Heterogeneous network selection algorithm for novel 5G services based on evolutionary game. Issue 2 (1st January 2020)

• Record Type: Journal Article
• Authors: Ma, Mingfang; Zhu, Anqi; Guo, Songtao; Wang, Xiaoqian; Liu, Bei; Su, Xin
• Is Part Of: IET communications. Volume 14:Issue 2(2020)
• Page Start: 320
• Page End: 330
• Publication Date: 2020-01-01
• Subjects: quality of service -- radio networks -- 5G mobile communication -- decision making -- resource allocation -- evolutionary computation -- game theory -- analytic hierarchy process; fifth‐generation mobile networks -- 5G services -- quality of service requirements -- evolutionary game -- analytic hierarchy process -- joint function -- network decision attributes -- evolutionary equilibria -- simple additive weighting algorithm -- multiplicative exponent weighting algorithm -- Q‐learning based algorithm -- contrast algorithms -- network load balancing -- heterogeneous network selection algorithm -- heterogeneous wireless networks -- network resource -- NS‐EG; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2018.6290
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : The network selection in heterogeneous wireless networks is considered as a crucial technology to take advantage of network resource in the coming fifth‐generation (5G) mobile networks. Considering the emergence of 5G novel services and the guarantee of quality of service requirements, in the study, the authors propose a network selection algorithm based on evolutionary game named NS‐EG, by using analytic hierarchy process to jointly analyse user preferences and service requirements. The utility is structured as a joint function of network decision attributes and available capacity. In addition, the dynamic behaviour of users accessing different networks with replicator dynamics are explicitly provided. In order to verify the superiority of the algorithm proposed, the authors evaluate the evolutionary equilibria as well as the iteration of the algorithm by comparing with the simple additive weighting algorithm, multiplicative exponent weighting algorithm and Q‐learning based algorithm. Simulation results confirm that the proposed algorithm outperforms the contrast algorithms and achieve network load balancing.