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- W2982678082 abstract "In the network world, successful delivery of binary 0 and 1 encapsulated in a network packet does not necessarily indicate a dazzling smile from end user. Good network performance may give false comfort to mobile operators since it is easy to measured and control but does not lead to the best user perception based Quality of Experience (QoE). We propose a wireless Artificial Intelligence (AI) based system to bridge the gap between network performance and user perception with an ultimate goal to maximize user QoE in 4G LTE networks through Deep Learning based Convolutional Neural Networks (CNN) and Sigmoid Activation Functions, both of which are customized for 4G LTE networks. We also implement a Geo-bin solution to enable network performance quantified by Network Performance Healthiness (NPH) Score to better approximate users’ true QoE as close as possible. With NPH and Geo-Bin Solution, the ultimate goal of network optimization is shifted from network performance driven to user QoE oriented in a new P2P (from packets to people) paradigm." @default.
- W2982678082 created "2019-11-08" @default.
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- W2982678082 date "2019-04-01" @default.
- W2982678082 modified "2023-09-26" @default.
- W2982678082 title "P2P: From Packets to Pepole, Leverage Wireless Artifical Intelligence for Bridging Network Performance Healthiness to Quality of Experience (QoE) in 4G Networks" @default.
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- W2982678082 doi "https://doi.org/10.1109/wts.2019.8715537" @default.
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