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A machine learning-based ETA estimator for Wi-Fi transmissions
Davide Del Testa
, Matteo Danieletto
, Michele Zorzi
Icahn School of Medicine at Mount Sinai
Research output
:
Contribution to journal
›
Article
›
peer-review
6
Scopus citations
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Dive into the research topics of 'A machine learning-based ETA estimator for Wi-Fi transmissions'. Together they form a unique fingerprint.
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Keyphrases
Machine Learning Based
100%
Controller
100%
Data Transfer
100%
WiFi Transmissions
100%
Prediction Algorithms
50%
Machine Learning Approach
50%
Network Paradigm
50%
User Needs
50%
Transmission Time
50%
Communication Protocol
50%
Recent Advancements
50%
Transfer Based
50%
Machine Learning Prediction
50%
Directed Networks
50%
Exchange Data
50%
Access Point
50%
Multiple Elements
50%
Network Infrastructure
50%
Radio Coverage
50%
Mobile Users
50%
Residual Time
50%
Master Node
50%
Controller Node
50%
Device-to-device (D2D) Communication
50%
Estimated Time of Arrival
50%
Software-defined Networking
50%
Wi-Fi Direct
50%
Computer Science
Wi-Fi
100%
Machine Learning
100%
Learning System
100%
Machine Learning Approach
50%
Communication Protocol
50%
Access Point
50%
Transmission Time
50%
Network Infrastructure
50%
Node Controller
50%
Software Defined Networking
50%
Device-To-Device
50%