Autonomous Mobility Management for 5G Ultra-Dense HetNets via Reinforcement Learning with Tile Coding Function Approximation

Keyphrases

• Reinforcement Learning 100%
• Mobility Management 100%
• Autonomous Mobility-on-demand (AMoD) 100%
• Function Approximation 100%
• Ultra-dense HetNets 100%
• Tile Coding 100%
• State Action 80%
• Hands-off Control 60%
• Handover Rate 40%
• User Equipment 40%
• Mobility Robustness 40%
• Control Policy 40%
• Maintenance Cost 20%
• Operating Cost 20%
• Control Method 20%
• Wireless Networks 20%
• Latency 20%
• Handover 20%
• Mobile Users 20%
• Dense Deployment 20%
• Unnecessary Handover 20%
• Operation Cost 20%
• Management Control 20%
• Control Approach 20%
• Speed Range 20%
• Value Function 20%
• Heterogeneous Network Topology 20%
• Near-zero 20%
• Seamless Connectivity 20%
• Reference Signal Received Power 20%
• Approximation Approach 20%
• Function Approximator 20%
• Small Cell Networks 20%
• Large Action Space 20%
• Action Function 20%
• Large State Space 20%
• Small Heterogeneous Networks 20%
• Increased Throughput 20%
• Ubiquitous Connectivity 20%
• Action Method 20%
• Linear Function 20%
• Node Density 20%
• Action Value 20%

Engineering

• Reinforcement Learning 100%
• Handover 100%
• HetNets 100%
• Policy Control 28%
• Simulation Result 14%
• Mobile User 14%
• Reference Signal 14%
• Operational Cost 14%
• Failure Rate 14%
• Received Power 14%
• Heterogeneous Network 14%
• Cell Network 14%
• Operation and Maintenance Cost 14%
• Network Topology 14%
• Value Function 14%
• Node Density 14%
• Approximators 14%
• Linear Function 14%
• Action Space 14%

Computer Science

• Reinforcement Learning 100%
• Function Approximation 100%
• Mobility Management 100%
• And-States 20%
• Control Method 20%
• Wireless Networks 20%
• Function Value 20%
• Control Approach 20%
• Network Topology 20%
• Operational Cost 20%
• Heterogeneous Network 20%
• Reference Signal 20%
• Linear Function 20%