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Research on Network Resource Optimal Allocation Algorithm Based on Game Theory

by Xiaojuan Yuan1,2,*

1 Harbin Institute of Technology, Harbin, 150001, China
2 Guilin Tourism University, Guilin, 541004, China

* Corresponding Author: Xiaojuan Yuan. Email: email

Intelligent Automation & Soft Computing 2021, 27(1), 249-257. https://doi.org/10.32604/iasc.2021.013637

Abstract

This paper briefly introduced the structure of heterogeneous cellular network and two algorithms which are used for optimizing the network resource allocation scheme: dynamic game algorithm based on spectrum allocation and the game allocation algorithm based on power allocation and alliance. After that, the two algorithms were simulated in MATLAB software and compared with another power iterative allocation algorithm based on non-cooperative game. The results showed that the system energy efficiency of the three algorithms decreased with the increase of the number of small base stations in the network; with the increase of the number of users in the network, the system energy efficiency of the three algorithms increased; under the same number of small base stations or users, the system energy efficiency of the allocation scheme based on the power allocation and alliance was the highest, followed by the power iterative allocation algorithm and the spectrum allocation based game algorithm; the allocation scheme based on power allocation and alliance could provide users with more satisfactory and fair services.

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APA Style
Yuan, X. (2021). Research on network resource optimal allocation algorithm based on game theory. Intelligent Automation & Soft Computing, 27(1), 249-257. https://doi.org/10.32604/iasc.2021.013637
Vancouver Style
Yuan X. Research on network resource optimal allocation algorithm based on game theory. Intell Automat Soft Comput . 2021;27(1):249-257 https://doi.org/10.32604/iasc.2021.013637
IEEE Style
X. Yuan, “Research on Network Resource Optimal Allocation Algorithm Based on Game Theory,” Intell. Automat. Soft Comput. , vol. 27, no. 1, pp. 249-257, 2021. https://doi.org/10.32604/iasc.2021.013637



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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