This repository contains the implementation of a Multi-Armed Bandit (MAB) algorithm for Multi-Access Point Coordination (MAPC). The MAB algorithm aims to solve the scheduling problem in coordinated spatial reuse (C-SR) by suggesting valid and fair AP-station pairs for simultanous transmissions. Its detailed operation and performance analysis can be found in:

• Maksymilian Wojnar, Wojciech Ciezobka, Katarzyna Kosek-Szott, Krzysztof Rusek, Szymon Szott, David Nunez, and Boris Bellalta. "IEEE 802.11bn Multi-AP Coordinated Spatial Reuse with Hierarchical Multi-Armed Bandits", $JOURNAL_NAME_TODO, 2024. [TODO_PREPRINT_INSERT, TODO_PUBLICATION_INSERT]

All the requirements can be installed automatically with the following commands:

cd mapc-mab   # Project root where the .toml file is located
pip install .

A complete list of dependencies is also provided in pyproject.toml.

mapc-mab uses a dedicated network simulator for the evaluation of the agents. The simulator is published as a pip package and is installed automatically with mapc-mab.

However, you can also install the simulator from the source code to allow for changes without the need to reinstall the package. To do so, follow these steps:

git clone [email protected]:ml4wifi-devs/mapc-sim.git
cd $PATH_TO_MAPC_MAB
pip install -e $PATH_TO_MAPC_SIM

The standard workflow for running the simulation is as follows:

1. Define a simulation configuration in a JSON file (e.g., default_config.json):

{
  "n_reps": 10,
  "seed": 42,
  "scenarios": [
    {
      "scenario": "simple_scenario_5",
      "name": "scenario_10m",
      "n_steps": 600,
      "params": {
        "d_ap": 10.0,
        "d_sta": 2.0,
        "mcs": 11
      }
    }
  ],
  "agents": [
    {
      "name": "EGreedy",
      "params": {
        "e": 9.0e-05,
        "optimistic_start": 556.0
      }
    }
  ]
}

2. Run the simulation with the following command:

python mapc_mab/envs/run.py -c $PATH_TO_CONFIG_FILE

3. This will generate an all_results.json file in the project root.

4. Plot the experiment results with the following command (-f is the option to provide the results file path):

python mapc_mab/plots/article/data_rate_plot_combined.py -f all_results.json

The repository is organized into three main directories:

• envs: This directory contains the environment part, namely the simulation scenarios, JSON configuration files and the running script.

  • scenarios.py contains an abstract class for simulation scenario definition and static_scenarios.py contains example scenarios with different network topologies (including non-line-of-sight topologies)
  • default_config.json: Example configuration file
  • run.py: Starts the simulation with the configuration given in a JSON file.

• agents: This directory contains the implementation of a MAB algorithm.

  • mapc_agent_factory.py: Factory pattern for creating a hierarchical MAPC agent with freedom of choosing the base bandit.
  • mapc_agent.py: Base class for MAPC agents responsible for the selection of AP-stations pairs.
  • tuning: Directory with code for optimizing agent hyperparameters.

• plots: This directory contains scripts for plotting simulation results.

  • article: Scripts for generating figures used in the published article.
  • article/data_rate_plot_combined.py: It is the most important plotting feature that plots the combined results of an experiment.

• The code and the simulator are written in JAX, which is an autodiff library similar to PyTorch or TensorFlow. This means that it may require additional dependencies or configurations to run properly, especially with GPU acceleration. For more information on JAX, please refer to the official JAX repository.

@INPROCEEDINGS{TODO,
  author={Wojnar, Maksymilian and Ciezobka, Wojciech and Kosek-Szott, Katarzyna and Rusek, Krzysztof and Szott, Szymon and Nunez, David and Bellalta, Boris},
  booktitle={TODO}, 
  title={{IEEE 802.11bn Multi-AP Coordinated Spatial Reuse with Hierarchical Multi-Armed Bandits}}, 
  year={2024},
  volume={},
  number={},
  pages={},
  doi={TODO}
}

We hope this repository is useful for your research!