Deep Reinforcement Learning-based Power Allocation for Downlink RSMA System

Keyphrases

• Access Frequency 25%
• Base Station 25%
• Decoding Method 25%
• Deep Reinforcement Learning (deep RL) 100%
• Deep Reinforcement Learning Algorithm 25%
• Downlink Rate 100%
• Learning-based 100%
• Markov Decision Process 25%
• Maximum Sum Rate 25%
• Multiple Access Systems 100%
• Multiple Access Techniques 25%
• Network Environment 25%
• Non-orthogonal multiple Access 25%
• Optimization Problem 25%
• Orthogonal Frequency Division multiple Access (OFDMA) 25%
• Power Allocation 100%
• Process Framework 25%
• Rate-splitting multiple Access 100%
• Single Base Station 25%
• Stochastic Properties 25%
• Successive Decoding 25%
• Sum Rate 25%
• System Sum Rate 25%
• Transmit Power 25%

Computer Science

• Access Technique 25%
• Considered Model 25%
• Deep Reinforcement Learning 100%
• Frequency Division Multiple Access 25%
• Markov Decision Process 25%
• Multiple Access 100%
• Network Environment 25%
• Non-Orthogonal Multiple Access 25%
• Optimization Problem 25%
• Power Allocation 100%
• Process Framework 25%
• Reinforcement Learning 25%
• sum rate 75%

Engineering

• Basestation 50%
• Decoding Technique 25%
• Deep Reinforcement Learning 100%
• Markov Decision Process 25%
• Multiple Access 100%
• Network Environment 25%
• Non-Orthogonal Multiple Access 25%
• Optimisation Problem 25%
• Orthogonal Frequency Division Multiple Access 25%
• Power Allocation 100%
• Received Data 25%
• Reinforcement Learning 25%
• Simulation Result 25%
• Stochastic Property 25%