Deep learning enabled optimization of downlink beamforming under per-antenna power constraints: algorithms and experimental demonstration

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-...

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Bibliographic Details
Main Authors: Juping Zhang, Wenchao Xia, Minglei You, Gan Zheng, Sangarapillai Lambotharan, Kai-Kit Wong
Format: Default Article
Published: 2020
Subjects:
Distributed computing and systems software not elsewhere classified
Array signal processing
Signal to noise ratio
Interference
Training
Downlink
Wireless communication
Optimization
Deep learning
Beamforming
Multiple-input-single-output (MISO)
Signal-to-interference-plus-noise ratio (SINR) balancing
Per-antenna power constraints
Distributed Computing
Online Access:https://hdl.handle.net/2134/13244591.v1
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https://hdl.handle.net/2134/13244591.v1

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