Reinforcement Learning-Based Resource Allocation for M2M Communications over Cellular Networks

Keyphrases

• Resource Allocation 100%
• Learning-based 100%
• Reinforcement Learning 100%
• Cellular Networks 100%
• Cross-tier Interference 100%
• M2M Communication 100%
• User Equipment 66%
• Spectral Efficiency 66%
• Optimal Power 66%
• Cellular User Equipment 66%
• Computational Complexity 33%
• Channel Power 33%
• Optimization Problem 33%
• Rate Performance 33%
• Existing Resources 33%
• Quality of Service 33%
• Resource Utilization 33%
• User Experience 33%
• Reinforcement Learning Algorithm 33%
• Capacity Efficiency 33%
• Quality of Service Guarantee 33%
• Network Capacity 33%
• Allocation Approach 33%
• Optimal Frequency 33%
• Spectrum Allocation 33%
• Allocation Scheme 33%
• Power Allocation 33%
• Achievable Rate 33%
• Available Channel 33%
• Frequency Allocation 33%
• Network Spectra 33%
• Pointer Network 33%
• Novel Resources 33%
• Machine-to-machine (M2M) Communications 33%
• Resource Block 33%

Computer Science

• Reinforcement Learning 100%
• Cellular Network 100%
• Quality of Service 100%
• Spectrum Efficiency 100%
• Resource Allocation 100%
• Optimization Problem 50%
• Computational Complexity 50%
• User Experience 50%
• Spectrum Allocation 50%
• Machine to Machines 50%
• Power Allocation 50%
• Resource Utilisation 50%
• Network Capacity 50%
• Achievable Rate 50%