publications

References 13 publications

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

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