DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls

Keyphrases

• Backhaul 100%
• Deep Learning Methods 100%
• Millimeter Wave 100%
• Network Utility 100%
• Sanity Checks 50%
• Rate Assignment 50%
• Utility Gains 50%
• Global Optimization Algorithm 50%
• Order of Magnitude 50%
• Deep Learning 50%
• Capacity Region 50%
• Delay-sensitive 50%
• Backhaul Link 50%
• Utility Framework 50%
• Conflicting Requirements 50%
• Highly Dynamic 50%
• Dynamic Traffic 50%
• Network Programmability 50%
• Network Slice 50%
• Network Capacity 50%
• Neural Network 50%
• Physical Infrastructure 50%
• Nonconvex Objective Function 50%
• Traffic Demand 50%
• Admissibility 50%
• Convolutional Block 50%
• Service Type 50%
• Logical Networks 50%
• Greedy Approach 50%
• Resource Allocation 50%
• Monetary Value 50%
• Traffic Regimes 50%
• Millimeter Wave Backhaul Networks 50%
• Rate Allocation 50%
• 5G mmWave 50%
• Optimal Rate 50%
• Service Level 50%
• Service Utility 50%
• Learning Solutions 50%

Computer Science

• Deep Learning Method 100%
• Learning Approach 100%
• Traffic Demand 50%
• Programmability 50%
• Network Capacity 50%
• Dynamic Traffic 50%
• Objective Function 50%
• Utility Service 50%
• Global Optimization 50%
• Logical Network 50%
• Physical Infrastructure 50%
• Capacity Region 50%
• Optimization Algorithm 50%
• Monetary Value 50%
• Neural Network 50%
• Backhaul Network 50%