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Journal Article Machine Learning-Based Vision-Aided Beam Selection for mmWave Multiuser MISO System
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Authors
Hyemin Ahn, Igbafe Orikumhi, Jeongwan Kang, Hyunwoo Park, Hyekyung Jwa, Jeehyeon Na, Sunwoo Kim
Issue Date
2022-06
Citation
IEEE Wireless Communications Letters, v.11, no.6, pp.1263-1267
ISSN
2162-2337
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/LWC.2022.3163780
Abstract
In this letter, we propose a machine learning-based vision-aided beam selection (ML-VBS) for millimeter-wave indoor multi-user communications. The proposed scheme is aimed at addressing the beam selection overhead with narrow beams in a multi-user scenario. The proposed scheme relies on a base station (BS) equipped with a single camera to observe the scene and estimates the angles to the multiple users. Given the estimated angle information and the limited number of radio frequency chains at the BS, two serial deep neural network structures are employed for joint user and beam selection subject to a minimum rate constraint. The numerical evaluation shows that the proposed ML-VBS scheme achieves a good performance in terms of the multi-user angle estimation, achievable sum rate and low computational complexity compared to conventional beam selection techniques.
KSP Keywords
Angle Estimation, Deep neural network(DNN), Learning-based, Low Computational Complexity, MISO systems, Minimum rate constraint, Multi-user communications, Neural network structure, Radio Frequency(RF), Selection techniques, User Scenario