QSPCA: A Two-Stage Efficient Power Control Approach in D2D Communication for 5G Networks
Author:
Affiliation:
1. Wireless Sensor Network Lab, National Institute of Technology Patna,Department of Electronics and Communication Engineering,Patna,India,800005
2. Trakya University,Department of Electrical-Electronics Engineering,Edirne,Turkey,22030
Funder
Science and Engineering Research Board (SERB-DST), Govt. of India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Link
http://xplorestaging.ieee.org/ielx7/9195266/9733244/09733300.pdf?arnumber=9733300
Reference21 articles.
1. A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks
2. Q-learning based power control algorithm for D2D communication
3. An Adaptive Protection of Flooding Attacks Model for Complex Network Environments
4. Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching
5. Smart Mode Selection Using Online Reinforcement Learning for VR Broadband Broadcasting in D2D Assisted 5G HetNets
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