Energy-Efficient D2D Communications Based on Centralised Reinforcement Learning Techniques
Author:
Affiliation:
1. University of Exeter,Department of Computer Science,Exeter,EX4 4QF,United Kingdom
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9724521/9724552/09724553.pdf?arnumber=9724553
Reference33 articles.
1. Energy Efficiency and Delay Tradeoff in Device-to-Device Communications Underlaying Cellular Networks
2. Power Control for D2D Communication Using Multi-Agent Reinforcement Learning;zhao;2018 IEEE/CIC International Conference on Communications in China ICCC 2018,2019
3. Q-learning based power control algorithm for D2D communication
4. Model-Based Reinforcement Learning via Proximal Policy Optimization
5. Power allocation in a secure-aware device-todevice communication underlaying cellular network;qu;2016 8th International Conference on Wireless Communications and Signal Processing WCSP 2016,2016
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1. Resource Allocation in D2D‐Enabled 5G Networks Using Multiagent Reinforcement Learning;Journal of Computer Networks and Communications;2024-01
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