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Deep Learning-Based Limited Feedback Designs for MIMO Systems

Authors
Jang, JeonghyeonLee, HoonHwang, SangwonRen, HaibaoLee, Inkyu
Issue Date
4월-2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
MIMO; deep learning; limited feedback
Citation
IEEE WIRELESS COMMUNICATIONS LETTERS, v.9, no.4, pp.558 - 561
Indexed
SCIE
SCOPUS
Journal Title
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume
9
Number
4
Start Page
558
End Page
561
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/56894
DOI
10.1109/LWC.2019.2962114
ISSN
2162-2337
Abstract
We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.
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공과대학 (전기전자공학부)
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