An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

The use of millimeter wave technologies offer a promising solution for dense small cell networks, despite having to contend with challenging propagation characteristics. In particular, user-induced effects can lead to significant channel variations depending on the user equipment (UE) usage mode whi...

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Bibliographic Details
Main Authors: Lei Zhang, Yang Hua, Simon L. Cotton, Seong Ki Yoo, Claudio R. C. M. Da Silva, William G. Scanlon
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Human activity recognition
millimeter wave
received signal strength
supervised learning
unsupervised learning
user equipment
Online Access:https://ieeexplore.ieee.org/document/8957118/

Internet

https://ieeexplore.ieee.org/document/8957118/

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