Adversarial Multi-armed Bandit for mmWave Beam Alignment with One-Bit Feedback-Reference-Cited by-同舟云学术

Adversarial Multi-armed Bandit for mmWave Beam Alignment with One-Bit Feedback

Published:2019-03-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools
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Author:

Chafaa Irched¹,Belmega E. Veronica²,Debbah Mérouane³

Affiliation:

1. ETIS, Université Paris, Seine, Université Cergy-Pontoise, ENSEA, CNRS, Cergy, France L2S, CentraleSupélec, Université Paris-Saclay Gif-sur-Yvette, France

2. ETIS, Université Paris, Seine, Université Cergy-Pontoise, ENSEA, CNRS, Cergy, France

3. CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France Mathematical and Algorithmic Sciences Lab, Huawei France R&D, Paris, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3306309.3306315

Reference23 articles.

1. Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

2. FML: Fast Machine Learning for 5G mmWave Vehicular Communications

3. E Veronica Belmega Panayotis Mertikopoulos Romain Negrel and Luca Sanguinetti. 2018. Online convex optimization and no-regret learning: Algorithms guarantees and applications. arXiv preprint arXiv:1804.04529 (2018). E Veronica Belmega Panayotis Mertikopoulos Romain Negrel and Luca Sanguinetti. 2018. Online convex optimization and no-regret learning: Algorithms guarantees and applications. arXiv preprint arXiv:1804.04529 (2018).

4. Luca De Nardis and Maria-Gabriella Di Benedetto. 2017. MoMo: a group mobility model for future generation mobile wireless networks. arXiv preprint arXiv:1704.03065 (2017). Luca De Nardis and Maria-Gabriella Di Benedetto. 2017. MoMo: a group mobility model for future generation mobile wireless networks. arXiv preprint arXiv:1704.03065 (2017).

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