Title

Mobility-aware Joint Power Control and Incremental Spectrum Re-Allocation using Q-Learning for D2D communication.

Steps

• Clone the Repository https://github.com/AnirbanBanik1998/D2D.git

• For the 1st case in which D2D rx is not moving radially wrt D2D tx: python3 compare.py 0

• For the 2nd case in which D2D rx is moving radially wrt D2D tx: python3 compare.py 1

• To compare between the update parameters, run python3 converge.py 0 or 1

Documentation

• core -> This directory contains the core modules for running the simulation

1. config.py -> This configures the cell model, the D2Ds, the Cellular UEs, and Channels.
2. utils.py -> This contains some of the utility functions needed in the algorithm.
3. q_learn.py -> This contains the various functions for Q-learning, with different terminating conditions.
4. display.py -> Contains the functionalities for plotting the results.

• algo.py -> Contains the main algorithm defined. Power Control using Q-Learning is written as a separate functionality for multiprocessing.
• compare.py -> Compares the performance of mAQRP, mQRP, Open-Loop with Swapping, and Open-Loop algorithms.
• converge.py -> Compares the convergence of mAQRP algorithm with different update parameters.