802.11g packet-loss behavior at high sustained bit rates in the home

Russ, Samuel H.; Haghani, Sasan

doi:10.1109/tce.2009.5174455

Cited by 10 publications

(11 citation statements)

References 3 publications

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“…Russ and Haghani [141] presented a distribution of consecutive packet losses as a combination of the classical Gilbert-Elliot model and heavy-tailed distribution, resulting in an alternative hybrid model. A distribution is heavy tailed…”

Section: B Two-state Markov Chainmentioning

confidence: 99%

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MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Silva

Pedroso

2019

IEEE Access

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Technical reports indicate that wireless and mobile devices will account for 71% of all IP traffic by 2022, an increase of 19% over four years. This increase is related to advances in wireless data communication technologies. Wireless networks have become one of the most important ways to connect devices to the Internet, therein improving productivity and encouraging information sharing. IEEE 802.11, known as Wi-Fi, has become the main standard for wireless local area networks. The most important metrics for measuring the quality of Wi-Fi are delay, jitter, and packet loss. Packet loss occurs when one or more packets fail to reach their destination and can occur for a variety of reasons. Packet loss influences the user's perceived quality of applications over Wi-Fi networks, mainly multimedia and real-time applications. The availability of accurate models for packet loss in Wi-Fi networks enables the development of more efficient methods for performance analysis and network design, as well as better computational simulations. Modeling packet loss in such networks presents a major challenge because packets may be lost for many different reasons, including signal attenuation, noise, multipathing, signal refraction, thermal noise, competition for media access and buffer issues. In this paper, we provide an overview of the causes of packet loss and a comprehensive survey of the available models for packet loss in Wi-Fi networks. The potential benefits of the survey are: (i) the systematic presentation of available packet loss models for Wi-Fi networks, their parameters, and respective packet loss rate evaluation, (ii) comparison of models considering validation scenarios and input parameters, and (iii) description of open issues and future research directions. We hope that our analysis will help researchers understand the most important characteristics of the packet loss process in Wi-Fi networks and the strengths and weaknesses of the main packet loss models.INDEX TERMS Gilbert-Elliot model, packet loss model, packet loss rate, Wi-Fi communication.

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Section: B Two-state Markov Chainmentioning

confidence: 99%

“…• Many models were designed, tested, and compared with traces collected from networks under controlled scenarios, which means that, in most cases, the resulting model is specific to the studied case [141], [144], [152], [154]- [156], [168].…”

Section: Selection Of a Suitable Modelmentioning

confidence: 99%

MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Silva

Pedroso

2019

IEEE Access

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“…Os demais estudos que buscavam modelar os erros em redes sem fio adotavam redes que continham apenas os dispositivos em teste [14], ou até mesmo uma rede ad-hoc [11]. Porém o objetivo deste estudo foi o de analisar a rede com o perfil de tráfego real, no modelo de infraestrutura, com diversos clientes conectados simultaneamente, de modo que as perdas de pacotes fossem reflexo do tráfego efetivo de uma rede WiFi.…”

Section: Descrição Dos Experimentos E Metodologiaunclassified

Evidências da Falha do Modelo de Gilbert-Elliott em Redes sem Fio

Rohling¹,

Silva²,

Fernández³

et al. 2016

Anais De XXXIV Simpósio Brasileiro De Telecomunicações

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Resumo-Este artigo descreve evidências de falha no modelo de Gilbert-Elliott na modelagem de perda de pacotes em redes WiFi. Para este estudo foi realizada a amostragem de tráfego na rede WiFi do Departamento de Engenharia Elétrica da Universidade Federal do Paraná. Os resultados obtidos indicam a existência de uma dependência temporal entre os surtos de perda, e também um comportamento de cauda pesada no tempo de duração do estado bom, a partir da avaliação realizada com a utilização de ferramentas estatísticas. Ao finalé apresentado um modelo alternativo para representar o comportamento observado. Palavras-Chave-Gilbert-Elliot, Perda de Pacotes, Redes sem fio Abstract-This article presents evidences of failure of the Gilbert-Elliott packet loss model for WiFi networks. To perform the analysis, real traffic samples were collected in Wifi network of the Department of Electrical Engineering, Federal University of Parana. The results indicate the existence of a time dependence of the loss bursts and heavy tail behavior in the duration of the good state. The samples were evaluated by using statistical tools. It is also presented an alternative model to represent the observed behavior.

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“…Home networks typically have error rates which are some orders of magnitude above these figures, especially in the case of wifi (802.11) [97]. If the media stream is to be delivered through the home network, the residential gateway must provide some kind of error correction mechanism (FEC or ARQ) in order to keep the required level of service.…”

Section: Unequal Error Protectionmentioning

confidence: 99%

“…Each of the curves refer to a different value of W and represents, for several values of MSE gain, which proportion of the sequences obtained at least that gain value. The range of values of W is selected to cover typical loss burst lengths in a wireless home network [97].…”

Section: Single-packet Lossmentioning

confidence: 99%

Analysis, monitoring, and management of quality of experience in video delivery services over IP

García¹

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TESIS DOCTORAL ANALYSIS, MONITORING, AND MANAGEMENT OF QUALITY OF EXPERIENCE IN VIDEO DELIVERY SERVICES OVER IP by Pablo Pérez García This thesis proposes a comprehensive approach to the monitoring and management of Quality of Experience (QoE) in multimedia delivery services over IP. It addresses the problem of preventing, detecting, measuring, and reacting to QoE degradations, under the constraints of a service provider: the solution must scale for a wide IP network delivering individual media streams to thousands of users.The solution proposed for the monitoring is called QuEM (Qualitative Experience Monitoring). It is based on the detection of degradations in the network Quality of Service (packet losses, bandwidth drops. . . ) and the mapping of each degradation event to a qualitative description of its effect in the perceived Quality of Experience (audio mutes, video artifacts. . . ). This mapping is based on the analysis of the transport and Network Abstraction Layer information of the coded stream, and allows a good characterization of the most relevant defects that exist in this kind of services: screen freezing, macroblocking, audio mutes, video quality drops, delay issues, and service outages. The results have been validated by subjective quality assessment tests. The methodology used for those test has also been designed to mimic as much as possible the conditions of a real user of those services: the impairments to evaluate are introduced randomly in the middle of a continuous video stream.Based on the monitoring solution, several applications have been proposed as well: an unequal error protection system which provides higher protection to the parts of the stream which are more critical for the QoE, a solution which applies the same principles to minimize the impact of incomplete segment downloads in HTTP Adaptive Streaming, and a selective scrambling algorithm which ciphers only the most sensitive parts of the media stream. A fast channel change application is also presented, as well as a discussion about how to apply the previous results and concepts in a 3D video scenario.

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802.11g packet-loss behavior at high sustained bit rates in the home

Cited by 10 publications

References 3 publications

MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Evidências da Falha do Modelo de Gilbert-Elliott em Redes sem Fio

Analysis, monitoring, and management of quality of experience in video delivery services over IP

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