Deep Deterministic Policy Gradient for Throughput Maximization in Energy Harvesting NOMA-Cognitive Radio Network
Author:
Affiliation:
1. National Institute of Technology Raipur,Electronics & Communication Engineering,Raipur,Chhattisgarh,India
2. Kennesaw State University,Electrical Engineering,Kennesaw,GA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10079844/10079947/10080443.pdf?arnumber=10080443
Reference15 articles.
1. Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow;ravichandiran,2020
2. Dynamic User Clustering and Power Allocation for Uplink and Downlink Non-Orthogonal Multiple Access (NOMA) Systems
3. Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks
4. Extreme Learning Machine Based Cooperative Spectrum Sensing in Cognitive Radio Networks
5. Joint Power Control and Beamforming for Uplink Non-Orthogonal Multiple Access in 5G Millimeter-Wave Communications
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1. Energy‐Efficiency Maximization in Backscatter Communication‐Based Non‐Orthogonal Multiple Access System: Dinkelbach and Successive Convex Approximation Approaches;IET Signal Processing;2024-01
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