ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Journal Article Deep Learning Methods for Universal MISO Beamforming
Cited 52 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Junbeom Kim, Hoon Lee, Seung-Eun Hong, Seok-Hwan Park
Issue Date
2020-11
Citation
IEEE Wireless Communications Letters, v.9, no.11, pp.1894-1898
ISSN
2162-2337
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/LWC.2020.3007198
Abstract
This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.
KSP Keywords
Beamforming optimization, Conventional methods, Deep neural network(DNN), Existing schemes, Learning methods, Multi-antenna systems, Numerical results, Power Constraint, Power Limitation, Transmit Power, base station