ChannelAttention: Utilizing Attention Layers for Accurate Massive MIMO Channel Feedback

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Recently the idea of using deep learning algorithms in massive multiple-input multiple-output channel state information feedback has been studied in great detail. To use the deep-learning-based feedback schemes over the air, they must be able to operate under extremely large antennas. These schemes also need to be verified under a realistic channel model and propagation environment. Thus, we propose ChannelAttention, a deep-learning-based channel state information feedback scheme that utilizes attention layers and residual blocks. Simulations for a 64-by-64 QuaDRiGa channel model based on the 3GPP 38.901 urban microcell scenario show that ChannelAttention surpasses the normalized mean square error and cosine similarity performance of the conventional CsiNet+ scheme across all compression ratio ranges.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-05
Language
English
Article Type
Article
Citation

IEEE WIRELESS COMMUNICATIONS LETTERS, v.10, no.5, pp.1079 - 1082

ISSN
2162-2337
DOI
10.1109/LWC.2021.3057934
URI
http://hdl.handle.net/10203/285400
Appears in Collection
EE-Journal Papers(저널논문)
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