A Lightweight Machine Learning Assisted Power Optimization for Minimum Error in NOMA-CRS Over Nakagami-$m$ Channels.
Ferdi KaraHakan KayaHalim YanikomerogluPublished in: IEEE Trans. Veh. Technol. (2021)
Keyphrases
- lightweight
- minimum error
- machine learning
- fading channels
- machine learning methods
- pattern recognition
- dos attacks
- wireless sensor networks
- supervised learning
- learning tasks
- communication infrastructure
- feature selection
- development environments
- bayes optimal
- minimax probability machine
- classification error
- learning algorithm
- power consumption
- model selection
- text classification
- support vector machine
- decision trees
- communication networks
- machine learning algorithms
- active learning
- support vector