Abstract
Metallic frame of elevators attenuate the radio signals severely causing Radio Link Failure (RLF) at Mobile Device (MD). RLF results in connection interruption causing degradation in user experience. To overcome this problem, we propose a reinforcement learning based technique called Intelligent Elevator Detection and Network Adaptation (IE-DNA) algorithm, that can be implemented on MD. The IE-DNA algorithm has the capability to handle frequent RLF, detection of elevator movement and intelligent handover among the Cellular (i.e., 3G/4G/5G) or Wi-Fi networks. Using IE-DNA algorithm, MD can upload and store radio link statistics, experienced by the user, to a cloud server. Further, uploaded data can be utilized by any mobile user to know the radio link condition a-priory for a specific elevator, when visiting later in time and handle it by selecting a network that wouldn't cause interruption during elevator movement. Based on real data-set for Cellular/Wi-Fi collected by Samsung Galaxy S8 device, we conduct extensive experiments using real elevator environment to compute performance of the schemes with respect to the state of art.
Published Version
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