@inproceedings{1852d551f69c418dae6676bbad186ba9,
title = "Deep Learning Method for Movable Antenna-Enabled Multiuser Downlink System",
abstract = "We investigate a movable antenna (MA)-enabled multiuser multiple-input single-output (MU-MISO) downlink system. In particular, we propose a deep learning-based algorithm comprising two deep neural networks (DNNs), where each DNN determines either the MA positions or key features of beamforming vectors. These DNNs are jointly trained to maximize the sum-rate performance. The effectiveness of the proposed method is demonstrated through numerical results.",
keywords = "deep learning, Movable antenna, multiuser beamforming",
author = "Dogon Kim and Park, \{Seok Hwan\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEICE.; 30th Asia-Pacific Conference on Communications, APCC 2025 ; Conference date: 26-11-2025 Through 28-11-2025",
year = "2025",
doi = "10.23919/APCC64555.2025.11279765",
language = "English",
series = "2025 30th Asia-Pacific Conference on Communications, APCC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 30th Asia-Pacific Conference on Communications, APCC 2025",
}