Grid-less mmWave Beam Alignment through Deep Learning

Author:

Heng Yuqiang1,Andrews Jeffrey G.1

Affiliation:

1. Wireless Networking and Communications Group (WNCG) The University of Texas at Austin,Austin,TX,USA,78712

Publisher

IEEE

Reference15 articles.

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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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