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Found 6 papers, 2 papers with code
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems.
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
First, we propose an energy model for different beamforming structures.
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems.
Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning
Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems.
Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate.
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hybrid beamforming is a promising technology for 5G millimetre-wave communications.
