Deep Learning Enabled Multicast Beamforming With Movable Antenna Array

Beamforming with movable antenna (MA) array has recently attracted increasing attention as an enabling technology for next-generation wireless communications, e.g., 6G. In this letter, we consider a multicast scenario where a base station (BS) equipped with a linear MA array broadcasts common inform...

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Bibliographic Details
Published in:IEEE wireless communications letters 2024-07, Vol.13 (7), p.1848-1852
Main Author: Kang, Jae-Mo
Format: Article
Language:English
Subjects:
Antenna arrays
Array signal processing
Beamforming
Broadcasting antennas
Deep learning
Effectiveness
Feature extraction
movable antenna
multicast
Multicasting
Optimization
Training
Transmitting antennas
Vectors
Vectors (mathematics)
Wireless communications
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Summary:Beamforming with movable antenna (MA) array has recently attracted increasing attention as an enabling technology for next-generation wireless communications, e.g., 6G. In this letter, we consider a multicast scenario where a base station (BS) equipped with a linear MA array broadcasts common information to multiple users, each equipped with a single fixed-position antenna. Our objective is to jointly optimize the antenna position vector (APV) and antenna weight vector (AWV) by maximizing the minimum beamforming gain for the users, which is challenging to tackle analytically due to nonconvexity. To effectively and intelligently break through such challenge, we propose a novel deep learning (DL) model with three modules, namely, feature extractor, APV optimizer, and AWV optimizer. An effective training strategy for the proposed DL model is also developed in an unsupervised manner with a customized loss function. The superiority and effectiveness of the proposed scheme are confirmed through simulation results.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2024.3392924