% Readme file:
% Created by Associate Professor / Mohamed Hassan Essai Ali,Egypt.
%on Thursday, 27/5/2021.
%for use with MATLAB R2019.b and its related Neural Networks Toolbox
%% 1 - 
%Open script m-file with title "TrainingdataGeneration", 
%This script is created to generate training and validation data for the
%deep learning model in a single-user OFDM system. 
% Go to line 32 for  OFDM system parameters, and specify the following
% parameters:
% Number of subcarriers
%NumSC = 64; 
% Number of pilot subcarriers 
%NumPilot = 4; 
% Then go to line 40 for Channel generation
%Number of pathes
%NumPath = 24;
% The length of the cyclic prefix
%LengthCP = 16; 
% Choose channel modle by adding or removing "%" before line 46 or 49.
%Finally, go to line 136 to save training data, validation data, 
%and simulation parameteers.
%for  example the following three lines for (Numscr=64, pilots=4, 
%and cyclick prefix length=16) 
    %save('TrainingData_sc64_pil4_cp16.mat','XTrain','YTrain','NumOFDMsym','NumSC','Label');
    %save('ValidationData_sc64_pil4_cp16.mat','XValid','YValid');
    %save('SimParameters_sc64_pil4_cp16.mat','NumPilotSym','NumDataSym','NumSC','idxSC','h','LengthCP','FixedPilot','Mod_Constellation','Label'); 
    
%%
%2-
%Open script m-file with title "TrainDNN"
% Go to line 10 for  loading training and validation data
%for  example the following three lines for (Numscr=64, pilots=4, 
%and cyclick prefix length=16) 
    %load('TrainingData_sc64_pil4_cp16.mat');
    %load('ValidationData_sc64_pil4_cp16.mat');
% Go to line 15 for define the training parameters
% Go to line 23 for Forming BiLSTM or LSTM DNN layers
% Go to line 31 to choose required loss function
% Go to line 70 for Saving the trained neural networks.

%%
%3-
%%Open script m-file with title "Accuracyforall"
% This script calculates the symbol error rate (SER) based on deep learning (DL), 
%   least square (LS) and minimum mean square error (MMSE).
% Go to line 13 for loading common parameters and the trained NN for your
% case study.