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
- 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.
The package can be installed using pip:
pip install mapc-optimal
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:
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 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.
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
.
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.
TODO