Decentralized Learning for Channel Allocation in IoT Networks Over Unlicensed Bandwidth as a Contextual Multi-Player Multi-Armed Bandit Game

Keyphrases

• Channel Allocation 100%
• Decentralized Learning 100%
• Multi-Player Multi-Armed Bandit (MPMAB) 100%
• IoT Networks 100%
• Channel State Information 66%
• Underlay 33%
• Collision-free 33%
• Trial-and-error 33%
• Computational Resources 33%
• Regret 33%
• Learning Algorithm 33%
• Multi-channel 33%
• Socially Optimal Strategy 33%
• Web-based 33%
• Operating Time 33%
• Cellular Networks 33%
• Time-varying Channels 33%
• Small Devices 33%
• Study Map 33%
• Optimal Channels 33%
• Channel Allocation Problem 33%
• Bandit Algorithms 33%
• Channel Quality 33%
• Channel Condition 33%
• IoT Devices 33%
• Partial Observation 33%
• Policy Learning 33%
• Device Mobility 33%
• Internet of Things Networks 33%
• Small-scale Fading 33%
• Decentralized Channel Allocation 33%
• Decentralized IoT 33%
• Channel Sensing 33%
• Network Scalability 33%

Computer Science

• Internet-Of-Things 100%
• Channel State Information 20%
• Learning Algorithm 20%
• Allocation Problem 20%
• State Information 20%
• Cellular Network 20%
• Computational Resource 20%
• Channel Quality 20%
• time-varying channel 20%
• Channel Condition 20%
• Multiple Channel 20%
• Unknown Pattern 20%