Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training

Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training

Abstract Deep learning (DL)-based beam training schemes have been exploited to improve spectral efficiency with fast optimal beam selection for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To achieve high prediction accuracy, these DL models rely on training with a...

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Bibliographic Details
Main Authors: Haohui Jia, Na Chen, Taisei Urakami, Hui Gao, Minoru Okada
Format: Article
Language:English
Published: SpringerOpen 2023-07-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
MmWave
Massive MIMO
Deep learning
Spatial attention
Feature quantization
Contrastive learning
Online Access:https://doi.org/10.1186/s13638-023-02277-w

Internet

https://doi.org/10.1186/s13638-023-02277-w

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