Deep Reinforcement Learning-Based Joint Trajectory Design and Resource Allocation for Secure and Energy-Efficient UAV Networks

Keyphrases

• Resource Allocation 100%
• Learning-based 100%
• Energy Efficient 100%
• Deep Reinforcement Learning (deep RL) 100%
• Joint Trajectory 100%
• Unmanned Aerial Vehicle 100%
• Unmanned Aerial Vehicle Network 100%
• Trajectory Design 100%
• Wireless Networks 66%
• Utility Function 33%
• Energy Harvesting 33%
• Optimization Problem 33%
• Problem-based Learning 33%
• Eavesdropper 33%
• Optimization Approach 33%
• Energy Use Efficiency 33%
• Secrecy Rate 33%
• Vehicle Power 33%
• High-dimensional Data 33%
• Non-convexity 33%
• Power Harvesting 33%
• Environmental Dynamics 33%
• Deep Reinforcement Learning Algorithm 33%
• Trajectory Optimization 33%
• Proximal Policy Optimization 33%
• Proximal Policy Optimization Algorithm 33%
• Conventional Reinforcement 33%
• Vehicle Base 33%
• Learning-based Method 33%
• Joint Optimization 33%
• Vehicle Energy 33%
• Rate-energy 33%
• 3D Positioning 33%

Computer Science

• Energy Efficient 100%
• Deep Reinforcement Learning 100%
• Resource Allocation 100%
• Unmanned Aerial Vehicle 100%
• Wireless Network 50%
• Utility Function 25%
• Dynamic Environment 25%
• Optimization Problem 25%
• Optimization Algorithm 25%
• High Dimensionality 25%
• Reinforcement Learning 25%
• Learning Problem 25%
• Reinforcement Learning-Based Method 25%