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IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

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Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

Simulation for Conference Proceedings

Note that the main_test.py is the main running python file. To run it, please type

python3 main_test.py

in the bash or powershell

References and Acknowledgement

Both RIS Simulation and the System Model for this Research Project are based the research work provided by Brook1711.
We intended to fork the original repo for the system model (as stated below) as the base of this project.
However, GitHub does not allow a forked repo to be private.
Hence, we could not maintain our code based a forked version of the original repo, while keeping it private until the project is completed. We would like to express our utmost gratitude for Brook1711 and his co-authors for their research work.

RIS Simulation

RIS Simulation is based on the following research work:
SimRIS Channel Simulator for Reconfigurable Intelligent Surface-Empowered Communication Systems
The original simulation code is coded in matlab, this GitHub repo provides a Python version of the simulation.

System Model: RIS-aided mmWave UAV communications

The simulation of the System Model is provided by the following research work:
Learning-Based Robust and Secure Transmission for Reconfigurable Intelligent Surface Aided Millimeter Wave UAV Communications
The code is provided in this GitHub repo.

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IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

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