mapc-sim


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SummaryIEEE 802.11 MAPC (C-SR) simulator
upload_time2024-01-29 14:35:35
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requires_python>=3.9
licenseCreative Commons Legal Code CC0 1.0 Universal CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED HEREUNDER. Statement of Purpose The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and Related Rights (defined below) upon the creator and subsequent owner(s) (each and all, an "owner") of an original work of authorship and/or a database (each, a "Work"). Certain owners wish to permanently relinquish those rights to a Work for the purpose of contributing to a commons of creative, cultural and scientific works ("Commons") that the public can reliably and without fear of later claims of infringement build upon, modify, incorporate in other works, reuse and redistribute as freely as possible in any form whatsoever and for any purposes, including without limitation commercial purposes. These owners may contribute to the Commons to promote the ideal of a free culture and the further production of creative, cultural and scientific works, or to gain reputation or greater distribution for their Work in part through the use and efforts of others. 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keywords 802.11 coordinated spatial reuse multi-access point coordination simulator
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # 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", $JOURNAL_NAME_TODO, 2024. [[TODO_PREPRINT_INSERT](https://github.com/ml4wifi-devs/mapc-mab/tree/main), [TODO_PUBLICATION_INSERT](https://github.com/ml4wifi-devs/mapc-mab/tree/main)]

## 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`?

```
TODO
```

            

Raw data

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    "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>",
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    "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\", $JOURNAL_NAME_TODO, 2024. [[TODO_PREPRINT_INSERT](https://github.com/ml4wifi-devs/mapc-mab/tree/main), [TODO_PUBLICATION_INSERT](https://github.com/ml4wifi-devs/mapc-mab/tree/main)]\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\n```\nTODO\n```\n",
    "bugtrack_url": null,
    "license": "Creative Commons Legal Code  CC0 1.0 Universal  CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS INFORMATION ON AN \"AS-IS\" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED HEREUNDER.  Statement of Purpose  The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and Related Rights (defined below) upon the creator and subsequent owner(s) (each and all, an \"owner\") of an original work of authorship and/or a database (each, a \"Work\").  Certain owners wish to permanently relinquish those rights to a Work for the purpose of contributing to a commons of creative, cultural and scientific works (\"Commons\") that the public can reliably and without fear of later claims of infringement build upon, modify, incorporate in other works, reuse and redistribute as freely as possible in any form whatsoever and for any purposes, including without limitation commercial purposes. These owners may contribute to the Commons to promote the ideal of a free culture and the further production of creative, cultural and scientific works, or to gain reputation or greater distribution for their Work in part through the use and efforts of others.  For these and/or other purposes and motivations, and without any expectation of additional consideration or compensation, the person associating CC0 with a Work (the \"Affirmer\"), to the extent that he or she is an owner of Copyright and Related Rights in the Work, voluntarily elects to apply CC0 to the Work and publicly distribute the Work under its terms, with knowledge of his or her Copyright and Related Rights in the Work and the meaning and intended legal effect of CC0 on those rights.  1. Copyright and Related Rights. A Work made available under CC0 may be protected by copyright and related or neighboring rights (\"Copyright and Related Rights\"). Copyright and Related Rights include, but are not limited to, the following:  i. the right to reproduce, adapt, distribute, perform, display, communicate, and translate a Work; ii. moral rights retained by the original author(s) and/or performer(s); iii. publicity and privacy rights pertaining to a person's image or likeness depicted in a Work; iv. rights protecting against unfair competition in regards to a Work, subject to the limitations in paragraph 4(a), below; v. rights protecting the extraction, dissemination, use and reuse of data in a Work; vi. database rights (such as those arising under Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, and under any national implementation thereof, including any amended or successor version of such directive); and vii. other similar, equivalent or corresponding rights throughout the world based on applicable law or treaty, and any national implementations thereof.  2. Waiver. To the greatest extent permitted by, but not in contravention of, applicable law, Affirmer hereby overtly, fully, permanently, irrevocably and unconditionally waives, abandons, and surrenders all of Affirmer's Copyright and Related Rights and associated claims and causes of action, whether now known or unknown (including existing as well as future claims and causes of action), in the Work (i) in all territories worldwide, (ii) for the maximum duration provided by applicable law or treaty (including future time extensions), (iii) in any current or future medium and for any number of copies, and (iv) for any purpose whatsoever, including without limitation commercial, advertising or promotional purposes (the \"Waiver\"). Affirmer makes the Waiver for the benefit of each member of the public at large and to the detriment of Affirmer's heirs and successors, fully intending that such Waiver shall not be subject to revocation, rescission, cancellation, termination, or any other legal or equitable action to disrupt the quiet enjoyment of the Work by the public as contemplated by Affirmer's express Statement of Purpose.  3. Public License Fallback. Should any part of the Waiver for any reason be judged legally invalid or ineffective under applicable law, then the Waiver shall be preserved to the maximum extent permitted taking into account Affirmer's express Statement of Purpose. In addition, to the extent the Waiver is so judged Affirmer hereby grants to each affected person a royalty-free, non transferable, non sublicensable, non exclusive, irrevocable and unconditional license to exercise Affirmer's Copyright and Related Rights in the Work (i) in all territories worldwide, (ii) for the maximum duration provided by applicable law or treaty (including future time extensions), (iii) in any current or future medium and for any number of copies, and (iv) for any purpose whatsoever, including without limitation commercial, advertising or promotional purposes (the \"License\"). The License shall be deemed effective as of the date CC0 was applied by Affirmer to the Work. Should any part of the License for any reason be judged legally invalid or ineffective under applicable law, such partial invalidity or ineffectiveness shall not invalidate the remainder of the License, and in such case Affirmer hereby affirms that he or she will not (i) exercise any of his or her remaining Copyright and Related Rights in the Work or (ii) assert any associated claims and causes of action with respect to the Work, in either case contrary to Affirmer's express Statement of Purpose.  4. Limitations and Disclaimers.  a. No trademark or patent rights held by Affirmer are waived, abandoned, surrendered, licensed or otherwise affected by this document. b. Affirmer offers the Work as-is and makes no representations or warranties of any kind concerning the Work, express, implied, statutory or otherwise, including without limitation warranties of title, merchantability, fitness for a particular purpose, non infringement, or the absence of latent or other defects, accuracy, or the present or absence of errors, whether or not discoverable, all to the greatest extent permissible under applicable law. c. Affirmer disclaims responsibility for clearing rights of other persons that may apply to the Work or any use thereof, including without limitation any person's Copyright and Related Rights in the Work. Further, Affirmer disclaims responsibility for obtaining any necessary consents, permissions or other rights required for any use of the Work. d. Affirmer understands and acknowledges that Creative Commons is not a party to this document and has no duty or obligation with respect to this CC0 or use of the Work. ",
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