Abstract
The next generation of heterogeneous wireless networks (HWNs) will integrate various radio access technologies, which will make how to connect mobile users based on the performance parameters of each wireless network and the quality of service requirements (as to enable mobile users to be connected to the most suitable wireless network) a hot topic for HWNs. This paper designs an algorithm for joint access selection and bandwidth allocation in HWNs. Taking into account the environment in which worldwide interoperability for microwave access, long term evolution, and wireless local area network may co-exist, the algorithm uses received signal strength, network load, and user rate requirements as input decision parameters and adjusts the parameters of the membership function in the five-layer fuzzy neural network structure through supervised learning to obtain the score and bandwidth allocation value for each candidate network. The simulation results show that the proposed algorithm can enable users to choose the most suitable network to access and may modify the fuzzy rules and adjust the resource utilization of different networks based on user preferences.
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