PARAMOUNT: Towards generalizable deeP leARning for mmwAve beaM selectiOn using sUb-6GHz chaNnel measuremenTs

Computer Science

• Wireless Communication 100%
• Deep Learning 100%
• Deep Neural Network 100%
• Channel State Information 100%
• Visual Representation 50%
• Learning Approach 50%
• Convolutional Neural Network 50%
• Training Dataset 50%
• Transfer Learning 50%
• Neural Network Architecture 50%
• Linear Transformation 50%
• Physical Aspect 50%
• Multipath 50%
• Communication Domain 50%

Chemical Engineering

• Deep Learning 100%
• Deep Neural Network 100%
• Neural Network 50%

Keyphrases

• MmWave Beam Selection 100%
• Delay Profile 40%
• Wireless Communication Domain 20%
• Profile Representation 20%
• Multipath Clusters 20%