A Low-Complexity Machine Learning Design for mmWave Beam Prediction
Author:
Affiliation:
1. NI Dresden R&D, National Instruments Corporation, Dresden, Germany
2. National Instruments Corporation, Dresden, Germany
3. Vodafone Chair for Mobile Communications Systems, Technische Universität, Dresden, Germany
Funder
European Union’s SEMANTIC ITN Project under the Marie Skodowska-Curie
Federal Ministry of Education and Research (BMBF) as part of the Project 6G-ANNA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/5962382/10552646/10478627.pdf?arnumber=10478627
Reference20 articles.
1. Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design
2. NR: The New 5G Radio Access Technology
3. Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges
4. Machine Learning Based Time Domain Millimeter-Wave Beam Prediction for 5G-Advanced and Beyond: Design, Analysis, and Over-The-Air Experiments
5. Hierarchical Codebook Design for Beamforming Training in Millimeter-Wave Communication
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