| Name | mapc-sim JSON |
| Version |
0.1.10
JSON |
| download |
| home_page | None |
| Summary | IEEE 802.11 MAPC (C-SR) simulator |
| upload_time | 2025-02-12 18:29:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Creative Commons Legal Code
CC0 1.0 Universal
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|
| keywords |
802.11
coordinated spatial reuse
multi-access point coordination
simulator
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# IEEE 802.11 MAPC Coordinated Spatial Reuse (C-SR) Simulator
`mapc-sim` is a simulation tool for IEEE 802.11 Multi-Access Point Coordination (MAPC) scenarios with coordinated
spatial reuse (C-SR). It provides a framework for modeling and analyzing the performance of wireless networks under
various configurations and environmental conditions. A detailed description 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", 2025.
- 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
- **Simulation of C-SR**: You can simulate the C-SR performance of an 802.11 network, including the effects of hidden
nodes, variable transmission power, node positions, and modulation and coding schemes (MCS). Calculate the aggregated
effective data rate.
- **TGax channel model**: The simulator incorporates the TGax channel model for realistic simulation in enterprise scenarios. The
simulator also supports the effects of wall attenuation and random noise in the environment.
- **JAX JIT compilation**: The simulator is written in JAX, which enables just-in-time (JIT) compilation and hardware acceleration.
- **Reproducibility**: The simulator uses JAX's pseudo random number generator (PRNG) to generate random numbers. This ensures that the
simulator is fully reproducible and you will get the same results for the same input parameters.
## Repository Structure
The repository is structured as follows:
- `mapc_sim/`: Main package containing the simulator.
- `constants.py`: Physical and MAC layer constants used in the simulator.
- `sim.py`: Main simulator code.
- `utils.py`: Utility functions, including the TGax channel model.
- `test/`: Unit tests and benchmarking scripts.
## Installation
The package can be installed using pip:
```bash
pip install mapc-sim
```
## Usage
The main functionality is provided by the `network_data_rate` function in `mapc_sim/sim.py`. This function calculates
the effective data rate for a given network configuration. Example usage:
```python
import jax
import jax.numpy as jnp
from mapc_sim.sim import network_data_rate
# Random number generator key
key = jax.random.PRNGKey(42)
# Transmission matrix - 1 if node i transmits to node j, 0 otherwise
tx = jnp.zeros((n_nodes, n_nodes))
tx = tx.at[i_0, j_0].set(1)
tx = tx.at[i_1, j_1].set(1)
...
tx = tx.at[i_n, j_n].set(1)
# Node positions
pos = jnp.array([
[x_0, y_0],
[x_1, y_1],
...
[x_n, y_n],
])
# MCS values of transmitting nodes
mcs = jnp.array([mcs_0, mcs_1, ..., mcs_n], dtype=int)
# Transmission power of transmitting nodes
tx_power = jnp.array([tx_power_0, tx_power_1, ..., tx_power_n])
# Standard deviation of the white Gaussian noise
sigma = 2.
# Walls matrix - 1 if there is a wall between node k and node l, 0 otherwise
walls = jnp.zeros((n_nodes, n_nodes))
walls = walls.at[k_0, l_0].set(1)
walls = walls.at[k_1, l_1].set(1)
...
walls = walls.at[k_m, l_m].set(1)
# Calculate the effective data rate with the simulator
data_rate = network_data_rate(key, tx, pos, mcs, tx_power, sigma, walls)
```
For more detailed examples, refer to the test cases in `test/test_sim.py`.
### JAX JIT Compilation
The simulator is written in JAX, which enables just-in-time (JIT) compilation and hardware acceleration.
The use of JIT is strongly recommended as it can improve the performance of the simulator by orders of magnitude.
To enable JIT, apply the `jax.jit` transformation on the simulator function:
```python
import jax
from mapc_sim.sim import network_data_rate
# Define your network configuration
# ...
network_data_rate_jit = jax.jit(network_data_rate)
data_rate = network_data_rate_jit(key, tx, pos, mcs, tx_power, sigma, walls)
```
As the `jax.jit` transformation can be applied to any function, you can also use it to JIT-compile closures.
For example, you can JIT-compile the `network_data_rate` function with a fixed network configuration as follows:
```python
from functools import partial
import jax
from mapc_sim.sim import network_data_rate
pos = ...
walls = ...
network_data_rate_jit = jax.jit(partial(
network_data_rate,
pos=pos,
walls=walls,
))
# Define the remaining values
# ...
data_rate = network_data_rate_jit(key=key, tx=tx, mcs=mcs, tx_power=tx_power, sigma=sigma)
```
### Reproducibility
The simulator uses JAX's PRNG. This ensures that the simulator is fully reproducible. However, the same key should
be used at most once for each simulation so that the results are not correlated. For example, you can generate a new
key and split it into two keys in each step of a simulation:
```python
import jax
from mapc_sim.sim import network_data_rate
# Define your network configuration
# ...
key = jax.random.PRNGKey(42)
for _ in range(n):
# Generate two new keys, one for the current step and one for the next splits
key, subkey = jax.random.split(key)
data_rate = network_data_rate(subkey, tx, pos, mcs, tx_power, sigma, walls)
```
### 64-bit Floating Point Precision
If you want to use 64-bit floating point precision, you can set the appropriate environment variable before running
your script:
```bash
export JAX_ENABLE_X64="True
```
Alternatively, you can set the environment variable in your Python script:
```python
import os
os.environ["JAX_ENABLE_X64"] = "True"
```
## Testing and Benchmarking
Run the unit tests to ensure everything is working correctly:
```bash
python -m unittest
```
You can benchmark the performance of the simulator using `test/sim_benchmark.py`.
## Additional Notes
- The simulator is written in JAX, an autodiff library for Python. It may require additional dependencies or
configurations to run properly, especially with hardware acceleration. For more information on JAX, please refer
to the official [JAX repository](https://jax.readthedocs.io/en/latest/).
## How to reference `mapc-sim`?
If you use this repository or tool in your research, please cite the following paper:
```
@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},
}
```
For a detailed description of the tool, you may also refer to:
```
@article{wojnar2025ieee,
author={Wojnar, Maksymilian and Ciezobka, Wojciech and Kosek-Szott, Katarzyna and Rusek, Krzysztof and Szott, Szymon and Nunez, David and Bellalta, Boris},
title={{IEEE 802.11bn Multi-AP Coordinated Spatial Reuse with Hierarchical Multi-Armed Bandits}},
year={2025},
}
```
Raw data
{
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"requires_python": ">=3.10",
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"keywords": "802.11, coordinated spatial reuse, multi-access point coordination, simulator",
"author": null,
"author_email": "Maksymilian Wojnar <maksymilian.wojnar@agh.edu.pl>, Wojciech Ci\u0119\u017cobka <wojciech.ciezobka@agh.edu.pl>, Katarzyna Kosek-Szott <katarzyna.kosek-szott@agh.edu.pl>, Krzysztof Rusek <krzysztof.rusek@agh.edu.pl>, Szymon Szott <szymon.szott@agh.edu.pl>",
"download_url": "https://files.pythonhosted.org/packages/81/ef/94cebaa3be22a2be5311a58190ecd019601a7b2d2856b791dea51f338106/mapc_sim-0.1.10.tar.gz",
"platform": null,
"description": "# IEEE 802.11 MAPC Coordinated Spatial Reuse (C-SR) Simulator\n\n`mapc-sim` is a simulation tool for IEEE 802.11 Multi-Access Point Coordination (MAPC) scenarios with coordinated \nspatial reuse (C-SR). It provides a framework for modeling and analyzing the performance of wireless networks under \nvarious configurations and environmental conditions. A detailed description can be found in:\n\n- 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\", 2025.\n- Maksymilian Wojnar, Wojciech Ci\u0119\u017cobka, Artur Tomaszewski, Piotr Cho\u0142da, 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.\n\n## Features\n\n- **Simulation of C-SR**: You can simulate the C-SR performance of an 802.11 network, including the effects of hidden \nnodes, variable transmission power, node positions, and modulation and coding schemes (MCS). Calculate the aggregated \neffective data rate.\n- **TGax channel model**: The simulator incorporates the TGax channel model for realistic simulation in enterprise scenarios. The \nsimulator also supports the effects of wall attenuation and random noise in the environment.\n- **JAX JIT compilation**: The simulator is written in JAX, which enables just-in-time (JIT) compilation and hardware acceleration.\n- **Reproducibility**: The simulator uses JAX's pseudo random number generator (PRNG) to generate random numbers. This ensures that the\nsimulator is fully reproducible and you will get the same results for the same input parameters.\n\n## Repository Structure\n\nThe repository is structured as follows:\n\n- `mapc_sim/`: Main package containing the simulator.\n - `constants.py`: Physical and MAC layer constants used in the simulator.\n - `sim.py`: Main simulator code.\n - `utils.py`: Utility functions, including the TGax channel model.\n- `test/`: Unit tests and benchmarking scripts.\n\n## Installation\n\nThe package can be installed using pip:\n\n```bash\npip install mapc-sim\n```\n\n## Usage\n\nThe main functionality is provided by the `network_data_rate` function in `mapc_sim/sim.py`. This function calculates \nthe effective data rate for a given network configuration. Example usage:\n\n```python\nimport jax\nimport jax.numpy as jnp\nfrom mapc_sim.sim import network_data_rate\n\n# Random number generator key\nkey = jax.random.PRNGKey(42)\n\n# Transmission matrix - 1 if node i transmits to node j, 0 otherwise\ntx = jnp.zeros((n_nodes, n_nodes))\ntx = tx.at[i_0, j_0].set(1)\ntx = tx.at[i_1, j_1].set(1)\n...\ntx = tx.at[i_n, j_n].set(1)\n\n# Node positions\npos = jnp.array([\n [x_0, y_0],\n [x_1, y_1],\n ...\n [x_n, y_n],\n])\n\n# MCS values of transmitting nodes\nmcs = jnp.array([mcs_0, mcs_1, ..., mcs_n], dtype=int)\n\n# Transmission power of transmitting nodes\ntx_power = jnp.array([tx_power_0, tx_power_1, ..., tx_power_n])\n\n# Standard deviation of the white Gaussian noise\nsigma = 2.\n\n# Walls matrix - 1 if there is a wall between node k and node l, 0 otherwise\nwalls = jnp.zeros((n_nodes, n_nodes))\nwalls = walls.at[k_0, l_0].set(1)\nwalls = walls.at[k_1, l_1].set(1)\n...\nwalls = walls.at[k_m, l_m].set(1)\n\n# Calculate the effective data rate with the simulator\ndata_rate = network_data_rate(key, tx, pos, mcs, tx_power, sigma, walls)\n```\n\nFor more detailed examples, refer to the test cases in `test/test_sim.py`.\n\n### JAX JIT Compilation\n\nThe simulator is written in JAX, which enables just-in-time (JIT) compilation and hardware acceleration. \nThe use of JIT is strongly recommended as it can improve the performance of the simulator by orders of magnitude.\nTo enable JIT, apply the `jax.jit` transformation on the simulator function:\n \n```python\nimport jax\nfrom mapc_sim.sim import network_data_rate\n\n# Define your network configuration\n# ...\n\nnetwork_data_rate_jit = jax.jit(network_data_rate)\ndata_rate = network_data_rate_jit(key, tx, pos, mcs, tx_power, sigma, walls)\n```\n\nAs the `jax.jit` transformation can be applied to any function, you can also use it to JIT-compile closures. \nFor example, you can JIT-compile the `network_data_rate` function with a fixed network configuration as follows:\n\n```python\nfrom functools import partial\n\nimport jax\nfrom mapc_sim.sim import network_data_rate\n\npos = ...\nwalls = ...\n\nnetwork_data_rate_jit = jax.jit(partial(\n network_data_rate,\n pos=pos,\n walls=walls,\n))\n\n# Define the remaining values\n# ...\n\ndata_rate = network_data_rate_jit(key=key, tx=tx, mcs=mcs, tx_power=tx_power, sigma=sigma)\n```\n\n### Reproducibility\n\nThe simulator uses JAX's PRNG. This ensures that the simulator is fully reproducible. However, the same key should \nbe used at most once for each simulation so that the results are not correlated. For example, you can generate a new \nkey and split it into two keys in each step of a simulation:\n\n```python\nimport jax\nfrom mapc_sim.sim import network_data_rate\n\n# Define your network configuration\n# ...\n\nkey = jax.random.PRNGKey(42)\n\nfor _ in range(n):\n # Generate two new keys, one for the current step and one for the next splits\n key, subkey = jax.random.split(key)\n data_rate = network_data_rate(subkey, tx, pos, mcs, tx_power, sigma, walls)\n```\n\n### 64-bit Floating Point Precision\n\nIf you want to use 64-bit floating point precision, you can set the appropriate environment variable before running\nyour script:\n\n```bash\nexport JAX_ENABLE_X64=\"True\n```\n\nAlternatively, you can set the environment variable in your Python script:\n\n```python\nimport os\nos.environ[\"JAX_ENABLE_X64\"] = \"True\"\n```\n\n## Testing and Benchmarking\n\nRun the unit tests to ensure everything is working correctly:\n\n```bash\npython -m unittest\n```\n\nYou can benchmark the performance of the simulator using `test/sim_benchmark.py`.\n\n## Additional Notes\n\n- The simulator is written in JAX, an autodiff library for Python. It may require additional dependencies or \nconfigurations to run properly, especially with hardware acceleration. For more information on JAX, please refer \nto the official [JAX repository](https://jax.readthedocs.io/en/latest/).\n\n## How to reference `mapc-sim`?\n\nIf you use this repository or tool in your research, please cite the following paper:\n\n```\n@article{wojnar2025coordinated,\n author={Wojnar, Maksymilian and Ci\u0119\u017cobka, Wojciech and Tomaszewski, Artur and Cho\u0142da, 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},\n title={{Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks}}, \n year={2025},\n}\n```\n\nFor a detailed description of the tool, you may also refer to:\n\n```\n@article{wojnar2025ieee,\n author={Wojnar, Maksymilian and Ciezobka, Wojciech and Kosek-Szott, Katarzyna and Rusek, Krzysztof and Szott, Szymon and Nunez, David and Bellalta, Boris},\n title={{IEEE 802.11bn Multi-AP Coordinated Spatial Reuse with Hierarchical Multi-Armed Bandits}},\n year={2025}, \n}\n```\n",
"bugtrack_url": null,
"license": "Creative Commons Legal Code\n \n CC0 1.0 Universal\n \n CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE\n LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN\n ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS\n INFORMATION ON AN \"AS-IS\" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES\n REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS\n PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM\n THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED\n HEREUNDER.\n \n Statement of Purpose\n \n The laws of most jurisdictions throughout the world automatically confer\n exclusive Copyright and Related Rights (defined below) upon the creator\n and subsequent owner(s) (each and all, an \"owner\") of an original work of\n authorship and/or a database (each, a \"Work\").\n \n Certain owners wish to permanently relinquish those rights to a Work for\n the purpose of contributing to a commons of creative, cultural and\n scientific works (\"Commons\") that the public can reliably and without fear\n of later claims of infringement build upon, modify, incorporate in other\n works, reuse and redistribute as freely as possible in any form whatsoever\n and for any purposes, including without limitation commercial purposes.\n These owners may contribute to the Commons to promote the ideal of a free\n culture and the further production of creative, cultural and scientific\n works, or to gain reputation or greater distribution for their Work in\n part through the use and efforts of others.\n \n For these and/or other purposes and motivations, and without any\n expectation of additional consideration or compensation, the person\n associating CC0 with a Work (the \"Affirmer\"), to the extent that he or she\n is an owner of Copyright and Related Rights in the Work, voluntarily\n elects to apply CC0 to the Work and publicly distribute the Work under its\n terms, with knowledge of his or her Copyright and Related Rights in the\n Work and the meaning and intended legal effect of CC0 on those rights.\n \n 1. Copyright and Related Rights. A Work made available under CC0 may be\n protected by copyright and related or neighboring rights (\"Copyright and\n Related Rights\"). Copyright and Related Rights include, but are not\n limited to, the following:\n \n i. the right to reproduce, adapt, distribute, perform, display,\n communicate, and translate a Work;\n ii. moral rights retained by the original author(s) and/or performer(s);\n iii. publicity and privacy rights pertaining to a person's image or\n likeness depicted in a Work;\n iv. rights protecting against unfair competition in regards to a Work,\n subject to the limitations in paragraph 4(a), below;\n v. rights protecting the extraction, dissemination, use and reuse of data\n in a Work;\n vi. database rights (such as those arising under Directive 96/9/EC of the\n European Parliament and of the Council of 11 March 1996 on the legal\n protection of databases, and under any national implementation\n thereof, including any amended or successor version of such\n directive); and\n vii. other similar, equivalent or corresponding rights throughout the\n world based on applicable law or treaty, and any national\n implementations thereof.\n \n 2. Waiver. To the greatest extent permitted by, but not in contravention\n of, applicable law, Affirmer hereby overtly, fully, permanently,\n irrevocably and unconditionally waives, abandons, and surrenders all of\n Affirmer's Copyright and Related Rights and associated claims and causes\n of action, whether now known or unknown (including existing as well as\n future claims and causes of action), in the Work (i) in all territories\n worldwide, (ii) for the maximum duration provided by applicable law or\n treaty (including future time extensions), (iii) in any current or future\n medium and for any number of copies, and (iv) for any purpose whatsoever,\n including without limitation commercial, advertising or promotional\n purposes (the \"Waiver\"). 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