Grid-less mmWave Beam Alignment through Deep Learning
Author:
Affiliation:
1. Wireless Networking and Communications Group (WNCG) The University of Texas at Austin,Austin,TX,USA,78712
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001720.pdf?arnumber=10001720
Reference15 articles.
1. DeepMIMO: A generic deep learning dataset for mil-limeter wave and massive MIMO applications;alkhateeb;Proc Inf Theory and Appl Workshop (ITA),0
2. An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
3. Active Learning and CSI Acquisition for mmWave Initial Alignment
4. Initial Access in 5G mmWave Cellular Networks
5. Six Key Challenges for Beam Management in 5.5G and 6G Systems
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1. SAMBA: Scenario-Adaptive Meta-Learning for mmWave Beam Alignment;2023 IEEE Globecom Workshops (GC Wkshps);2023-12-04
2. Environment-Specific Beam Training for Extremely Large-Scale MIMO Systems via Contrastive Learning;IEEE Communications Letters;2023-10
3. New Machine Learning Approach for Low Overhead Multi-Beam Prediction;2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC);2023-09-05
4. Improving Beam Alignment Accuracy in mmWave Communication Systems With Auxiliary Tasks;IEEE Signal Processing Letters;2023
5. Grid-Free MIMO Beam Alignment through Site-Specific Deep Learning;IEEE Transactions on Wireless Communications;2023
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