Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges
Author:
Affiliation:
1. National Instruments Corporation, Dresden, Germany
2. Vodafone Chair for Mobile Communications Systems, Technische Universität Dresden, Dresden, Germany
Funder
European Union’s Horizon 2020 Research and Innovation Program
Project “Industrial Radio Lab Germany (IRLG)” through the Federal Ministry of Education and Research, Germany
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10036372.pdf?arnumber=10036372
Reference149 articles.
1. Deep Learning-Based mmWave Beam Selection for 5G NR/6G With Sub-6 GHz Channel Information: Algorithms and Prototype Validation
2. Position aided beam prediction in the real world: How useful GPS locations actually are?;morais;arXiv 2205 09054,2022
3. Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels
4. Deep Learning Assisted Beam Prediction Using Out-of-Band Information
5. Online Learning for Position-Aided Millimeter Wave Beam Training
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