Update README.md

Improvement/README.md

@@ -7,7 +7,7 @@ Since the user must convert the codeword into binary, it can be sent back to the
 
    Binary codeword = Quantized(codeword*2-1)
 
-2. tanh：- 1 < codeword < 1
+2. tanh：-1 < codeword < 1
 
    Binary codeword = Quantized(codeword)
 
@@ -34,6 +34,6 @@ The following are the simulation results of two different activation functions a
 From the above results, we can see that in most scenarios, using tanh for the activation function can bring better results. I think that the corresponding domain of tanh is larger than that of sigmoid, which means that the gradient is also stronger.
 
 ## Reference
-[1] Huang, Tzu-Hao. "5G downlink channel feedback technology based on deep learning"
+[1] T.-H. Huang, "5G downlink channel feedback technology based on deep learning," M.S. thesis, Comm. Eng., National Sun Yat-sen Univ., Kaohsiung, Taiwan, 2023.
 
 [2] MathWorks CSI Feedback with Autoencoders. https://www.mathworks.com/help/5g/ug/csi-feedback-with-autoencoders.html

Improvement/plotNetwork.m

@@ -0,0 +1,24 @@
+function plotNetwork(net,encNet,decNet)
+%plotNetwork Plot autoencoder network
+%   plotNetwork(NET,ENC,DEC) plots the full autoencoder network together
+%   with encoder and decoder networks.
+fig = figure;
+t1 = tiledlayout(1,2,'TileSpacing','Compact');
+t2 = tiledlayout(t1,1,1,'TileSpacing','Tight');
+t3 = tiledlayout(t1,2,1,'TileSpacing','Tight');
+t3.Layout.Tile = 2;
+nexttile(t2)
+plot(net)
+title("Autoencoder")
+nexttile(t3)
+plot(encNet)
+title("Encoder")
+nexttile(t3)
+plot(decNet)
+title("Decoder")
+pos = fig.Position;
+pos(3) = pos(3) + 200;
+pos(4) = pos(4) + 300;
+pos(2) = pos(2) - 300;
+fig.Position = pos;
+end

Improvement/plot_Quantization_Result.m

@@ -0,0 +1,35 @@
+clc
+clear
+close all
+
+envir = 'Indoor'; %'Indoor' or 'Outdoor'
+encoded_dim = 32;  %compress rate=1/4->dim.=512, compress rate=1/16->dim.=128, compress rate=1/32->dim.=64, compress rate=1/64->dim.=32
+model_name = "model_CsiNet_"+envir+"_dim"+num2str(encoded_dim);
+
+load("Sigmoid result\"+model_name+"_Quantization_Result.mat")
+rho_all_sigmoid=rho_all;
+NMSE_all_sigmoid=NMSE_all;
+load("Tanh result\"+model_name+"_Quantization_Result.mat")
+rho_all_tanh=rho_all;
+NMSE_all_tanh=NMSE_all;
+
+figure
+tiledlayout(2,1)
+nexttile
+plot(nBitsVec,rho_all_sigmoid,'*-','LineWidth',1.5)
+hold on
+plot(nBitsVec,rho_all_tanh,'*-','LineWidth',1.5)
+legend("Sigmoid","Tanh")
+title("Correlation ("+envir+" Codeword-" + encoded_dim + ")")
+xlabel("Number of Quantization Bits"); ylabel("\rho")
+grid on
+nexttile
+plot(nBitsVec,NMSE_all_sigmoid,'*-','LineWidth',1.5)
+hold on
+plot(nBitsVec,NMSE_all_tanh,'*-','LineWidth',1.5)
+legend("Sigmoid","Tanh")
+title("NMSE ("+envir+" Codeword-" + encoded_dim + ")")
+xlabel("Number of Quantization Bits"); ylabel("NMSE (dB)")
+grid on
+savefig(gcf,"Compare result\"+envir+"_"+num2str(encoded_dim)+"_Quantized.fig")
+saveas(gcf,"Compare result\"+envir+"_"+num2str(encoded_dim)+"_Quantized.jpg")