A Fast Effective Greedy Approach for MU-MIMO Beam Selection in mm-Wave and THz Communications
Author:
Affiliation:
1. The University of Texas at Dallas,Department of Electrical and Computer Engineering,Texas,USA
2. University of Delaware,Department of Electrical and Computer Engineering,Delaware,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10480156/10480157/10480178.pdf?arnumber=10480178
Reference20 articles.
1. An Overview of Massive MIMO: Benefits and Challenges
2. Deep Learning Based Power Allocation in 6G URLLC for Jointly Optimizing Latency and Reliability
3. An Overview of Massive MIMO for 5G and 6G
4. Multi-Cell MIMO Cooperative Networks: A New Look at Interference
5. Beam Management for Millimeter-Wave Beamspace MU-MIMO Systems
Cited by 1 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. Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09
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