A flexible and generalized framework for access network selection in heterogeneous wireless networks http://researchonline.ljmu.ac.uk/id/eprint/7270/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively.
Abstract:The rapid development and integration of heterogeneous wireless networks provide ubiquitous communications for mobile users. The intelligent and multimodal mobile terminals should select the best access network at any time anywhere. However, the "best" is a complex and fuzzy concept, which has different meanings to different users and even to the same user under different conditions. There are various factors to consider when deciding which one is the best for a mobile terminal. In this paper, we design a generalized and flexible framework for the access network selection in heterogeneous wireless networks. The framework is generalized because it considers various factors in a comprehensive way to get the solutions. These factors can be classified as network-related or user-related, economic or non-economic, objective or subjective, accurate or fuzzy. Meanwhile, the framework is also flexible because these factors can be customized and adapted to specific solutions. Under the framework, given N mobile terminals and M access networks, we have developed a novel access network selection scheme based on a Quantum-inspired Immune Clonal Algorithm (QICA). Experimental results demonstrate that our proposed scheme provides better utilities for both the users and the access networks, and also better services for users as compared with four other schemes.
ABC requirements. Instead of simply optimizing one objective, we propose gaming strategy and devise the user utility and the network utility, as motivated by the economics theory. Thus, our models aim at achieving the win-win situation for both the users and the network providers under the Nash equilibrium. In this way, our models and problem are rooted in the real-world scenarios. Under our framework, we have developed a specific method to solve the access network selection problem. Due to its high computation complexity, the method applies an artificial intelligence algorithm, QICA, to find the best solutions. The method shows remarkable performance as QICA achieves natural balance between exploration and exploitation. The method will serve as an example for developing other novel methods under our framework and also give benchmark solutions for comparison purposes.The rest of this paper is organized as follows. The related works are reviewed and compared with our work in Section 2. The models and the framework are described in Section 3.The proposed access network selection scheme is presented in Section 4. Simulation experiments are described in Section 5. Finally, Section 6 concludes the paper.
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