Learning-Based Multi Attribute Network Selection in Heterogeneous Wireless Access

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The rise of heterogeneous networks including macro, micro, pico, femto, and WLAN presents new challenges in optimizing user’s access to the networks. To fully utilize the capacity of such rich field of heterogeneous wireless connectivity, mobile devices should be able to select Radio Access Network (RAN) for their connection depending on their need. The proposed algorithm here is based on an autonomous agent at the mobile node and assisted by integration of distributed cloud services, e.g. the edge cloud. The selection of RAN is based on a Multi Attribute Decision Making process combined with the Reinforcement Learning that reinforces historical data collected from the edge cloud. Mobile agent is responsible for collecting data, executing the selection algorithm, possibly offloading the parts of the execution to the edge cloud, and providing feedback to the edge cloud after termination of the connection. The ultimate aim is to the design of RAN selection algorithm owned by autonomous and intelligent user agents that can significantly improve the user’s experience in terms of network coverage, data rate, latency, and battery lifetime. Through extensive simulation scenarios, we demonstrate the stability and precision of our proposed algorithm.