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Machine Learning for Channel Coding: A Paradigm Shift from FEC Codes
Kayode A. Olaniyi
,
Reolyn Heymann
,
Theo G. Swart
Electrical and Electronic Engineering Science
University of Johannesburg
Research output
:
Contribution to journal
›
Article
›
peer-review
6
Citations (Scopus)
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Keyphrases
Machine Learning
100%
Channel Coding
100%
Forward Error Correction Codes
100%
Communication Systems
50%
Computational Efficiency
50%
Channel Estimation
50%
Optimal Channels
50%
Channel Codes
50%
Communication Technologies
25%
Machine Learning Applications
25%
Competitive Performance
25%
Machine Learning Based
25%
Communications Applications
25%
Reliable Communication
25%
Open Research Issues
25%
Efficient Communication
25%
Technology Design
25%
Reduced Complexity
25%
Performance Flexibility
25%
Turbo
25%
Communication Algorithms
25%
LDPC Codes
25%
Modern Communication
25%
Capacity Approach
25%
Reduced Latency
25%
Current Channel
25%
ML Potential
25%
Deep Neural Architecture
25%
Computer Science
Channel Coding
100%
Error Correction Code
100%
Forward Error Correction
100%
Paradigm Shift
100%
Machine Learning
100%
Learning System
100%
Channel Estimation
50%
Research Problem
25%
Information Technology
25%
Neural Network Architecture
25%
Deep Neural Network
25%
low-density parity-check code
25%
Open Research
25%
Reliable Communication
25%
Technology Design
25%