Metadata-Version: 2.4
Name: mapc_optimal
Version: 0.2.1
Summary: Optimal solution of the MAPC (C-SR) problem for IEEE 802.11 networks
Author-email: Maksymilian Wojnar <maksymilian.wojnar@agh.edu.pl>, Wojciech Ciężobka <wojciech.ciezobka@agh.edu.pl>, Artur Tomaszewski <artur.tomaszewski@pg.edu.pl>, Piotr Chołda <piotr.cholda@agh.edu.pl>, Krzysztof Rusek <krzysztof.rusek@agh.edu.pl>, Katarzyna Kosek-Szott <katarzyna.kosek-szott@agh.edu.pl>, Szymon Szott <szymon.szott@agh.edu.pl>
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Keywords: 802.11,coordinated spatial reuse,multi-access point coordination,optimization
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy~=2.3.4
Requires-Dist: pulp~=3.3.0
Dynamic: license-file

# Optimal solution for IEEE 802.11 MAPC Coordinated Spatial Reuse (C-SR) problem

`mapc-optimal` is a tool for finding the optimal solution of the Multi-Access Point Coordination (MAPC) scheduling 
problem with coordinated spatial reuse (C-SR) for IEEE 802.11 networks. It provides a mixed-integer linear programming
(MILP) solution to find the upper bound on network performance. A detailed description can be found in:

- Maksymilian Wojnar, Wojciech Ciężobka, Artur Tomaszewski, Piotr Chołda, Krzysztof Rusek, Katarzyna Kosek-Szott, Jetmir Haxhibeqiri, Jeroen Hoebeke, Boris Bellalta, Anatolij Zubow, Falko Dressler, and Szymon Szott. "Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks", 2025.

## Features

- **Calculation of optimal scheduling**: Calculate the best transmission configurations and the corresponding time 
  division that enhance the network performance.
- **Two optimization criteria**: Find the optimal solution for two optimization criteria: maximizing the sum of the 
  throughput of all nodes in the network and maximizing the minimum throughput of all nodes in the network.
- **Modulation and coding scheme (MCS) selection**: Select the optimal MCS for each transmission.
- **Transmission power selection**: Set the appropriate transmission power to maximize network performance.
- **Versatile network configuration**: Define network settings by specifying network nodes, available MCSs, 
  and transmission power levels.

## Installation

The package can be installed using pip:

```bash
pip install mapc-optimal
```

## Usage

The main functionality is provided by the `Solver` class in `mapc_optimal/solver.py`. This class manages the process of 
finding the optimal solution. Example usage:

```python
from mapc_optimal import Solver

# Define your network
# ...

solver = Solver(stations, access_points)
configurations, rate = solver(path_loss)
```

where `stations` and `access_points` are lists of numbers representing the stations and access points (APs) in the 
network, respectively. The `path_loss` is an $n \times n$ matrix representing the path loss between each pair of nodes 
in the network. The solver returns calculated configurations and the total throughput of the network. The `Solver` 
class can be further configured by passing additional arguments to the constructor. The full list of arguments can 
be found in the [documentation](...). 

By default, the solver associates APs with the stations that have the highest signal strength. However, the solver can
be configured to use a different association policy. To do so, set the `associations` argument when calling the solver. 
Additionally, the solver can return a list of the pricing objective values for each iteration. It can be useful to 
check if the solver has converged. To do so, set the `return_objective` argument to `True` when calling the solver:

```python
configurations, rate, objectives = solver(path_loss, associations, return_objective=True)
```

For a more detailed example, refer to the test case in `test/test_solver.py`.

**Note:** The underlying MILP solver can significantly affect the performance of the tool. By default, the solver 
uses the `CBC` solver from the `PuLP` package. However, we recommend using a better solver, such as `CPLEX`.

## Repository Structure

The repository is structured as follows:

- `mapc_optimal/`: The main package of the tool.
  - `constants.py`: Default values of the parameters used in the solver.
  - `main.py`: The formulation of the main problem solving the selection and division of configurations.
  - `pricing.py`: The pricing algorithm used to propose new configurations for the main problem.
  - `solver.py`: The solver class coordinating the overall process of finding the optimal solution. It initializes the 
     solver, sets up the network configuration, and manages the iterations.
  - `utils.py`: Utility functions, including the function for calculation of the path loss from node positions using 
    the TGax channel model.
- `test/`: Unit tests with example usage of the tool.

## How to reference `mapc-optimal`?

```
@article{wojnar2025coordinated,
  author={Wojnar, Maksymilian and Ciężobka, Wojciech and Tomaszewski, Artur and Chołda, Piotr and Rusek, Krzysztof and Kosek-Szott, Katarzyna and Haxhibeqiri, Jetmir and Hoebeke, Jeroen and Bellalta, Boris and Zubow, Anatolij and Dressler, Falko and Szott, Szymon},
  title={{Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks}}, 
  year={2025},
}
```
