OSCAR: a Contention Window Optimization approach using Deep Reinforcement Learning to quickly learn the optimal policies under different network conditions

A working ns3-gym environment is required. Moreover, a wandb account is required to show the results.

Clone this repository so that it lands inside ns3-gym/scratch/linear-mesh. Edit run = wandb.init(entity="xraulz", project="contention_window", tags = wtags) in each training/test file and change entity=xraulz with entity=your_wandb_username, where your_wand_username is the wandb account username.

Launch python OSCAR_train.py to train the OSCAR algorithm.

Launch python CCOD_train.py to train the CCOD algorithm.

Launch python standard_test_and_ccod_train.py to test the 802.11 algorithm and train the CCOD algorithm.

@INPROCEEDINGS{10279663,
  author={Grasso, Christian and Raftopoulos, Raoul and Schembra, Giovanni},
  booktitle={ICC 2023 - IEEE International Conference on Communications}, 
  title={OSCAR: A Contention Window Optimization Approach Using Deep Reinforcement Learning}, 
  year={2023},
  volume={},
  number={},
  pages={459-465},
  doi={10.1109/ICC45041.2023.10279663}}