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Online Meta-Learning for Hybrid Model-Based Deep Receivers
Tomer Raviv
, Sangwoo Park
, Osvaldo Simeone
, Yonina C. Eldar
, Nir Shlezinger
Research output
:
Contribution to journal
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Article
›
peer-review
42
Scopus citations
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Dive into the research topics of 'Online Meta-Learning for Hybrid Model-Based Deep Receivers'. Together they form a unique fingerprint.
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Keyphrases
Deep Neural Network
100%
Online Learning
100%
Hybrid Model
100%
Meta-learning
100%
Deep Receivers
100%
Communication Channels
50%
Training Methods
50%
Bit Error Rate
50%
Neural Network
50%
Distribution Shift
50%
Training Strategy
50%
Training Mechanism
50%
Online Training
50%
Model Driven Architecture
50%
Dynamic Nature
50%
Receiver Design
50%
Channel Model
50%
Learning Scheme
50%
Linear Channel
50%
Model Interpretation
50%
Channel Realization
50%
Training Scheme
50%
Joint Learning
50%
Online Adaptation
50%
Modular Training
50%
Applications of Deep Neural Networks
50%
COST 2100
50%
Nonlinear Channel
50%
Self-supervised Learning
50%
Two-stage Training
50%
Computer Science
Deep Neural Network
100%
Learning Scheme
33%
Complex Environment
33%
Dynamic Nature
33%
Channel Realization
33%
Chemical Engineering
Deep Neural Network
100%