Throughput and Delay Estimator for 2.4GHz WiFi APs: A Machine Learning-Based Approach

Kajita, Shugo; Yamaguchi, Hirozumi; Higashino, Teruo; Urayama, Hirofumi; Yamada, Masaya; Takai, Mineo

doi:10.1109/wmnc.2015.30

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Os resultados da simulação mostraram que o ML-TCP possui maior precisão para prever a causa da perda de pacotes em diferentes ambientes. Kajita et al [Kajita et al 2015] projetaram estimadores de atraso e vazão para canais 2.4GHz com interferência. Os autores concentram esforços em projetar duas funções de estimação que possam ser utilizadas por cada ponto de acesso para escolha de um canal baseado na observação do tráfego de outros pontos de acesso.…”

Section: Trabalhos Relacionadosunclassified

Avaliação de Desempenho de Algoritmos de Machine Learning para Otimização de Simulações de Redes de Computadores

Quintino¹,

Santiago²,

Braga³

et al. 2020

Anais Do XXV Workshop De Gerência E Operação De Redes E Serviços (WGRS 2020)

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Os simuladores de redes são ferramentas computacionais capazes de auxiliar na análise e aperfeiçoamento de protocolos de comunicação em diferentes cenários antes da implantação em um ambiente real. No entanto, o tempo de execução das simulações e os recursos computacionais alocados tendem a crescer à medida que os requisitos de complexidade e precisão aumentam. Além disso, muitos processos de simulação possuem como única saída traces sem interpretação lógica. Este trabalho avalia o desempenho de algoritmos de Machine Learning na otimização de simulações de redes de computadores. Os resultados mostram que os algoritmos são capazes de fazer suposições próximas das simulações consideradas, alcançando precisões acima de 90%.

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Section: Trabalhos Relacionadosunclassified

Avaliação de Desempenho de Algoritmos de Machine Learning para Otimização de Simulações de Redes de Computadores

Quintino¹,

Santiago²,

Braga³

et al. 2020

Anais Do XXV Workshop De Gerência E Operação De Redes E Serviços (WGRS 2020)

View full text Add to dashboard Cite

show abstract

“…For instance, a decision tree based classification algorithm is employed in [9] to choose the best available network between a 3G and a WLAN using features such as RSSI, location and type of application. In [10], Support Vector Machines (SVM) is used to estimate the throughput and delay in a WiFi network, given the traffic volume and signal strength of active interferes as input features. A recent work presents "PenSeive" [11], an Adaptive Bit Rate (ABR) video transmission framework based on reinforcement learning and neural networks.…”

Section: Arxiv:200401300v1 [Csni] 2 Apr 2020mentioning

confidence: 99%

Data-Driven Path Selection for Real-Time Video Streaming at the Network Edge

Baidya

Tehrani

Levorato

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we present a framework for the dynamic selection of the wireless channels used to deliver information-rich data streams to edge servers. The approach we propose is data-driven, where a predictor, whose output informs the decision making of the channel selector, is built from available data on the transformation imposed by the network on previously transmitted packets. The proposed technique is contextualized to real-time video streaming for immediate processing. The core of our framework is the notion of probes, that is, short bursts of packets transmitted over unused channels to acquire information while generating a controlled impact on other active links. Results indicate a high accuracy of the prediction output and a significant improvement in terms of received video quality when the prediction output is used to dynamically select the used channel for transmission.

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“…For this, Haghani et al [5] developed a theoretical model that takes as input the busy/idle periods in the channel, as this factor affects the channel delay. Kajita et al [8] estimate the channel delay by using a machine learning approach, where the input parameters come from traffic around the AP, implying that this approach requires information about the frame transmitted in the neighborhood. This information may not be always available, since APs might not be able to decode frames if the MCS is too high.…”

Section: Introductionmentioning

confidence: 99%

Identifying Channel Saturation in Wi-Fi Networks via Passive Monitoring of IEEE 802.11 Beacon Jitter

Molina

Blanc

Montavont

2017

Proceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access

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International audienceEvery day large number of users connect to IEEE 802.11 networks in order to access the Internet and all sort of services. But, due to their unplanned and unregulated nature, and to the lack of admission control and Quality of Service Guarantees, these wireless networks can be confronted to a traffic demand that can exceed the network capacity. In this case, if a device tries to send more traffic, or if a new device joins the network, the aggregate throughput does not necessarily increase. In this paper we show that it is possible for IEEE 802.11 Stations to detect a saturated channel by passively monitoring the beacon frames. Access Points send beacon frames periodically and encode them using the strongest modulation and coding scheme, so that even stations further away from the sending Access Points can decode them correctly. When sending beacons, Access Points sense the channel first and, if it is busy, delay sending the frame, resulting in unevenly spaced beacon frames, whenever other transmitters are active. We present several experiments, under varying traffic loads, and analyze the distribution of the beacon jitter, whose variance increases as the offered load increases. We show that it is possible to determine, with an acceptable error rate, whether a channel is saturated by comparing the distribution of the beacon jitter with a reference distribution corresponding to a saturated channel

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Throughput and Delay Estimator for 2.4GHz WiFi APs: A Machine Learning-Based Approach

Cited by 8 publications

References 7 publications

Avaliação de Desempenho de Algoritmos de Machine Learning para Otimização de Simulações de Redes de Computadores

Avaliação de Desempenho de Algoritmos de Machine Learning para Otimização de Simulações de Redes de Computadores

Data-Driven Path Selection for Real-Time Video Streaming at the Network Edge

Identifying Channel Saturation in Wi-Fi Networks via Passive Monitoring of IEEE 802.11 Beacon Jitter

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