mshokrnezhad/6G_Research_Directions

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mshokrnezhad/6G_Research_Directions

mshokrnezhad

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Identifying New Challenges and Opportunities, Conceptualizing Innovative Solutions, and Setting a Research Agenda to Advance the SOTA

DRL_GNN

The code shows how we can combine the ideas of deep reinforcement learning and graph neural networks

VFP_DDQL_DNN

A Virtual Function Placement (VFP) Method Using Double Deep Q-Learning (DDQL) Powered by Linear Deep Neural Network (DNN) Layers

iLunarLander

Instructing an Actor-Critic Agent to Land on the Moon

VFP_DDQL_GATConv

A Virtual Function Placement (VFP) Method Using Double Deep Q-Learning (DDQL) Powered by GATConv (a Graph Neural Network -GNN- Layer)

VFP_DDQL_A3TGCN

A Virtual Function Placement (VFP) Method Using Double Deep Q-Learning (DDQL) Powered by A3TGCN (a Graph Neural Network -GNN- Layer)

Dueling_for_DRL

Implementation of "Dueling Network Architectures for Deep Reinforcement Learning" paper

REST_ONOS

Creating Applications for ONOS using REST API

ORARS_in_MD2D

Optimized Resource Allocation and Relay Selection in Multi-hop D2D Networks

Stock_Market_Analysis

To Analyse the Iran's Stock Market

ReNet

A Network Environment to Test Reinforcement Learning Techniques

Delay_Aware_Gateway_Selection

Selecting Gateways in a Wireless Sensor Network to Minimize Delay