FML: Fast Machine Learning for 5G mmWave Vehicular Communications

Author:

Asadi Arash,Muller Sabrina,Sim Gek Hong,Klein Anja,Hollick Matthias

Publisher

IEEE

Cited by 31 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. Context-Aware Beam Management via Online Probing in Combinatorial Multi-Armed Bandits;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. Rapid Beam Training at Terahertz Frequency with Contextual Multi-Armed Bandit Learning;2024 IEEE International Conference on Industrial Technology (ICIT);2024-03-25

3. Deep Reinforcement Learning-Based mmWave Beam Alignment for V2I Communications;IEEE Transactions on Machine Learning in Communications and Networking;2024

4. Online Learning for Adaptive Probing and Scheduling in Dense WLANs;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications;2023-05-17

5. Deep Reinforcement Learning Based Joint Beam Allocation and Relay Selection in mmWave Vehicular Networks;IEEE Transactions on Communications;2023-04

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