update script and readme

JuliaTelecom · Jan 25, 2024 · 20fd42c · 20fd42c
1 parent 7fff641
commit 20fd42c
Show file tree

Hide file tree

Showing 10 changed files with 99 additions and 167 deletions.
diff --git a/README.md b/README.md
@@ -7,4 +7,17 @@
 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. 
 
 
-The framework will be access here soon.
+
+Create a folders
+Download or clone the project with git clone 
+Going in Rifyfi.jl
+Open Julia terminal
+Activate the project ] activate .
+Instantiate the project with ] instantiate 
+
+Then you can use RiFyFi in two manners: 
+-   Creating random parameter values for impairments 
+-   Define the value of the impairments parameters with scenario file 
+
+
+
diff --git a/src/Augmentation/src/Augmentation.jl b/src/Augmentation/src/Augmentation.jl
@@ -5,7 +5,7 @@ using Random
 using DigitalComm
 using DSP
 using Distributed
-
+using Infiltrator
 include("Struct_Augmentation.jl")
 export Data_Augmented
 export Data_Augmented_construct
@@ -47,7 +47,7 @@ function Add_diff_Channel_train_test(bigMat, bigLabels, N, channel, ChunkSize, p
             subSigAugmented = zeros(ComplexF32,Int(ChunkSize*burstSizeTemp))
             subSig = @views sig[  Int((iN-1)*ChunkSize*burstSize) .+ (1:Int(ChunkSize*burstSizeTemp))]
             for j = 1 : 1 : pourcentAugment  
-                Random.seed!(seed * iR + j )    
+                Random.seed!(seed * iR + j )   
                 #--- Data augmentation 
                 # First get unique impairment model
                 σ   = choose(snrRange)

diff --git a/src/Augmentation/src/Struct_Augmentation.jl b/src/Augmentation/src/Struct_Augmentation.jl
@@ -1,10 +1,10 @@
 Base.@kwdef mutable struct Data_Augmented
-    augmentationType::String = "sans"
+    augmentationType::String = "No_channel"
     Channel::String = "etu"
     Channel_Test::String = "etu"
     nb_Augment::Int = 1
     seed_channel::Int = 12
-    seed_channel_test::Int = 9999s99999 * 100000000
+    seed_channel_test::Int = 999999999 * 100000000
     burstSize::Int =64
 end
 
@@ -14,7 +14,7 @@ function Data_Augmented_construct(; kwargs...)
     if haskey(kwargs, :augmentationType)
         augmentationType = kwargs[:augmentationType]
     else
-        augmentationType="sans"
+        augmentationType="No_channel"
     end
 
     if haskey(kwargs, :Channel)

diff --git a/src/Results/src/Prod_ConfusionMatrix.jl b/src/Results/src/Prod_ConfusionMatrix.jl
@@ -67,7 +67,7 @@ function Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson="")
         hardware1 ="CPU"
     end 
     if savepathbson == ""
-        if Param_Data.Augmentation_Value.augmentationType == "sans"
+        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)/$(hardware1)"
         else 
             savepathbson = "run/Synth/$(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)/$(hardware1)"
@@ -101,7 +101,7 @@ function Confusion_Matrix_CSV(Param_Data,Param_Network,savepathbson="")
     savepath ="Results/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.E)_$(Param_Data.S)"
     !ispath(savepath) && mkpath(savepath)
         Temp=zeros(1,Param_Data.nbTx)
-        if Param_Data.Augmentation_Value.augmentationType == "sans"
+        if Param_Data.Augmentation_Value.augmentationType == "No_channel"
             file="$(savepath)/confMatrix_$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)_seed_$(Param_Network.Seed_Network).csv"
         else 
             file="$(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"
@@ -123,7 +123,6 @@ function MainPlottingMatrix_Latex(file,E="E2",S="S1",C="C1",Network="AlexNet",RF
     header      = (["Tx\$_$i\$" for i = 1 : 1 : nbRadioTx])
     row_labels  = (["TxTrue\$_$i\$" for i = 1 : 1 : nbRadioTx])
     row_label_column_title = "\\backslashbox[15mm][]{\\footnotesize True}{\\footnotesize Guess}"
-       # file="run/ManySig/sans_$(nbRadioTx)_$(ChunkSize)_$(Network)/$(hardware)/confMatrix_$(E)_$(S)_$(C)_$(C_test)_$(RFF).csv"
         cellcolor_header = "blue!10!white"      
 
         m = latexConfusionMatrix(file;doSave=true,standalone=true,header,row_label_column_title,row_labels,cellcolor_header)

diff --git a/src/Results/src/Results.jl b/src/Results/src/Results.jl
@@ -54,13 +54,13 @@ function F1_score_Synth(Param_Data,Param_Network,Table_Seed_Network,savepathbson
         hardware1 ="CPU"
     end 
     if savepathbson == ""
-        if Param_Data.Augmentation_Value.augmentationType == "sans"
+        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)/$(hardware1)"
         else 
             savepathbson = "run/Synth/$(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)/$(hardware1)"
         end 
     end 
-    if Param_Data.Augmentation_Value.augmentationType == "sans"
+    if Param_Data.Augmentation_Value.augmentationType == "No_channel"
         savename ="$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)"
     else 
         savename ="$(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)"
@@ -119,7 +119,7 @@ function Compute_mean(Param_Data,Param_Network,nameSituation,Table_Seed_Network)
     else 
         hardware1 ="CPU"
     end 
-    if Param_Data.Augmentation_Value.augmentationType == "sans"
+    if Param_Data.Augmentation_Value.augmentationType == "No_channel"
         name= "$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.name)"
         else 
         name ="$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.name)_nbAugment_$(Param_Data.Augmentation_Value.nb_Augment)"

diff --git a/src/RiFyFi.jl b/src/RiFyFi.jl
@@ -47,7 +47,7 @@ export getAccuracy
 
 """ Function that drives the cnn and saves it in .bson\n
     Parameters : 
-    - Param_Data type Data_Synth ou Data_WiSig 
+    - Param_Data type Data_Synth  
     - Param_Network type of Network_struct 
 """
 function main(Param_Data,Param_Network)  
@@ -59,15 +59,13 @@ function main(Param_Data,Param_Network)
     end
 
     (dataTrain,dataTest) = init(Param_Data,Param_Network)
-    if Param_Data.name== "WiSig"
-        savepath = "run/WiSig/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)_$(Param_Network.Networkname)/$(Param_Data.txs)_$(Param_Data.rxs)/$(Param_Data.txs)_$(Param_Data.rxs)_$(Param_Data.days)_$(Param_Data.nbSignals)/$(hardware)"
-    elseif Param_Data.Augmentation_Value.augmentationType == "sans"
+    if Param_Data.Augmentation_Value.augmentationType == "No_channel"
         savepath = "run/Synth/$(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)"
     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)"
     end 
     !ispath(savepath) && mkpath(savepath)
-
+    @infiltrate
     (model,trainLoss,trainAcc,testLoss,testAcc,args) = customTrain!(dataTrain,dataTest,savepath,Param_Network)
     # ----------------------------------------------------
     # --- Saving model 
@@ -85,7 +83,7 @@ end
 
 """ Function that load Data in Matrix format and initialize the Network
     Parameters : 
-    - Param_Data type Data_Synth ou Data_WiSig 
+    - Param_Data type Data_Synth 
     - Param_Network type of Network_struct
 """
 function init(Param_Data,Param_Network)
@@ -94,11 +92,9 @@ function init(Param_Data,Param_Network)
     # ---------------------------------------------------- 
     @info "init"
     Random.seed!(Param_Network.Seed_Network)
-    if Param_Data.name== "WiSig"
-        (X_train,Y_train,X_test,Y_test)=loadCSV_WiSig(Param_Data)
-    else
-        (X_train,Y_train,X_test,Y_test)=loadCSV_Synthetic(Param_Data)
-    end 
+
+    (X_train,Y_train,X_test,Y_test)=loadCSV_Synthetic(Param_Data)
+
 
     # ----------------------------------------------------
     # --- Create datasets

diff --git a/src/RiFyFi_IdF/README.md b/src/RiFyFi_IdF/README.md
@@ -3,4 +3,28 @@
 [![Build Status](https://github.com/achillet/RiFyFi_IdF.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/achillet/RiFyFi_IdF.jl/actions/workflows/CI.yml?query=branch%3Amain)
 
 
-The Module RiFyFi_IdF defines function related to the classification/ Identification Framework particularly based on CNN
+The Module RiFyFi_IdF defines function related to the classification/ Identification Framework particularly based on CNN
+
+
+function initAlexNet(x)
+    - definition des différentes couche du reseaux
+    - définition de la crossentropy 
+    return (m,loss)
+end
+
+
+# customTrain.jl
+
+function customTrain!(nn,customLoss,dataTrain,dataTest,savepath;kws...)
+    - décision GPU ou CPU
+    - on charge le model sur le hardware choisi
+    - apprentissage : à chaque epoch on lance le timer et les données sont présentées au réseau. 
+    Un Chunk (séquence de 256 IQ) est présenté et passe dans le réseau, puis une autre jusqu'a atteindre batchsize. Lorsque le nombre de Chunk présenté atteint batchsize le réseau est mis à jour par backpropagation et on passe au batch suivant. Une fois que tout les batch ont été parcouru on a atteint une epoch, on va tester l'accuracy du reseau en Test et en Train et afficher les résultats. 
+    - Après chaque epoque le Timer est arrété et le score F1 est calculé par la fonction F1Score 
+return nn,trainLoss,trainAcc,testLoss,testAcc,args
+
+
+
+function F1Score(loader,model,device=cpu)
+    - calcul le F1 score global à partir des estimations de classe et les classes vraies.
+return F1
diff --git a/src/RiFyFi_VDG/src/RiFyFi_VDG.jl b/src/RiFyFi_VDG/src/RiFyFi_VDG.jl
@@ -16,6 +16,7 @@ using Statistics
 using CSV 
 using DelimitedFiles
 using DataFrames
+using Infiltrator
 include("../../Augmentation/src/Augmentation.jl")
 using .Augmentation
 # ----------------------------------------------------
@@ -60,8 +61,8 @@ function setSynthetiquecsv(Param_Data)
     (bigMatTrain,bigLabel_Train,bigMatTest,bigLabel_Test,X) = create_virtual_Database(Param_Data)
     bigLabels_Train = create_bigMat_Labels_Tx(bigLabel_Train)
     bigLabels_Test = create_bigMat_Labels_Tx(bigLabel_Test)
-
-    if Param_Data.Augmentation_Value.augmentationType == "sans"
+@infiltrate
+    if Param_Data.Augmentation_Value.augmentationType == "No_channel"
         savepath = "./CSV_Files/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)/$(Param_Data.E)_$(Param_Data.S)/$(Param_Data.E)_$(Param_Data.S)_$(Param_Data.C)_$(Param_Data.RFF)_$(Param_Data.nbSignals)_$(Param_Data.nameModel)"
     else 
         savepath = "./CSV_Files/$(Param_Data.Augmentation_Value.augmentationType)_$(Param_Data.nbTx)_$(Param_Data.Chunksize)/$(Param_Data.E)_$(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_$(Param_Data.Augmentation_Value.nb_Augment)"
@@ -266,7 +267,7 @@ function loadCSV_Synthetic(Param_Data)
     nbChunks=Int(nbRadioTx*nbSignaux)
     nbTrain = Int(round(pourcentTrain*nbChunks))
     nbTest = nbChunks - nbTrain
-    if augmentationType == "sans"
+    if augmentationType == "No_channel"
         savepath = "./CSV_Files/$(augmentationType)_$(nbRadioTx)_$(ChunkSize)/$(E)_$(S)/$(E)_$(S)_$(C)_$(RFF)_$(nbSignaux)_$(name)"    
     else  
         channel = Param_Data.Augmentation_Value.Channel

diff --git a/src/RiFyFi_VDG/src/Struct_data.jl b/src/RiFyFi_VDG/src/Struct_data.jl
@@ -129,102 +129,3 @@ function Data_Synth_construct(; kwargs...)
     end
 return Data_Synth(name,nameModel,nbTx,nbSignals,Chunksize,features,S,E,C,RFF,Normalisation,pourcentTrain,configuration,seed_data,seed_model,seed_dataTest,seed_modelTest,Augmentation_Value)
 end
-
-#=
-
-Base.@kwdef mutable struct Data_WiSig
-    File_name::String = "../My_WiSig_ManySig/pkl_wifi_ManySig/ManySig.pkl"
-    name::String = "WiSig"
-    nbTx::Int = 6
-    nbSignals::Int = 1000
-    Chunksize::Int = 256
-    features::String= "IQsamples"
-    txs = 1:6
-    rxs = 1
-    days = 1
-    equalized= 1
-    Normalisation::Bool = true
-    pourcentTrain::Float64 =0.9
-    Augmentation_Value::Data_Augmented = Data_Augmented() 
-end
-
-
-
-function Data_WiSig(; kwargs...)
-    if haskey(kwargs, :File_name)
-        File_name = kwargs[:File_name]
-    else
-        File_name="../My_WiSig_ManySig/pkl_wifi_ManySig/ManySig.pkl"
-    end
-
-    if haskey(kwargs, :name)
-        name = kwargs[:name]
-    else
-        name="WiSig"
-    end
-
-    if haskey(kwargs, :nbTx)
-        nbTx = kwargs[:nbTx]
-    else 
-        nbTx = 6
-    end
-
-    if haskey(kwargs, :nbSignals)
-        nbSignals = kwargs[:nbSignals]
-    else 
-        nbSignals = 1000
-    end
-
-    if haskey(kwargs, :Chunksize)
-        Chunksize = kwargs[:Chunksize]
-    else 
-        Chunksize = 256
-    end
-
-    if haskey(kwargs, :features)
-        features = kwargs[:features]
-    else 
-        features = "IQsamples"
-    end
-
-
-    if haskey(kwargs, :txs)
-        txs = kwargs[:txs]
-    else 
-        txs = 1:6
-    end
-    if haskey(kwargs, :rxs)
-        rxs = kwargs[:rxs]
-    else 
-        rxs = 1
-    end
-    if haskey(kwargs, :days)
-        days = kwargs[:days]
-    else 
-        days = 1
-    end
-    if haskey(kwargs, :equalized)
-        equalized = kwargs[:equalized]
-    else 
-        equalized =1
-    end
-    if haskey(kwargs, :Normalisation)
-        Normalisation = kwargs[:Normalisation]
-    else 
-        Normalisation = true
-    end
-    if haskey(kwargs, :pourcentTrain)
-        pourcentTrain = kwargs[:pourcentTrain]
-    else 
-        pourcentTrain = 0.9
-    end
-
-    if haskey(kwargs, :Augmentation_Value)
-        Augmentation_Value = kwargs[:Augmentation_Value]
-    else 
-        Augmentation_Value = Data_Augmented() 
-    end
-    return Data_WiSig(File_name,name, nbTx, nbSignals, Chunksize,features, txs,rxs,days,equalized,Normalisation,pourcentTrain,Augmentation_Value)
-end
-
-=#