| Name | mapc-optimal JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Optimal solution of the MAPC (C-SR) problem for IEEE 802.11 networks |
| upload_time | 2024-11-03 12:28:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| 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"). 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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. 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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. 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| keywords |
802.11
coordinated spatial reuse
multi-access point coordination
optimization
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
# 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:
- TODO
## 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](...).
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, 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`?
```
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>, Artur Tomaszewski <artur.tomaszewski@pg.edu.pl>, Piotr Cho\u0142da <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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"description": "# Optimal solution for IEEE 802.11 MAPC Coordinated Spatial Reuse (C-SR) problem\n\n`mapc-optimal` is a tool for finding the optimal solution of the Multi-Access Point Coordination (MAPC) scheduling \nproblem with coordinated spatial reuse (C-SR) for IEEE 802.11 networks. It provides a mixed-integer linear programming\n(MILP) solution to find the upper bound on network performance. A detailed description can be found in:\n\n- TODO\n\n## Features\n\n- **Calculation of optimal scheduling**: Calculate the best transmission configurations and the corresponding time \n division that enhance the network performance.\n- **Two optimization criteria**: Find the optimal solution for two optimization criteria: maximizing the sum of the \n throughput of all nodes in the network and maximizing the minimum throughput of all nodes in the network.\n- **Modulation and coding scheme (MCS) selection**: Select the optimal MCS for each transmission.\n- **Transmission power selection**: Set the appropriate transmission power to maximize network performance.\n- **Versatile network configuration**: Define network settings by specifying network nodes, available MCSs, \n and transmission power levels.\n\n## Installation\n\nThe package can be installed using pip:\n\n```bash\npip install mapc-optimal\n```\n\n## Usage\n\nThe main functionality is provided by the `Solver` class in `mapc_optimal/solver.py`. This class manages the process of \nfinding the optimal solution. Example usage:\n\n```python\nfrom mapc_optimal import Solver\n\n# Define your network\n# ...\n\nsolver = Solver(stations, access_points)\nconfigurations, rate = solver(path_loss)\n```\n\nwhere `stations` and `access_points` are lists of numbers representing the stations and access points (APs) in the \nnetwork, respectively. The `path_loss` is an $n \\times n$ matrix representing the path loss between each pair of nodes \nin the network. The solver returns calculated configurations and the total throughput of the network. The `Solver` \nclass can be further configured by passing additional arguments to the constructor. The full list of arguments can \nbe found in the [documentation](...). \n\nAdditionally, the solver can return a list of the pricing objective values for each iteration. It can be useful to \ncheck if the solver has converged. To do so, set the `return_objective` argument to `True` when calling the solver.\n\n```python\nconfigurations, rate, objectives = solver(path_loss, return_objective=True)\n```\n\nFor a more detailed example, refer to the test case in `test/test_solver.py`.\n\n**Note:** The underlying MILP solver can significantly affect the performance of the tool. By default, the solver \nuses the `CBC` solver from the `PuLP` package. However, we recommend using a better solver, such as `CPLEX`.\n\n## Repository Structure\n\nThe repository is structured as follows:\n\n- `mapc_optimal/`: The main package of the tool.\n - `constants.py`: Default values of the parameters used in the solver.\n - `main.py`: The formulation of the main problem solving the selection and division of configurations.\n - `pricing.py`: The pricing algorithm used to propose new configurations for the main problem.\n - `solver.py`: The solver class coordinating the overall process of finding the optimal solution. It initializes the \n solver, sets up the network configuration, and manages the iterations.\n - `utils.py`: Utility functions, including the function for calculation of the path loss from node positions using \n the TGax channel model.\n- `test/`: Unit tests with example usage of the tool.\n\n## How to reference `mapc-optimal`?\n\n```\nTODO\n```\n",
"bugtrack_url": null,
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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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