Data Driven QoE-QoS Association Modeling of Conversational Video

Gu, Hongcheng; Yang, Dong; Cao, Tingting

doi:10.1109/globalsip45357.2019.8969521

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…This results in fast convergence of the algorithm. Because, in each update iteration, the computation of network parameters becomes smaller, as shown in Equation (13) and (14). The choice of network structure and layer dimensions are the results of experience.…”

Section: Performance Versus Delay and Number Of Vehiclesmentioning

confidence: 99%

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A Resource Allocation Scheme for Intelligent Tasks in Vehicular Networks

Jiujiu

Guo

Feng

et al. 2022

Wireless Communications and Mobile Computing

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Lots of resource-consuming intelligent tasks need to be handled in vehicular networks, and traditional resource allocation schemes are hard to meet the intelligent demands. Therefore, this paper proposes a task-oriented resource allocation scheme for intelligent tasks in vehicular networks. First, we propose a task-oriented communication system and formulate a resource allocation problem, which is aimed at maximizing the task performance. Second, based on the system model, an intelligent task-oriented resource allocation optimization criterion is proposed, which is formulated as a mathematical model, and its parameters are solved by the proposed gradient descent-based algorithm. Third, to solve resource allocation problem, a multiagent deep Q -network- (MADQN-) based algorithm is proposed, whose convergence and complexity are further analyzed. Last, experiments on real datasets verify the performance advantages of our proposed algorithms.

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Section: Performance Versus Delay and Number Of Vehiclesmentioning

confidence: 99%

“…The main method is to map QoE requirements to communication resource requirements such as spectrum and power [11,12]. And the other is to establish the mapping or association between QoE and QoS and then use the multiobjective resource allocation methods to improve the quality of user experience [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Resource Allocation Scheme for Intelligent Tasks in Vehicular Networks

Jiujiu

Guo

Feng

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…They indicated that quantitative evaluation of the QoE in multimedia communication is a challenging problem. In [ 14 ], through the statistical analysis of network video stream data, authors used the ITU-T p.1201 1 model to find a correlation between the QoE and video service QoS parameters. From the perspective of user experience, the QoS recommendation index of each MOS value interval could be given, and the connection between the QoS and QoE was established.…”

Section: Related Workmentioning

confidence: 99%

User Experience Estimation in Multi-Service Scenario of Cellular Network

Zhang

Chuai

Maimaiti

et al. 2021

Sensors

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The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management.

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