Dynamics of Network Selection in Heterogeneous Wireless Networks: An Evolutionary Game Approach

• Published in: IEEE transactions on vehicular technology 2009-05, Vol.58 (4), p.2008-2017
• Main Authors: Niyato, D., Hossain, E.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Bandwidth; Broadcasting; Centralized control; Degradation; Disturbances. Regulation. Protection; Dynamic tests; Dynamics; Electrical engineering. Electrical power engineering; Electrical machines; Electrical power engineering; Evolutionary; Evolutionary equilibrium; evolutionary game theory; Exact sciences and technology; Game theory; Games; heterogeneous wireless access networks; Learning; Load management; Mathematical models; Nash equilibrium; network selection; Networks; Next generation networking; Operation. Load control. Reliability; Power networks and lines; Process control; Regulation and control; replicator dynamics; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Teletraffic; Wireless communication; Wireless communications; Wireless networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2008.2004588

Next-generation wireless networks will integrate multiple wireless access technologies to provide seamless mobility to mobile users with high-speed wireless connectivity. This will give rise to a heterogeneous wireless access environment where network selection becomes crucial for load balancing to avoid network congestion and performance degradation. We study the dynamics of network selection in a heterogeneous wireless network using the theory of evolutionary games. The competition among groups of users in different service areas to share the limited amount of bandwidth in the available wireless access networks is formulated as a dynamic evolutionary game, and the evolutionary equilibrium is considered to be the solution to this game. We present two algorithms, namely, population evolution and reinforcement-learning algorithms for network selection. Although the network-selection algorithm based on population evolution can reach the evolutionary equilibrium faster, it requires a centralized controller to gather, process, and broadcast information about the users in the corresponding service area. In contrast, with reinforcement learning, a user can gradually learn (by interacting with the service provider) and adapt the decision on network selection to reach evolutionary equilibrium without any interaction with other users. Performance of the dynamic evolutionary game-based network-selection algorithms is empirically investigated. The accuracy of the numerical results obtained from the game model is evaluated by using simulations.