Paper and Code for Machine Learning Based Wireless Communication Optimization Problems

This is a collection of Paper/Code for Wireless Communication Optimization Problems solved with machine learning based algorithms.

List of Papers with Code

Paper	Code
Deep Reinforcement Learning Based Dynamic Resource Allocation in 5G Ultra-Dense Networks	drl-cran-release1 (TF)
A Machine Learning Approach for Power Allocation in HetNets Considering QoS	proximity (MATLAB)
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks	dql_main (TF)
Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation	CSI_Estimation_in_5G_Technology_Using_Depp_Learning (TF)
Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks	Federated2Fog (TF)
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks	DROO (TF/PT)
Intelligent Resource Allocation in Wireless Communications Systems	IRAWCS (TF)
Deep Reinforcement Learning Based Resource Allocation for V2V Communications	Double-Deep-Q-Learning-for-Resource-Allocation (TF)
Communication-Efficient Learning of Deep Networks from Decentralized Data	Federated-Learning (PT)
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks	Power-Control-asilomar (TF)
Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks	DDLO (TF)

Code
RL-for-binary-computation-offloading-in-wireless-powered-MEC-networks (TF)
Applications of Deep Learning in Resource Allocation in Wireless Networks (PT)
drl-cran-release1 (TF)
Deep-Q-Learning-based-Downlink-Power-Allocation (TF)