@@ -12,7 +12,6 @@ Finally, the code for an experimental validation testbed using NOMA with softwar

*[GNURadio](#folder_gnuradio) : GNURadio flowgraphs used in our experimental setup

*[gr-NOMA](#folder_gr-NOMA) : Custom-made blocks, using C++, used in the flowgraphs

*[LaTeX](#folder_Latex) : LaTeX sources for our report

*[Matlab](#folder_Matlab) : Matlab code used to generate the channel parameters used in simulations

*[Python](#folder_Python) : Python scripts used for the simulations and optimization

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@@ -156,13 +155,6 @@ After sending those symbols, it just acts as a passthrough block. No symbol is d

**Warning:** The values in `syncFrames` MUST be != 0 (see sync_frames_receiver for details).

## <a name="folder_Latex"></a> LaTeX

The file `Généralisation N récepteurs.tex` contains the source code to compile in order to generate the PDF version of the paper.

The file `biblio.bib` contains the bibliographic elements used in the paper, and need to be compiled with `Généralisation N récepteurs.tex`.

## <a name="folder_Matlab"></a> Matlab

In order to use this code, the parameters from `BaseCodeMS.m` must be modified, especially the values of `N` (the noise of the canal) and `g` (the attenuation for each user).

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@@ -184,6 +176,12 @@ Contains measured BER (Bit Error Rate) for 3 users, and compares them with theor

Contains several functions to compare the theoretical BER with a statistical BER calculated using the Monte Carlo method.

##### Find\_Optimum.py

Contains the algorithm for searching for the optimum power distribution. A custom implementation is compared to an existing solution. It is based on scipy and on the file `Theorie_N_receptors.py`.

Its behavior is detailed in our report, and explanations on the matrices used in the optimization are given at the beginning of the file.

##### Model\_N\_receivers.py

Contains functions to calculate an error rate by following the ideal channel model with the Monte-Carlo method.

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@@ -196,11 +194,12 @@ Contains functions to calculate the theoretical error rate using the formula dev

Usage examples are given in `Comparaison_Modele_Theorie.py` and at the end of the file.

##### Find\_Optimum.py

##### Theory\_N\_receivers\_auto\_develop.py

Contains the algorithm for searching for the optimum power distribution. A custom implementation is compared to an existing solution. It is based on scipy and on the file `Theorie_N_receptors.py`.

Contains functions to compute automatically the theoretical formula for a given user, regardless of the number of users. The output is formated as a LaTeX source.

A usage example is given at the end of the file.

Its behavior is detailed in our report, and explanations on the matrices used in the optimization are given at the beginning of the file.