A cognitive wireless networks access selection algorithm based on MADM

Abstract High-efficiency Access network selection algorithms are crucial to ensure Quality of Service (QoS) and Quality of Experience (QoE) in heterogeneous networks environment. Hereby, an intelligent access network selection algorithm based on multiple attribute decision making (MADM) is proposed in this paper. First, considering the special characteristics of cognitive wireless networks, a cross-layer network framework based on cognitive cycle was proposed. Secondly, according to network condition and QoE of users, we used the cross-layer multiple attributes to make decision for network selection. Lastly, simulation experiments were carried out using MATLAB. The simulation results show that the network selection algorithm was effective. The number of handoffs and sorting anomaly ratios were reduced. The quality of network service, user experience and network resource utilization were improved. Mobile users can access the most suitable network according to different traffic type requirements.

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