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PARAMOUNT: Toward Generalizable Deep Learning for mmWave Beam Selection Using Sub-6 GHz Channel Measurements.

, , , , and . IEEE Trans. Wirel. Commun., 23 (5): 5187-5202 (May 2024)

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Single-sided adaptive estimation of multi-path millimeter wave channels., , , and . SPAWC, page 125-129. IEEE, (2014)3D Scene-Based Beam Selection for mmWave Communications., , , and . IEEE Wirel. Commun. Lett., 9 (11): 1850-1854 (2020)Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems., and . IEEE Trans. Commun., 64 (5): 1801-1818 (2016)Computer Vision Aided Codebook Design for MIMO Communications Systems., , , , , and . IEEE Trans. Wirel. Commun., 22 (5): 3341-3354 (May 2023)Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles., and . CoRR, (2022)Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems., , and . ACSSC, page 445-449. IEEE, (2020)Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction., , , , , , and . GLOBECOM, page 2951-2956. IEEE, (2022)Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Prediction., , , and . ICC Workshops, page 1-6. IEEE, (2021)Integrated Imaging and Communication with Reconfigurable Intelligent Surfaces., and . ACSSC, page 151-156. IEEE, (2023)Millimeter Wave V2V Beam Tracking using Radar: Algorithms and Real-World Demonstration., , and . EUSIPCO, page 740-744. IEEE, (2023)