channel state information (CSI)
Channel frequency response (CFR) dataset used for "In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks" paper (IEEE Transactions on Microwave Theory and Techniques, in print, doi: 10.1109/TMTT.2023.3256532, preprint link: https://arxiv.org/abs/2303.04470).
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With the rapid deployment of indoor Wi-Fi networks, Channel State Information (CSI) has been used for device-free occupant activity recognition. However, various environmental factors interfere with the stable propagation of Wi-Fi signals indoors, which causes temporal variation of CSI data. In this study, we investigated temporal CSI variation in a real-world housing environment and its impact on learning-based occupant activity recognition.
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Abstract—Network slicing (NwS) is one of the main technologies
in the h-generation of mobile communication and
beyond (5G+). One of the important challenges in the NwS
is information uncertainty which mainly involves demand
and channel state information (CSI). Demand uncertainty is
divided into three types: number of users requests, amount
of bandwidth, and requested virtual network functions workloads.
Moreover, the CSI uncertainty is modeled by three
methods: worst-case, probabilistic, and hybrid. In this paper,
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Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.
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