Beam Prediction for mmWave V2I Communication Using ML-Based Multiclass Classification Algorithms

Keyphrases

• Machine Learning Based 100%
• Millimeter Wave V2I Communications 100%
• Multi-class Classification Algorithm 100%
• Beam Prediction 100%
• Codebook Size 66%
• Prediction Accuracy 50%
• Split Ratio 50%
• Position-aided 50%
• Decision Tree 33%
• Support Vector Machine 33%
• Beam Management 33%
• Machine Learning Algorithms 33%
• Performance Prediction 16%
• Millimeter-wave Frequency 16%
• Millimeter Wave 16%
• Cellular Networks 16%
• System Complexity 16%
• Management Procedure 16%
• Prediction Approach 16%
• Experimental Dataset 16%
• Area under the Receiver Operating Characteristic Curve 16%
• K-nearest 16%
• Specificity Score 16%
• Misclassification 16%
• Vehicular Ad Hoc Networks 16%
• Reliable Communication 16%
• Synthetic Data 16%
• High-intensity Beam 16%
• Neighborhood Support 16%
• Precision Score 16%
• Precision-recall 16%
• Machine Learning Framework 16%
• Machine Decisions 16%
• Vision-based 16%
• Beam Training 16%
• Nave Bayes Algorithm 16%
• Training Overhead 16%
• Wireless Resources 16%
• Vehicle-to-infrastructure (V2I) Communication 16%
• Artificial Intelligence Machine Learning 16%
• Multimodal Sensing 16%
• Confusion Matrix 16%
• Learning to Optimize 16%
• Classification Statistics 16%
• Beam Codebook 16%
• Recall Score 16%
• High-mobility Scenarios 16%
• F1 Score 16%

Computer Science

• Learning System 100%
• Machine Learning 100%
• Classification Algorithm 100%
• Prediction Accuracy 100%
• Multiclass Classification 100%
• Decision Tree 66%
• Support Vector Machine 66%
• Machine Learning Algorithm 66%
• Cellular Network 33%
• Artificial Intelligence 33%
• Characteristic Curve 33%
• Vehicular Network 33%
• Reliable Communication 33%
• Nave Bayes 33%
• Prediction Performance 33%
• Learning Framework 33%
• Synthetic Datasets 33%
• System Complexity 33%
• Experimental Dataset 33%
• Confusion Matrix 33%

Engineering

• Millimeter Wave 100%
• Learning System 100%
• Classification Algorithm 100%
• Multiclass Classification 100%
• Codebook Size 100%
• Support Vector Machine 50%
• Machine Learning Algorithm 50%
• Nearest Neighbor 25%
• Metrics 25%
• Artificial Intelligence 25%
• Case Scenario 25%
• Vehicle to infrastructure 25%
• Reliable Communication 25%
• Data Sample 25%
• Receiver Operating Characteristic 25%
• Prediction Performance 25%
• Operating-Characteristic Curve 25%
• Confusion Matrix 25%
• Misclassification 25%