Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks

Keyphrases

• Access Cost 25%
• Allocation Scheme 25%
• Bandwidth Allocation 25%
• Cache System 25%
• Caching Scheme 50%
• Content Caching 100%
• Content Delivery 100%
• Content Placement 50%
• Content Popularity 25%
• Cooperative Caching 25%
• Deep Deterministic Policy Gradient 25%
• Deep Reinforcement Learning (deep RL) 100%
• Delivery Latency 50%
• Delivery Process 25%
• Edge Caching 25%
• Hit Ratio 25%
• Joint Optimization Problem 25%
• Low Computational Complexity 25%
• Macrocell 25%
• Markov Decision Process 25%
• Mixed Integer Linear Programming Problem 25%
• Mobile Edge Caching 25%
• Mobility State 25%
• Nature-inspired 25%
• Network State 25%
• Nonoperative 25%
• Path Availability 25%
• Process-based 25%
• Random Edge 25%
• Resource Availability 25%
• Roadside Unit 25%
• Small Time Scale 25%
• Smart Vehicle 25%
• Suboptimal Solutions 25%
• System Cost 25%
• Trilateral Cooperation 25%
• Vehicle Driving 25%
• Vehicle Mobility 25%
• Vehicle Network 25%
• Vehicle Scheduling 25%
• Vehicular Edge Computing 100%

Engineering

• Computation Complexity 33%
• Cooperative 100%
• Edge Computing 100%
• Joints (Structural Components) 66%
• Linear Programming Problem 33%
• Markov Decision Process 33%
• Mixed-Integer Linear Programming 33%
• Network State 33%
• Optimisation Problem 33%
• Reinforcement Learning 100%
• Roadsides 33%
• Simulation Result 33%
• System Cost 33%

Computer Science

• Bandwidth Allocation 25%
• Computation Complexity 25%
• Content Delivery 100%
• Deep Reinforcement Learning 100%
• Edge Caching 50%
• Edge Computing 100%
• Inspired Method 25%
• Linear Programming Problem 25%
• Markov Decision Process 25%
• Mixed-Integer Linear Programming 25%
• Optimization Problem 25%
• Resource Availability 25%