DRL-based Distributed Resource Allocation for Edge Computing in Cell-Free Massive MIMO Network

Keyphrases

• Distributed Resource Allocation 100%
• Cell-free Massive MIMO 100%
• Edge Computing 100%
• MEC System 100%
• Cell-based 50%
• User-specific 50%
• Total Energy Consumption 50%
• Multimedia Services 50%
• Resource-constrained 50%
• Quality of Service Guarantee 50%
• Solution Approach 50%
• Cell-free 50%
• Resource Allocation Problem 50%
• Local Processing 50%
• Distributed Approach 50%
• Server Resource 50%
• Cell-free System 50%
• Distributed Solution 50%
• Learning Agents 50%
• Multi-agent Reinforcement Learning 50%
• Reinforcement Learning Framework 50%
• Computing Resource Allocation 50%
• Communication Resource Allocation 50%
• Mobile Edge Networks 50%
• Joint Communication 50%
• Joint Resource Allocation 50%
• Edge Server 50%

Computer Science

• Resource Allocation 100%
• Distributed Resource 100%
• Massive MIMO 100%
• Deep Reinforcement Learning 100%
• Edge Computing 100%
• Multi-Access Edge Computing 100%
• Learning Framework 50%
• Quality of Service 50%
• Multimedia Service 50%
• Service Requirement 50%
• Learning Agent 50%
• Allocation Problem 50%
• Local Information 50%
• Distributed Approach 50%
• Total Energy Consumption 50%
• Intensive Computation 50%
• Communication Resource 50%
• Multi-Agent Reinforcement Learning 50%
• Edge Server 50%

Engineering

• Free Cell 100%
• Distributed Resource 100%
• Massive MIMO 100%
• Edge Computing 100%
• Allocation 100%
• Joints (Structural Components) 66%
• Simulation Result 33%
• Energy Engineering 33%
• Quality of Service 33%
• End-Users 33%
• Resource Allocation Problem 33%
• Distributed Approach 33%
• Local Information 33%
• Service Requirement 33%
• Reinforcement Learning 33%
• Free System 33%
• Cooperative Multi 33%
• Distributed Solution 33%
• Network Edge 33%