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• W2890784233 abstract "With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. Leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate the angle of arrival (AoA). We then create estimated AoA images as input to a DCNN, to train the weights in the offline phase. The location of mobile device is predicted based using the trained DCNN and new CSI AoA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments." @default.
• W2890784233 created "2018-09-27" @default.
• W2890784233 creator A5032469771 @default.
• W2890784233 creator A5043788836 @default.
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• W2890784233 date "2020-01-01" @default.
• W2890784233 modified "2023-10-18" @default.
• W2890784233 title "Deep Convolutional Neural Networks for Indoor Localization with CSI Images" @default.
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• W2890784233 doi "https://doi.org/10.1109/tnse.2018.2871165" @default.
• W2890784233 hasPublicationYear "2020" @default.
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