GERT-Q-learning model for intelligent QoS dynamic optimization of Inmarsat STN based on grey clustering of delay and delay variation
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 10 February 2023
Issue publication date: 6 July 2023
Abstract
Purpose
This paper aims to implement quality of service(QoS) dynamic optimization for the integrated satellite-terrestrial network(STN) of the fifth-generation Inmarsat system(Inmarsat-5).
Design/methodology/approach
The structure and operational logic of Inmarsat-5 STN are introduced to build the graphic evaluation and review technique(GERT) model. Thus, the equivalent network QoS metrics can be derived from the analytical algorithm of GERT. The center–point mixed possibility functions of average delay and delay variation are constructed considering users' experiences. Then, the grey clustering evaluation of link QoS is obtained combined with the two-stage decision model to give suitable rewards for the agent of GERT-Q-learning, which realizes the intelligent optimization mechanism under real-time monitoring data.
Findings
A case study based on five time periods of monitoring data verifies the adaptability of the proposed method. On the one hand, grey clustering based on possibility function enables a more effective measurement of link QoS from the users' perspective. On the other hand, the method comparison intuitively shows that the proposed method performs better.
Originality/value
With the development trend of integrated communication, STN has become an important research object in satellite communications. This paper establishes a modular and extensible optimization framework whose loose coupling structure and flexibility facilitate management and development. The grey-clustering-based GERT-Q-Learning model has the potential to maximize design and application benefits of STN throughout its life cycle.
Keywords
Acknowledgements
This work was supported by projects of the National Natural Science Foundation of China (72271124, 52232014, 72071111, 71801127, 71671091) . It is also supported by a joint project of both the NSFC and the RS of the UK (71811530338). At the same time, the authors would like to acknowledge the partial support of the special postdoctoral fund of China (2019TQ0150) and support of a project of Intelligence Introduction Base of the Ministry of Science and Technology (G20190010178).
Citation
Hua, C., Fang, Z., Zhang, Y., Nan, S., Wu, S., Qiu, X., Zhao, L. and Xiao, S. (2023), "GERT-Q-learning model for intelligent QoS dynamic optimization of Inmarsat STN based on grey clustering of delay and delay variation", Grey Systems: Theory and Application, Vol. 13 No. 3, pp. 445-463. https://doi.org/10.1108/GS-08-2022-0092
Publisher
:Emerald Publishing Limited
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