add different Modulation, update code

Project.toml

@@ -15,6 +15,7 @@ DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 DigitalComm = "a7b11256-881b-11e9-3aff-ff4a76f1bfae"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
+Flatten = "4c728ea3-d9ee-5c9a-9642-b6f7d7dc04fa"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Infiltrator = "5903a43b-9cc3-4c30-8d17-598619ec4e9b"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"

README.md

@@ -1,20 +1,31 @@
-# RiFyFi
+# RiFyFi.jl 
 
 [![Build Status](https://github.com/achilletIrisa/RiFyFi.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/achilletIrisa/RiFyFi.jl/actions/workflows/CI.yml?query=branch%3Amain)
 
 [introduction.pdf](https://github.com/JuliaTelecom/Rifyfi.jl/files/13974266/introduction.pdf)
 
 RiFyFi is a framework for Radio Frequency Fingerprint Identification. RFF is a unique signature created in the emitter transmission chain by the hardware impairments. These impairments may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. RiFyFi is introduced to propose a framework combine with a virtual database to explore the RFF identification. Different transmission scenarios are modeled such as the data type (being a preamble or a payload) and the data size. 
 
+RiFyFi is composed of different subpackage 
+- RiFyFi_VDG : The Virtual Database Generator
+- RiFyFi_IdF : Package for training and testing network
+- Augmentation : Package used for data augmentation
+- Results : Package to create some confusion matrix or F1 score evolution in function of time.
 
+## Protocol to use RiFyFi with Julia 
 
+Insatall Julia (here develop with 1.8.5)
 Create a folders
-Download or clone the project with git clone 
-Going in Rifyfi.jl
-Open Julia terminal
+Download or clone the project with: git clone https://github.com/JuliaTelecom/Rifyfi.jl
+Going in Rifyfi.jl folders
+Open a Julia terminal
 Activate the project ] activate .
 Instantiate the project with ] instantiate 
 
+Then you can you the script script_example.jl, in the Julia Terminal :
+include("src/script_exemple.jl")
+
+
 Then you can use RiFyFi in two manners: 
 -   Creating random parameter values for impairments 
 -   Define the value of the impairments parameters with scenario file 

src/Results/src/Prod_ConfusionMatrix.jl

@@ -53,37 +53,39 @@ end
 
 
 """ Fonction qui plot la matrice de confusion en format csv \n 
-    filename = .pkl file 
+    filename = .pkl
+     file 
     txs,rxs,days,equalized = param for test with 2 for equalized
     rxsnn,daysnn = param to choose to CNN 
     ChunSize = 256 
     dataAug = "sans" or "OfflineV1" 
 """
 
-function Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson="")
+function Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson="",Param_Data_test=Param_Data)
     if Param_Network.Train_args.use_cuda ==true 
         hardware1 = "GPU"
     else 
         hardware1 ="CPU"
     end 
     if savepathbson == ""
         if Param_Data.Augmentation_Value.augmentationType == "No_channel"
-            savepathbson = "run/Synth/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)/$(hardware1)"
+            savepath_model = "run/Synth/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)/$(hardware1)"
         else 
-            savepathbson = "run/Synth/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)/$(hardware1)"
+            savepath_model = "run/Synth/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)/$(hardware1)"
         end 
     end 
 
     allAcc = Float64[]
 
-    res =RiFyFi_IdF.loadCNN("$(savepathbson)/model_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr).bson")
+    res = RiFyFi_IdF.loadCNN("$(savepath_model)/model_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr)_$(Param_Data.Modulation).bson")
+    #res =RiFyFi_IdF.loadCNN("$(savepath_model)/model_new.bson")
 
     model = res.model
     testmode!(model, true)  # We are in test mode, with no dropout 
     (moy,std_val) = (nothing,nothing)
     allAccuracy = Float64[]
 
-    (_,_,X_test,Y_test) =loadCSV_Synthetic(Param_Data) 
+    (_,_,X_test,Y_test) =loadCSV_Synthetic(Param_Data_test) 
 
 
     if Param_Network.Train_args.use_cuda
@@ -98,13 +100,13 @@ function Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson="")
     confMatrix = confusionMatrix(l̂,l,Param_Data.nbTx)
     plt = plotConfusionMatrix(confMatrix )
 
-    savepath ="Results/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)"
-    !ispath(savepath) && mkpath(savepath)
+    savepath_result ="Results/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)"
+    !ispath(savepath_result) && mkpath(savepath_result)
         Temp=zeros(1,Param_Data.nbTx)
         if Param_Data.Augmentation_Value.augmentationType == "No_channel"
-            file="$(savepath)/confMatrix_$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)_seed_$(Param_Network.Seed_Network).csv"
+            file="$(savepath_result)/confMatrix_$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)_seed_$(Param_Network.Seed_Network).csv"
         else 
-            file="$(savepath)/confMatrix_$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)_seed_$(Param_Network.Seed_Network).csv"
+            file="$(savepath_result)/confMatrix_$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)_seed_$(Param_Network.Seed_Network).csv"
         end 
         open(file,"w") do io
             for i in 0:size(confMatrix,1)-1

src/Results/src/Results.jl

@@ -30,7 +30,7 @@ using .RiFyFi_VDG
 include("../../RiFyFi_IdF/src/RiFyFi_IdF.jl")
 using .RiFyFi_IdF
 
-function main(Param_Data,Param_Network,Type_Resuts,Table_Seed_Network,savepathbson)
+function main(Param_Data,Param_Network,Type_Resuts,Table_Seed_Network,savepathbson,Param_Data_test)
 
     if Type_Resuts == "F1_score"
         F1_score_Synth(Param_Data,Param_Network,Table_Seed_Network,savepathbson)
@@ -39,7 +39,7 @@ function main(Param_Data,Param_Network,Type_Resuts,Table_Seed_Network,savepathbs
         Compute_mean(Param_Data,Param_Network,nameSituation,Table_Seed_Network)
 
     elseif Type_Resuts == "Confusion_Matrix"
-        Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson )
+        Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson,Param_Data_test )
 
     end 
 
@@ -55,9 +55,9 @@ function F1_score_Synth(Param_Data,Param_Network,Table_Seed_Network,savepathbson
     end 
     if savepathbson == ""
         if Param_Data.Augmentation_Value.augmentationType == "No_channel"
-            savepathbson = "run/Synth/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)/$(hardware1)"
+            savepathbson = "run/Synth/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)/$(hardware1)"
         else 
-            savepathbson = "run/Synth/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)/$(hardware1)"
+            savepathbson = "run/Synth/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)/$(hardware1)"
         end 
     end 
     if Param_Data.Augmentation_Value.augmentationType == "No_channel"
@@ -85,7 +85,7 @@ function F1_score_Synth(Param_Data,Param_Network,Table_Seed_Network,savepathbson
     for i =1 :1: size(Table_Seed_Network,1)
         Param_Network.Seed_Network= Table_Seed_Network[i]
         name = savename
-        Scenario ="$(savepathbson)/F1_Score_$(hardware1)_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr).csv"
+        Scenario ="$(savepathbson)/F1_Score_$(hardware1)_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr)_$(Param_Data.Modulation).csv"
         delim=';'
         nameBase = split(Scenario,".")[1]
         #  ----------------------------------------------------
@@ -104,7 +104,7 @@ function F1_score_Synth(Param_Data,Param_Network,Table_Seed_Network,savepathbson
         @pgf push!(a, LegendEntry("Seed $(i)")) # Train)
 
     end
-    pgfsave("$(savepathbson)/F1_Score_$(savename).tex",a)
+    pgfsave("$(savepathbson)/F1_Score_$(savename)_$(Param_Data.Modulation).tex",a)
 
 end
 
@@ -113,7 +113,7 @@ end
 
 function Compute_mean(Param_Data,Param_Network,nameSituation,Table_Seed_Network)    
 
-    savepath = "../My_RFFI_Syst/run/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)"
+    savepath = "run/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)"
     if Param_Network.Train_args.use_cuda ==true 
         hardware1 = "GPU"
     else 

src/RiFyFi.jl

@@ -30,6 +30,13 @@ using .RiFyFi_VDG
 include("RiFyFi_IdF/src/RiFyFi_IdF.jl")
 using .RiFyFi_IdF
 
+include("Augmentation/src/Augmentation.jl")
+using .Augmentation
+
+include("Results/src/Results.jl")
+using .Results
+
+
 # ----------------------------------------------------
 # --- Loading utility functions
 # ---------------------------------------------------- 
@@ -60,22 +67,22 @@ function main(Param_Data,Param_Network)
 
     (dataTrain,dataTest) = init(Param_Data,Param_Network)
     if Param_Data.Augmentation_Value.augmentationType == "No_channel"
-        savepath = "run/Synth/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)/$(hardware)"
+        savepath_model = "run/Synth/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)/$(hardware)"
     else 
-        savepath = "run/Synth/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)/$(hardware)"
+        savepath_model = "run/Synth/$(Param_Data.Modulation)/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)_$(Param_Data.Augmentation_Value.Channel)_$(Param_Data.Augmentation_Value.Channel_Test)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)/$(hardware)"
     end 
-    !ispath(savepath) && mkpath(savepath)
-    @infiltrate
-    (model,trainLoss,trainAcc,testLoss,testAcc,args) = customTrain!(dataTrain,dataTest,savepath,Param_Network)
+    !ispath(savepath_model) && mkpath(savepath_model)
+
+    (model,trainLoss,trainAcc,testLoss,testAcc,args) = customTrain!(dataTrain,dataTest,savepath_model,Param_Network,Param_Data.Modulation)
     # ----------------------------------------------------
     # --- Saving model 
     # ---------------------------------------------------- 
-    modelpath = joinpath(savepath, "model_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr).bson") 
+    modelpath = joinpath(savepath_model, "model_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr)_$(Param_Data.Modulation).bson") 
     nbEpochs = args.epochs
     BSON.@save modelpath model nbEpochs trainLoss trainAcc testLoss testAcc args 
     testAugmented_acc =0
     testAugmented_loss=0    
-    return (savepath,model,trainLoss,trainAcc,testLoss,testAcc,testAugmented_loss,testAugmented_acc) 
+    return (savepath_model,model,trainLoss,trainAcc,testLoss,testAcc,testAugmented_loss,testAugmented_acc) 
 end
 
 

src/RiFyFi_IdF/src/RiFyFi_IdF.jl

@@ -266,13 +266,16 @@ end
 
 function loadCNN(cnnPath)
     # --- Loading data 
+   # dict = BSON.parse(cnnPath)
+
     dict = BSON.load(cnnPath,@__MODULE__) 
     # --- Exporting variables 
     model     = dict[:model]
     testAcc   = dict[:testAcc]
     trainAcc  = dict[:trainAcc]
     testLoss  = dict[:testLoss]
     trainLoss = dict[:trainLoss]
+
     #=moy = 0 
         std = 1
     try 
@@ -318,6 +321,8 @@ export Network_struct
 export Args
 export Args_construct
 
+
+
 end 
 
 

src/RiFyFi_IdF/src/customTrain.jl

@@ -69,7 +69,7 @@ end
 
 
 
-function customTrain!(dataTrain,dataTest,savepath,Param_Network)
+function customTrain!(dataTrain,dataTest,savepath_model,Param_Network,Modulation)
     # ----------------------------------------------------
     # --- CPU or GPU
     # ---------------------------------------------------- 
@@ -151,7 +151,7 @@ function customTrain!(dataTrain,dataTest,savepath,Param_Network)
     # Timings are calculated per eopch, we want complete time 
     Param_Network.Train_args.timings = cumsum(Param_Network.Train_args.timings;dims=1)
     # Write timings and accuracy in a file 
-    open("$(savepath)/F1_Score_$(DeviceName)_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr).csv","w") do io 
+    open("$(savepath_model)/F1_Score_$(DeviceName)_seed_$(Param_Network.Seed_Network)_dr$(Param_Network.Train_args.dr)_$(Modulation).csv","w") do io 
         arr = [Param_Network.Train_args.timings[1:epoch] trainf1 testf1 trainLoss testLoss]
         writedlm(io,round.(arr;digits=4),';')
     end