Deep Q-Network Based Beam Tracking for Mobile Millimeter-Wave Communications
Author:
Affiliation:
1. Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea
2. Navigation Research and Development Division, Korea Aerospace Research Institute, Daejeon, South Korea
Funder
Institute for Information and Communications Technology Promotion
Institute for Information communications Technology Promotion
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications
Link
http://xplorestaging.ieee.org/ielx7/7693/10042160/09869437.pdf?arnumber=9869437
Reference41 articles.
1. Beam Tracking Under Highly Nonlinear Mobile Millimeter-Wave Channel
2. Coding the Beams: Improving Beamforming Training in mmWave Communication System
3. Robust Beam-Tracking for mmWave Mobile Communications
4. Array-based GNSS signal tracking with a reduced state signal model
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1. Deep Active Learning for mmWave Array-Based Multi-Source AoA Tracking;IEEE Transactions on Wireless Communications;2024-09
2. PPO LSTM Based Beam Tracking for mmWave Communication Systems;2024 26th International Conference on Digital Signal Processing and its Applications (DSPA);2024-03-27
3. Deep Active Learning for Multi-Source AoA Tracking in mmWave-Based ISAC Systems;2023 IEEE Globecom Workshops (GC Wkshps);2023-12-04
4. DQN-based Joint Adaptive Beamwidth Control and Beam Tracking for mmWave Communications;2023 IEEE Statistical Signal Processing Workshop (SSP);2023-07-02
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