mmWave
We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.
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Image : Image was made by me for other International Contest (held by some Medical Institute,USA in the year 2021), 'An intuitive of electromagnetic radiation flowing over epithelial tissue'.
This is an open-access page. All the content can be freely downloaded after sign-up. This webpage contains datasets and models, which are in support of my Research claim/discovery and also used in my Invited/Keynote/Featured/Speaker Presentations.
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This is the dataset of mmWave massive MIMO beamspace channels, which is used for the experiment implementation of the paper "Acquiring Measurement Matrices via Deep Basis Pursuit for Sparse Channel Estimation in mmWave Massive MIMO Systems". The source code of the experiment implementation is also open-access on the Github repository DeepBP-AE.
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This pdf file contains supplementary information to a published article, "Early Warning of mmWave Signal Blockage and AoA Transition Using sub-6 GHz Observations," by Ziad Ali, Alexandra Duel-Hallen and Hans Hallen, published in IEEE Communications Letters, 2019.
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Data and codes for journal paper "MmWave Vehicular Beam Training with Situational Awareness Using Machine Learning" submitted to IEEE Access.
The code assumes Python 3.
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This database includes data measured by Qualcomm's 60GHz mmWave Radar.
It includes:
Face signature data base: radar face scan data of 206 individuals for face recognition.
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