A Noise-Adaptive Machine Learning Framework for Optimizing User Grouping in Dynamic IM-OFDMA Systems

Keyphrases

• Index Modulation 100%
• Machine Learning Framework 100%
• Orthogonal Frequency Division multiple Access (OFDMA) 100%
• Multiple Access Systems 100%
• Noise Adaptive 100%
• User Grouping 100%
• Adaptive Machine Learning 100%
• Dynamic Index 100%
• Internal Noise 50%
• Noise Power 50%
• Optimization Algorithm 25%
• Specialized Model 25%
• Signal-to-noise Ratio 25%
• Extreme Gradient Boosting(XGBoost) 25%
• Wireless Communication Systems 25%
• Prediction Model 25%
• Selection Strategy 25%
• System Performance 25%
• Robust Performance 25%
• Improved System 25%
• Noise Spectrum 25%
• Noise Level 25%
• Ratio Level 25%
• Bit Rate 25%
• Dynamic Environment 25%
• Practical Setting 25%
• Distribution Shift 25%
• Noisy Environment 25%
• Noisy Conditions 25%
• Machine Learning Strategies 25%
• Stochastic Noise 25%
• Dynamic Noise 25%
• Sequential Bayesian Updating 25%
• Xgboost Machine Learning Algorithm 25%
• Algorithms Effectiveness 25%

Computer Science

• Learning Framework 100%
• Machine Learning 100%
• Learning System 100%
• Frequency Division Multiple Access 100%
• Extreme Gradient Boosting 50%
• Dynamic Environment 25%
• Machine Learning Algorithm 25%
• wireless communication system 25%
• Prediction Model 25%
• Systems Performance 25%
• Noise-to-Signal Ratio 25%
• Selection Process 25%
• Internal System 25%
• Bayesian Updating 25%