Self-Optimizing Data Offloading in Mobile Heterogeneous Radio-Optical Networks: A Deep Reinforcement Learning Approach

Keyphrases

• Latency 100%
• Radio Frequency 100%
• Deep Reinforcement Learning (deep RL) 100%
• State Space 100%
• Dynamic Environment 100%
• Heterogeneous Networks 100%
• Optical Networks 100%
• Self-optimization 100%
• Heterogeneous Radios 100%
• Data Offloading 100%
• Reinforcement Learning Approach 100%
• Wireless Networks 50%
• Optimal Resource Allocation 50%
• Performance Analysis 50%
• Seamless Connectivity 50%
• Next Generation Wireless Networks 50%
• Macrocell 50%
• Internet of Things 50%
• Deep Neural Network 50%
• Energy Solutions 50%
• Position Level 50%
• Process Optimization 50%
• Gigabit 50%
• Numerical Simulation Analysis 50%
• Environmental Parameters 50%
• Visible Light Communication 50%
• Fast Convergence 50%
• Location Accuracy 50%
• Femtocell 50%
• Data Security 50%
• Frequency-based Approach 50%
• Data Transmission Rate 50%
• Decision-making Problem 50%
• Service Requirements 50%
• Network Solution 50%
• Numerical Performance 50%
• Handover 50%
• Mobility-aware 50%
• User Terminal 50%
• Centimeter Level 50%
• Time-triggered 50%
• Handover Parameters 50%
• Radio Frequency Link 50%
• Data Mobility 50%
• Optical Attocells 50%
• Heterogeneous Mobile Networks 50%
• Strong Sensitivity 50%
• Optical Light Communication 50%
• Optical Attocell Network 50%
• Line-of-sight Constraint 50%
• Hysteresis Margin 50%
• State Succession 50%

Engineering

• Radio Frequency 100%
• Reinforcement Learning 100%
• Learning Approach 100%
• Heterogeneous Network 100%
• Fiber Optic Networks 100%
• Handover 66%
• Data Rate 33%
• Computer Simulation 33%
• Data Transmission Rate 33%
• Deep Neural Network 33%
• Performance Analysis 33%
• Service Requirement 33%
• Internet of Things 33%
• Data Security 33%
• Adaptability 33%
• Macrocell 33%
• Environmental Parameter 33%
• Femtocells 33%
• Making Problem 33%
• Centimeter 33%
• Convergence Speed 33%
• Self-Optimization 33%
• Directionality 33%

Computer Science

• Optical Network 100%
• Heterogeneous Network 100%
• Deep Reinforcement Learning 100%
• Learning Approach 100%
• Wireless Networks 66%
• State Space 66%
• Dynamic Environment 66%
• Resource Allocation 33%
• Energy Efficient 33%
• Transmission Rate 33%
• Decision-Making 33%
• Visible Spectrum 33%
• Internet of Things 33%
• And-States 33%
• Numerical Simulation 33%
• Deep Neural Network 33%
• Performance Analysis 33%
• Service Requirement 33%
• Process Optimization 33%
• Fast Convergence 33%
• Experienced User 33%
• Convergence Speed 33%