Multi-Agent Reinforcement Learning for Joint Cooperative Spectrum Sensing and Channel Access in Cognitive UAV Networks

Keyphrases

• UAV 100%
• Cognitive Radio 100%
• Channel Access 100%
• Multi-agent Reinforcement Learning 100%
• Cooperative Spectrum Sensing 100%
• Cognitive UAV Networks 100%
• Upper Confidence Bound 66%
• Hoeffding 66%
• Double Deep Q-network 66%
• Optimization Problem 33%
• Vanilla 33%
• Network Performance 33%
• Value Iteration 33%
• Numerical Simulation 33%
• Channel Allocation 33%
• Q-learning 33%
• Curse of Dimensionality 33%
• Primary Channel 33%
• Original Problem 33%
• Stable Strategy 33%
• Independent Learner 33%
• Reinforcement Learning Problems 33%
• Markov Games 33%
• Benchmark Algorithms 33%
• One-shot Optimization 33%
• Primary User Activity 33%
• Opportunistic Transmission 33%
• RL Algorithm 33%
• Deep Q Network Algorithm 33%

Computer Science

• Unmanned Aerial Vehicle 100%
• Channel Access 100%
• Cognitive Radio 100%
• Multi-Agent Reinforcement Learning 100%
• Deep Q-Network 66%
• Confidence Bound 66%
• Optimization Problem 33%
• Network Performance 33%
• Learning Problem 33%
• Network Algorithm 33%
• Numerical Simulation 33%

Engineering

• Joints (Structural Components) 100%
• Unmanned Aerial Vehicle 100%
• Reinforcement Learning 100%
• Cooperative Spectrum Sensing 100%
• Cognitive Radio 75%
• Transmissions 25%
• Computer Simulation 25%
• Optimisation Problem 25%
• Primary User 25%
• Dimensionality 25%
• Channel Allocation 25%
• Q-Learning 25%
• Coordinator 25%