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Neural Computing and Applications

Erschienen in:

10.11.2020 | S.I. : SPIoT 2020

QoS intelligent prediction for mobile video networks: a GR approach

verfasst von: Lingwei Xu, Han Wang, Hui Li, Wenzhong Lin, T. Aaron Gulliver

Erschienen in: Neural Computing and Applications | Ausgabe 9/2021

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Abstract

With the growth of mobile devices, consumer networks make the life more convenient and faster. Consumer networks consider mobile video as an important communication mode. Mobile video transmission faces complex environments, and the quality of service (QoS) of mobile video networks is very important for mobile entertainment applications. To evaluate the QoS of mobile video networks, outage probability (OP) is an important criterion. However, the mobile video networks gradually become complex, dynamic, and variable, which make it increasingly more difficult to predict the OP performance. In this paper, we investigate the OP performance analysis and prediction. The OP expressions are derived in exact closed-form. Then, based on the characteristics of mobile data, we have established a prediction model based on generalized regression (GR) neural network. A GR-based OP performance intelligent prediction algorithm is proposed. Compared with other methods, our proposed approach can obtain a better prediction effect. The prediction accuracy of the proposed approach can be increased by 64% and 58%, respectively. The running time is also the shortest.

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Titel: QoS intelligent prediction for mobile video networks: a GR approach
verfasst von: Lingwei Xu
Han Wang
Hui Li
Wenzhong Lin
T. Aaron Gulliver
Publikationsdatum: 10.11.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05441-1

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