The Regressive QoE Model for VoLTE

Yang, Ching Fen

doi:10.1109/waina.2017.95

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…23 According to the VoLTE standard, packets of VoLTE share QoS Class Identifier (QCI) of 1 or 2, which is higher than 8 or 9 of traditional VoIP applications such as Skype and WeChat. 24 That means, both jitter and packet loss rate in VoLTE are lower than VoIP applications; excessive modifications of IPDs or packet orders could be identified by the warden.…”

Section: Preliminariesmentioning

confidence: 99%

An end-to-end covert channel via packet dropout for mobile networks

Tan

Chen

et al. 2018

International Journal of Distributed Sensor Networks

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Voice over Long-Term Evolution enables reliable transmission among enormous Internet of Things devices, by providing end-to-end quality of service for Internet protocol-based services such as audio, video, and multimedia messaging. The research of covert timing channels aims at transmitting covert message stealthily to the receiver using variations of timing behavior. Existing approaches mainly modulate the covert message into inter-packet delays of overt traffic, which are not suitable for Voice over Long-Term Evolution, since most of the inter-packet delays of Voice over Long-Term Evolution traffic are of regular distribution, and any modification on inter-packet delays is easy to be detected. To address the issue, in this work, we propose a novel covert timing channel for the video stream in Voice over Long-Term Evolution, which modulates the covert message by deliberately dropping out video packets. Based on the twodimensional mapping matrix, the blocks of covert message are mapped into dropout-packet sequence numbers. To recover the covert message, the receiver retrieves the sequence numbers of lost packets and identifies them to be translated into blocks of the covert message. To evaluate our scheme, the simulations with different packet loss rates are conducted to validate the undetectability, throughput, and robustness, finally, the results show that this scheme is effective and reliable.

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Section: Preliminariesmentioning

confidence: 99%

An end-to-end covert channel via packet dropout for mobile networks

Tan

Chen

et al. 2018

International Journal of Distributed Sensor Networks

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“…In [2] a generic web services mapping is performed to estimate the KPI-KQI contribution to the QoE via machine learning techniques using principles of web engineering and web quality requirements. Referred to voice services, in [3] and [4], also a mathematical mechanism able to estimate the KPI/KQI contribution to QoE metrics is proposed. In [5] an exponential model is used to establish the KPI/KQI relationship for LTE video streaming and voice service.…”

Section: Introductionmentioning

confidence: 99%

“…The work in [9] studies YouTube and Netflix video traffic over mobile networks and shows that the network bandwidth and the CPU computing power impact on video QoE. However, the vast majority of these studies are focused on the contribution of the network and service performance to the customer experience [2]- [3]- [4]- [5]- [8]- [9], and not in the relationship between network performance and configurations parameters with the service performance. In addition, they are not adapted to the specific case of cellular networks and in concrete [2]-[3]-[6]- [7], to the RAN (Radio Access Network), characterized, among others, by changing conditions.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Key Quality Indicators for End-to-End Network Management: Preparing for 5G

Herrera-García

Fortes

Baena

et al. 2019

IEEE Veh. Technol. Mag.

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Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key features. However, estimating the end-user experience is not an easy task for network operators. The amount of end-user performance information operators can measure from the network is limited, complicating this approach. Here we explore the calculation of service metrics [known as key quality indicators (KQIs)] from classic low-layer measurements and parameters. We propose a complete machine-learning (ML) modeling framework. This system's low-layer metrics can be applied to measure service-layer performance. To assess the approach, we implemented and evaluated the proposed system on a real cellular network testbed.

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