Online Reinforcement Learning for Beam Tracking and Rate Adaptation in Millimeter-wave Systems

Author:

Krunz Marwan1ORCID,Aykin Irmak2ORCID,Sarkar Sopan1,Akgun Berk2ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA

2. Qualcomm Inc.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Reference53 articles.

1. Millimeter wave beamforming training: A reinforcement learning approach;Mohamed;Int. J. Electron. Telecommun.,2021

2. Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits

3. What will 5G be?;Andrews;IEEE J. Sel. Areas Commun.,2014

4. IEEE Standard-part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 3: Enhancements for very high throughput in the 60 GHz band (adoption of IEEE standard 802.11ad-2012),2012

5. Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges

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1. Comparison of Centralized and Federated Machine Learning Techniques for Beamtracking in 5G/6G Systems;Lecture Notes in Computer Science;2024

2. Beam Selection Approach in Millimeter Wave Communication using Multi-Objective Prairie Dog Optimization;2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS);2023-11-24

3. Beam Management in Operational 5G mmWave Networks;Proceedings of the 7th ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems;2023-10-06

4. A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems;Sensors;2023-04-28

5. Universal Dynamic Pilot Allocation for Beam Alignment Based On Multi-Armed Bandits;IEEE Wireless Communications Letters;2023

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