Multi-Agent Power and Resource Allocation for D2D Communications: A Deep Reinforcement Learning Approach

Computer Science

• multi agent 100%
• Resource Allocation 100%
• Deep Reinforcement Learning 100%
• Learning Approach 100%
• time-delay 100%
• Achievable Rate 100%
• Power Allocation 100%
• Delay Constraint 100%
• Resource Allocation Scheme 33%
• Wearable Device 33%
• Reinforcement Learning 33%
• Spectrum Resource 33%
• Distributed Algorithm 33%
• Performance Improvement 33%
• Multiple Agents 33%
• Channel Interference 33%
• Deep Q-Network 33%
• Transmission Time 33%
• Smartphone Device 33%

Engineering

• Achievable Rate 100%
• Reinforcement Learning 100%
• Learning Approach 100%
• Delay Constraint 100%
• Demonstrates 33%
• Transmissions 33%
• Simulation Result 33%
• Obtains 33%
• Power Control 33%
• Joints (Structural Components) 33%
• Delay Time 33%
• Learning Algorithm 33%
• Wearable Sensor 33%
• Performance Improvement 33%
• Spectrum Resource 33%
• Channel Interference 33%