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Learn to Rapidly and Robustly Optimize Hybrid Precoding
Ortal Lavi,
Nir Shlezinger
Department of Electrical & Computer Engineering
School of Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
13
Scopus citations
Overview
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Keyphrases
Channel State Information
100%
Hybrid Precoding
100%
Multiple-input multiple-output
66%
Noisy Channel
66%
Hybrid Precoder
66%
Adaptation
33%
Transmitter
33%
Numerical Results
33%
Minimax
33%
Performance Improvement
33%
Deep Learning Methods
33%
Massive multiple-input multiple-output (mMIMO)
33%
Number of Iterations
33%
Precoding
33%
Precoder
33%
Sum-rate Maximization
33%
Gradient Projection
33%
Channel Condition
33%
Spectral Efficiency
33%
Learning to Optimize
33%
Achievable Sum Rate
33%
Hyperparameters
33%
Hyperparameter Setting
33%
Channel Realization
33%
Inference Speed
33%
Tolerable Error
33%
Analog Beamforming
33%
Mirror Prox
33%
Digital Beamforming
33%
Engineering
Channel State Information
100%
Precoders
100%
Multiple-Input Multiple-Output
66%
Deep Learning
33%
Massive MIMO
33%
Learning Technique
33%
Beamforming
33%
Channel Condition
33%
Spectral Efficiency
33%
Channel Realization
33%
Computer Science
precoding
100%
Channel State Information
75%
MIMO Systems
75%
sum rate
50%
Spectral Efficiency
25%
Deep Learning Technique
25%
Channel Condition
25%
Channel Realization
25%