An Unsupervised Learning Paradigm for User Scheduling in Large Scale Multi-Antenna Systems
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, USA
Funder
Division of Electrical, Communications and Cyber Systems
Division of Computing and Communication Foundations
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/4656680/09930883.pdf?arnumber=9930883
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
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2. Unsupervised Learning for Resource Allocation and User Scheduling in Wideband MU-MIMO Systems;IEEE Open Journal of the Communications Society;2024
3. From 5G to 6G Networks: A Survey on AI-Based Jamming and Interference Detection and Mitigation;IEEE Open Journal of the Communications Society;2024
4. Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04
5. Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks;Sensors;2023-07-29
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