Deep Reinforcement-based Q-Learning Approach for Improving Energy Efficiency and Resource Management in V2X Communication Networks
Author:
Affiliation:
1. Bannari Amman Institute of Technology,ECE Department,Sathyamangalam,TamilNadu,India
2. SNS College of Engineering,ECE Department,Coimbatore,TamilNadu,India
3. Presidency University, Itgalpura,ECE Department,Bengaluru,Karnataka,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10392286/10396847/10396888.pdf?arnumber=10396888
Reference15 articles.
1. Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system
2. Optimizing Resource Allocation With High-Reliability Constraint for Multicasting Automotive Messages in 5G NR C-V2X Networks
3. A Deep Reinforcement Learning-Based Two-Dimensional Resource Allocation Technique for V2I Communications
4. Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks
5. Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach
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