An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

Computer Science

• Waves 100%
• User 100%
• Machine Learning 100%
• Received Signal Strength 100%
• Wireless Networks 100%
• Accuracy 20%
• Use Case 20%
• Learning Approach 20%
• Access Point 20%
• Classification Accuracy 20%
• Supervised Learning 20%
• Decision Tree Classifier 20%
• Activity Recognition 10%
• Operating Condition 10%
• Channels 10%
• Quality of Service 10%
• Measurement System 10%
• Sliding Window 10%
• Processing Capability 10%
• Propagation Characteristic 10%
• Extracted Information 10%
• Wave Technology 10%
• Classification Performance 10%
• Ranking Algorithm 10%
• Constrained Set 10%
• Radio Resource Management 10%
• Feature Scaling 10%
• Series Segmentation 10%
• Mean Algorithm 10%