I am having trouble understanding how the time width of the Kaiser window is determined from each parameter used in "pspectrum" for time-frequency analysis. Currently, I am using the code below:
pspectrum(W_mix,Fs,’spectrogram’,’FrequencyLimits’,[0 1.2e6],
‘FrequencyResolution’,9e4,’Overlap’,99,’Leakage’,0.75,’MinThreshold’,-95);
Here, w_mix is a waveform data of size 1×15000, and Fs is the sampling frequency, which is 1×10^8. I understand that the shape factor βbetaβ is 10 in this case, but how is the time width of the window function determined? According to the previous research I am referring to, the time width is supposed to be 20.52 μs, but I do not understand the calculation method. Could you please explain it to me?I am having trouble understanding how the time width of the Kaiser window is determined from each parameter used in "pspectrum" for time-frequency analysis. Currently, I am using the code below:
pspectrum(W_mix,Fs,’spectrogram’,’FrequencyLimits’,[0 1.2e6],
‘FrequencyResolution’,9e4,’Overlap’,99,’Leakage’,0.75,’MinThreshold’,-95);
Here, w_mix is a waveform data of size 1×15000, and Fs is the sampling frequency, which is 1×10^8. I understand that the shape factor βbetaβ is 10 in this case, but how is the time width of the window function determined? According to the previous research I am referring to, the time width is supposed to be 20.52 μs, but I do not understand the calculation method. Could you please explain it to me? I am having trouble understanding how the time width of the Kaiser window is determined from each parameter used in "pspectrum" for time-frequency analysis. Currently, I am using the code below:
pspectrum(W_mix,Fs,’spectrogram’,’FrequencyLimits’,[0 1.2e6],
‘FrequencyResolution’,9e4,’Overlap’,99,’Leakage’,0.75,’MinThreshold’,-95);
Here, w_mix is a waveform data of size 1×15000, and Fs is the sampling frequency, which is 1×10^8. I understand that the shape factor βbetaβ is 10 in this case, but how is the time width of the window function determined? According to the previous research I am referring to, the time width is supposed to be 20.52 μs, but I do not understand the calculation method. Could you please explain it to me? ＃time-frecuency analysis MATLAB Answers — New Questions

​

Can someone clear the way to calculate NPCR and UACI as some suggests to change one or more bits of each pixel value in image while otther prefers to change single pixel value in whole image and also some do by a change of single bit in key, Which is the correct way to evaluate these values?Can someone clear the way to calculate NPCR and UACI as some suggests to change one or more bits of each pixel value in image while otther prefers to change single pixel value in whole image and also some do by a change of single bit in key, Which is the correct way to evaluate these values? Can someone clear the way to calculate NPCR and UACI as some suggests to change one or more bits of each pixel value in image while otther prefers to change single pixel value in whole image and also some do by a change of single bit in key, Which is the correct way to evaluate these values? image processing, npcr, uaci, matlab MATLAB Answers — New Questions

​

I generated the code for the model using a C coder, and for real-time performance, I want to push it forward every 1ms. During this 1ms cycle, there are some other tasks that need to be completed in addition to the points that should be generated based on the step size. Therefore, I do not want to simply push the waveform generation based on the step size, as this will result in new points coming before I have finished reading and writing, which may miss or overwrite these points. This is something I do not want to happen.I generated the code for the model using a C coder, and for real-time performance, I want to push it forward every 1ms. During this 1ms cycle, there are some other tasks that need to be completed in addition to the points that should be generated based on the step size. Therefore, I do not want to simply push the waveform generation based on the step size, as this will result in new points coming before I have finished reading and writing, which may miss or overwrite these points. This is something I do not want to happen. I generated the code for the model using a C coder, and for real-time performance, I want to push it forward every 1ms. During this 1ms cycle, there are some other tasks that need to be completed in addition to the points that should be generated based on the step size. Therefore, I do not want to simply push the waveform generation based on the step size, as this will result in new points coming before I have finished reading and writing, which may miss or overwrite these points. This is something I do not want to happen. simulink, embedded coder MATLAB Answers — New Questions

​

I have a text box (text edit field, shoud it be a label box?)
I want to add lines of text to it without loosing the old text.
app.DebugField.Value = append(app.DebugField.Value, "rew LBn");
but the newlines are showing up as text n
Start Trials at 22:26:09 >> rew LBn

I guess I have to sprintf() the string first but why can’t n be properly interpreted by string handling functions?I have a text box (text edit field, shoud it be a label box?)
I want to add lines of text to it without loosing the old text.
app.DebugField.Value = append(app.DebugField.Value, "rew LBn");
but the newlines are showing up as text n
Start Trials at 22:26:09 >> rew LBn

I guess I have to sprintf() the string first but why can’t n be properly interpreted by string handling functions? I have a text box (text edit field, shoud it be a label box?)
I want to add lines of text to it without loosing the old text.
app.DebugField.Value = append(app.DebugField.Value, "rew LBn");
but the newlines are showing up as text n
Start Trials at 22:26:09 >> rew LBn

I guess I have to sprintf() the string first but why can’t n be properly interpreted by string handling functions? n in strings MATLAB Answers — New Questions

​

How can I stop the MathWorks Service Host from running on startup?How can I stop the MathWorks Service Host from running on startup? How can I stop the MathWorks Service Host from running on startup? mathworks-startup-host MATLAB Answers — New Questions

​

I need help to simulate this paper for my final project.
Please help me to simulate this paper.
thanksI need help to simulate this paper for my final project.
Please help me to simulate this paper.
thanks I need help to simulate this paper for my final project.
Please help me to simulate this paper.
thanks comparison of artifact correction methods for infant eeg applied to extraction of event-related potential signals MATLAB Answers — New Questions

​

I am using the following two-sample tests for non-normal distributions:
chi2gof
kstest2
ranksum
kruskalwallis
and I would like to calculate the Bayes Factor as well.
I found the bayesFactor Version 1.0.0 (253 KB) by Bart Krekelberg. However, to the best of my understanfding, that package has a limited number of implemented tests:
One sample t-test (bf.ttest)
Two sample t-test (bf.ttest2)
N-Way Anova with fixed and random effects, including continuous co-variates (bf.anova)
Regression (bf.regression)
Pearson Correlation (bf.corr)
Binomial Test (bf.binom)
Experimental Design & Power Analysis (bf.designAnalysis)
and I am not sure if they can be used as additional analysis to the 4 initially listed ones that I am employing.

Does anyone know if there are other Matlab functions/packages to calculate the Bayes factor, in relation to the two-sample tests I am currently using (i.e. the chi2gof, the kstest2, the ranksum, and the kruskalwallis)?I am using the following two-sample tests for non-normal distributions:
chi2gof
kstest2
ranksum
kruskalwallis
and I would like to calculate the Bayes Factor as well.
I found the bayesFactor Version 1.0.0 (253 KB) by Bart Krekelberg. However, to the best of my understanfding, that package has a limited number of implemented tests:
One sample t-test (bf.ttest)
Two sample t-test (bf.ttest2)
N-Way Anova with fixed and random effects, including continuous co-variates (bf.anova)
Regression (bf.regression)
Pearson Correlation (bf.corr)
Binomial Test (bf.binom)
Experimental Design & Power Analysis (bf.designAnalysis)
and I am not sure if they can be used as additional analysis to the 4 initially listed ones that I am employing.

Does anyone know if there are other Matlab functions/packages to calculate the Bayes factor, in relation to the two-sample tests I am currently using (i.e. the chi2gof, the kstest2, the ranksum, and the kruskalwallis)? I am using the following two-sample tests for non-normal distributions:
chi2gof
kstest2
ranksum
kruskalwallis
and I would like to calculate the Bayes Factor as well.
I found the bayesFactor Version 1.0.0 (253 KB) by Bart Krekelberg. However, to the best of my understanfding, that package has a limited number of implemented tests:
One sample t-test (bf.ttest)
Two sample t-test (bf.ttest2)
N-Way Anova with fixed and random effects, including continuous co-variates (bf.anova)
Regression (bf.regression)
Pearson Correlation (bf.corr)
Binomial Test (bf.binom)
Experimental Design & Power Analysis (bf.designAnalysis)
and I am not sure if they can be used as additional analysis to the 4 initially listed ones that I am employing.

Does anyone know if there are other Matlab functions/packages to calculate the Bayes factor, in relation to the two-sample tests I am currently using (i.e. the chi2gof, the kstest2, the ranksum, and the kruskalwallis)? chi2gof, chi-square, kstest2, kolmogorov-smirnov test, ranksum, wilcoxon rank sum test, kruskalwallis, kruskal-wallis test, bayes factor MATLAB Answers — New Questions

​

hello everyone
i have error whene i use cnn-lstm
this is the error
Error using trainNetwork (line 191)
Invalid training data. The output size (1024) of the last layer does not match the response size (1).

Error in Main_fn (line 266)
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);

Error in Fig12_generator (line 49)
[Rate_DL,Rate_OPT]=Main_fn(L,My_ar,Mz_ar,M_bar,K_DL,Pt,kbeams(rr),Training_Size);

but whene use cnn onle the code run without error and i make every possible to fix it but it not work and i change the shape of YTrain but still the error
function [Rate_DL,Rate_OPT]=Main_fn(L,My,Mz,M_bar,K_DL,Pt,kbeams,Training_Size)
%% Description:
%
% This is the function called by the main script for ploting Figure 10
% in the original article mentioned below.
%
% version 1.0 (Last edited: 2019-05-10)
%
% The definitions and equations used in this code refer (mostly) to the
% following publication:
%
% Abdelrahman Taha, Muhammad Alrabeiah, and Ahmed Alkhateeb, "Enabling
% Large Intelligent Surfaces with Compressive Sensing and Deep Learning,"
% arXiv e-prints, p. arXiv:1904.10136, Apr 2019.
% [Online]. Available: https://arxiv.org/abs/1904.10136
%
% The DeepMIMO dataset is adopted.
% [Online]. Available: http://deepmimo.net/
%
% License: This code is licensed under a Creative Commons
% Attribution-NonCommercial-ShareAlike 4.0 International License.
% [Online]. Available: https://creativecommons.org/licenses/by-nc-sa/4.0/
% If you in any way use this code for research that results in
% publications, please cite our original article mentioned above.

%% System Model Parameters

params.scenario=’O1_28′; % DeepMIMO Dataset scenario: http://deepmimo.net/
params.active_BS=3; % active basestation(/s) in the chosen scenario
D_Lambda = 0.5; % Antenna spacing relative to the wavelength
BW = 100e6; % Bandwidth

Ut_row = 850; % user Ut row number
Ut_element = 90; % user Ut position from the row chosen above
Ur_rows = [1000 1200]; % user Ur rows

Validation_Size = 6200; % Validation dataset Size
K = 512; % number of subcarriers
miniBatchSize = 500; % Size of the minibatch for the Deep Learning
% Note: The axes of the antennas match the axes of the ray-tracing scenario
Mx = 1; % number of LIS reflecting elements across the x axis
M = Mx.*My.*Mz; % Total number of LIS reflecting elements

% Preallocation of output variables
Rate_DL = zeros(1,length(Training_Size));
Rate_OPT = Rate_DL;
LastValidationRMSE = Rate_DL;

%— Accounting SNR in ach rate calculations
%— Definning Noisy channel measurements
Gt=3; % dBi
Gr=3; % dBi
NF=5; % Noise figure at the User equipment
Process_Gain=10; % Channel estimation processing gain
noise_power_dB=-204+10*log10(BW/K)+NF-Process_Gain; % Noise power in dB
SNR=10^(.1*(-noise_power_dB))*(10^(.1*(Gt+Gr+Pt)))^2; % Signal-to-noise ratio
% channel estimation noise
noise_power_bar=10^(.1*(noise_power_dB))/(10^(.1*(Gt+Gr+Pt)));

No_user_pairs = (Ur_rows(2)-Ur_rows(1))*181; % Number of (Ut,Ur) user pairs
RandP_all = randperm(No_user_pairs).’; % Random permutation of the available dataset

%% Starting the code
disp(‘======================================================================================================================’);
disp([‘ Calculating for M = ‘ num2str(M)]);
Rand_M_bar_all = randperm(M);

%% Beamforming Codebook
% BF codebook parameters
over_sampling_x=1; % The beamsteering oversampling factor in the x direction
over_sampling_y=1; % The beamsteering oversampling factor in the y direction
over_sampling_z=1; % The beamsteering oversampling factor in the z direction

% Generating the BF codebook
[BF_codebook]=sqrt(Mx*My*Mz)*UPA_codebook_generator(Mx,My,Mz,over_sampling_x,over_sampling_y,over_sampling_z,D_Lambda);
codebook_size=size(BF_codebook,2);

%% DeepMIMO Dataset Generation
disp(‘————————————————————-‘);
disp([‘ Calculating for K_DL = ‘ num2str(K_DL)]);
% —— Inputs to the DeepMIMO dataset generation code ———— %
% Note: The axes of the antennas match the axes of the ray-tracing scenario
params.num_ant_x= Mx; % Number of the UPA antenna array on the x-axis
params.num_ant_y= My; % Number of the UPA antenna array on the y-axis
params.num_ant_z= Mz; % Number of the UPA antenna array on the z-axis
params.ant_spacing=D_Lambda; % ratio of the wavelnegth; for half wavelength enter .5
params.bandwidth= BW*1e-9; % The bandiwdth in GHz
params.num_OFDM= K; % Number of OFDM subcarriers
params.OFDM_sampling_factor=1; % The constructed channels will be calculated only at the sampled subcarriers (to reduce the size of the dataset)
params.OFDM_limit=K_DL*1; % Only the first params.OFDM_limit subcarriers will be considered when constructing the channels
params.num_paths=L; % Maximum number of paths to be considered (a value between 1 and 25), e.g., choose 1 if you are only interested in the strongest path
params.saveDataset=0;
disp([‘ Calculating for L = ‘ num2str(params.num_paths)]);

% —————— DeepMIMO "Ut" Dataset Generation —————–%
params.active_user_first=Ut_row;
params.active_user_last=Ut_row;
DeepMIMO_dataset=DeepMIMO_generator(params);
Ht = single(DeepMIMO_dataset{1}.user{Ut_element}.channel);
clear DeepMIMO_dataset

% —————— DeepMIMO "Ur" Dataset Generation —————–%
%Validation part for the actual achievable rate perf eval
Validation_Ind = RandP_all(end-Validation_Size+1:end);
[~,VI_sortind] = sort(Validation_Ind);
[~,VI_rev_sortind] = sort(VI_sortind);
%initialization
Ur_rows_step = 100; % access the dataset 100 rows at a time
Ur_rows_grid=Ur_rows(1):Ur_rows_step:Ur_rows(2);
Delta_H_max = single(0);
for pp = 1:1:numel(Ur_rows_grid)-1 % loop for Normalizing H
clear DeepMIMO_dataset
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
for u=1:params.num_user
Hr = single(conj(DeepMIMO_dataset{1}.user{u}.channel));
Delta_H = max(max(abs(Ht.*Hr)));
if Delta_H >= Delta_H_max
Delta_H_max = single(Delta_H);
end
end
end
clear Delta_H
disp(‘=============================================================’);
disp([‘ Calculating for M_bar = ‘ num2str(M_bar)]);
Rand_M_bar =unique(Rand_M_bar_all(1:M_bar));
Ht_bar = reshape(Ht(Rand_M_bar,:),M_bar*K_DL,1);
DL_input = single(zeros(M_bar*K_DL*2,No_user_pairs));
DL_output = single(zeros(No_user_pairs,codebook_size));
DL_output_un= single(zeros(numel(Validation_Ind),codebook_size));
Delta_H_bar_max = single(0);
count=0;
for pp = 1:1:numel(Ur_rows_grid)-1
clear DeepMIMO_dataset
disp([‘Starting received user access ‘ num2str(pp)]);
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
%% Construct Deep Learning inputs
u_step=100;
Htx=repmat(Ht(:,1),1,u_step);
Hrx=zeros(M,u_step);
for u=1:u_step:params.num_user
for uu=1:1:u_step
Hr = single(conj(DeepMIMO_dataset{1}.user{u+uu-1}.channel));
Hr_bar = reshape(Hr(Rand_M_bar,:),M_bar*K_DL,1);
%— Constructing the sampled channel
n1=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
n2=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
H_bar = ((Ht_bar+n1).*(Hr_bar+n2));
DL_input(:,u+uu-1+((pp-1)*params.num_user))= reshape([real(H_bar) imag(H_bar)].’,[],1);
Delta_H_bar = max(max(abs(H_bar)));
if Delta_H_bar >= Delta_H_bar_max
Delta_H_bar_max = single(Delta_H_bar);
end
Hrx(:,uu)=Hr(:,1);
end
%— Actual achievable rate for performance evaluation
H = Htx.*Hrx;
H_BF=H.’*BF_codebook;
SNR_sqrt_var = abs(H_BF);
for uu=1:1:u_step
if sum((Validation_Ind == u+uu-1+((pp-1)*params.num_user)))
count=count+1;
DL_output_un(count,:) = single(sum(log2(1+(SNR*((SNR_sqrt_var(uu,:)).^2))),1));
end
end
%— Label for the sampled channel
R = single(log2(1+(SNR_sqrt_var/Delta_H_max).^2));
% — DL output normalization
Delta_Out_max = max(R,[],2);
if ~sum(Delta_Out_max == 0)
Rn=diag(1./Delta_Out_max)*R;
end
DL_output(u+((pp-1)*params.num_user):u+((pp-1)*params.num_user)+u_step-1,:) = 1*Rn; %%%%% Normalized %%%%%
end
end
clear u Delta_H_bar R Rn
%– Sorting back the DL_output_un
DL_output_un = DL_output_un(VI_rev_sortind,:);
%— DL input normalization
DL_input= 1*(DL_input/Delta_H_bar_max); %%%%% Normalized from -1->1 %%%%%

%% DL Beamforming
% —————— Training and Testing Datasets —————–%
% Reshape for CNN-LSTM
% Assuming each sample is a sequence of features where each feature vector should be treated as a 1D image (sequence length x 1 x 1)

DL_output_reshaped = reshape(DL_output.’, size(DL_output,2), 1, 1, size(DL_output,1));
DL_output_reshaped_un = reshape(DL_output_un.’, size(DL_output_un,2), 1, 1, size(DL_output_un,1));
DL_input_reshaped = reshape(DL_input, size(DL_input,1), 1, 1, size(DL_input,2));

for dd=1:numel(Training_Size)
disp([‘ Calculating for Dataset Size = ‘ num2str(Training_Size(dd))]);
Training_Ind = RandP_all(1:Training_Size(dd));

% Index the reshaped data for training and validation
XTrain = single(DL_input_reshaped(:,1,:,Training_Ind));
YTrain = single(DL_output_reshaped(:,:,1,Training_Ind));
XValidation = single(DL_input_reshaped(:,1,:,Validation_Ind));
YValidation = single(DL_output_reshaped(:,:,1,Validation_Ind));
YValidation_un = single(DL_output_reshaped_un(:,:,1,:));

%% DL Model definition with adjusted pooling and convolution layers
layers = [
imageInputLayer([size(XTrain,1), 1, 1],’Name’,’input’,’Normalization’,’none’)

convolution2dLayer(3, 64, ‘Padding’, ‘same’, ‘Name’, ‘conv1’)
batchNormalizationLayer(‘Name’, ‘bn1’)
reluLayer(‘Name’, ‘relu1’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool1’)

convolution2dLayer(3, 128, ‘Padding’, ‘same’, ‘Name’, ‘conv2’)
batchNormalizationLayer(‘Name’, ‘bn2’)
reluLayer(‘Name’, ‘relu2’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool2’)

convolution2dLayer(3, 256, ‘Padding’, ‘same’, ‘Name’, ‘conv3’)
batchNormalizationLayer(‘Name’, ‘bn3’)
reluLayer(‘Name’, ‘relu3’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool3’)

flattenLayer(‘Name’, ‘flatten’)
lstmLayer(128, ‘Name’, ‘lstm1’, ‘OutputMode’, ‘sequence’)
lstmLayer(128, ‘Name’, ‘lstm2’, ‘OutputMode’, ‘last’)

fullyConnectedLayer(512, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu4’)
dropoutLayer(0.5, ‘Name’, ‘dropout1’)

fullyConnectedLayer(1024, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu5’)
dropoutLayer(0.5, ‘Name’, ‘dropout2’)

fullyConnectedLayer(2048, ‘Name’, ‘fc3’)
reluLayer(‘Name’, ‘relu6’)
dropoutLayer(0.5, ‘Name’, ‘dropout3’)

fullyConnectedLayer(size(YTrain,3), ‘Name’, ‘fc4’)
regressionLayer(‘Name’, ‘output’)
];

options = trainingOptions(‘rmsprop’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 20, …
‘InitialLearnRate’, 1e-3, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.5, …
‘LearnRateDropPeriod’, 10, …
‘L2Regularization’, 1e-4, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {XValidation, YValidation}, …
‘ValidationFrequency’, 30, …
‘Verbose’, 1, …
‘Plots’, ‘none’, …
‘ExecutionEnvironment’, ‘cpu’);

[~,Indmax_OPT]= max(YValidation,[],3);
Indmax_OPT = squeeze(Indmax_OPT); %Upper bound on achievable rates
MaxR_OPT = single(zeros(numel(Indmax_OPT),1));
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);
YPredicted = predict(trainedNet,XValidation);
% ——————— Achievable Rate ————————–%
[~,Indmax_DL] = maxk(YPredicted,kbeams,2);
MaxR_DL = single(zeros(size(Indmax_DL,1),1)); %True achievable rates
for b=1:size(Indmax_DL,1)
MaxR_DL(b) = max(squeeze(YValidation_un(1,1,Indmax_DL(b,:),b)));
MaxR_OPT(b) = squeeze(YValidation_un(1,1,Indmax_OPT(b),b));
end
Rate_OPT(dd) = mean(MaxR_OPT);
Rate_DL(dd) = mean(MaxR_DL);
LastValidationRMSE(dd) = traininfo.ValidationRMSE(end);
clear trainedNet traininfo YPredicted
clear layers options Rate_DL_Temp MaxR_DL_Temp Highest_Rate
end
endhello everyone
i have error whene i use cnn-lstm
this is the error
Error using trainNetwork (line 191)
Invalid training data. The output size (1024) of the last layer does not match the response size (1).

Error in Main_fn (line 266)
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);

Error in Fig12_generator (line 49)
[Rate_DL,Rate_OPT]=Main_fn(L,My_ar,Mz_ar,M_bar,K_DL,Pt,kbeams(rr),Training_Size);

but whene use cnn onle the code run without error and i make every possible to fix it but it not work and i change the shape of YTrain but still the error
function [Rate_DL,Rate_OPT]=Main_fn(L,My,Mz,M_bar,K_DL,Pt,kbeams,Training_Size)
%% Description:
%
% This is the function called by the main script for ploting Figure 10
% in the original article mentioned below.
%
% version 1.0 (Last edited: 2019-05-10)
%
% The definitions and equations used in this code refer (mostly) to the
% following publication:
%
% Abdelrahman Taha, Muhammad Alrabeiah, and Ahmed Alkhateeb, "Enabling
% Large Intelligent Surfaces with Compressive Sensing and Deep Learning,"
% arXiv e-prints, p. arXiv:1904.10136, Apr 2019.
% [Online]. Available: https://arxiv.org/abs/1904.10136
%
% The DeepMIMO dataset is adopted.
% [Online]. Available: http://deepmimo.net/
%
% License: This code is licensed under a Creative Commons
% Attribution-NonCommercial-ShareAlike 4.0 International License.
% [Online]. Available: https://creativecommons.org/licenses/by-nc-sa/4.0/
% If you in any way use this code for research that results in
% publications, please cite our original article mentioned above.

%% System Model Parameters

params.scenario=’O1_28′; % DeepMIMO Dataset scenario: http://deepmimo.net/
params.active_BS=3; % active basestation(/s) in the chosen scenario
D_Lambda = 0.5; % Antenna spacing relative to the wavelength
BW = 100e6; % Bandwidth

Ut_row = 850; % user Ut row number
Ut_element = 90; % user Ut position from the row chosen above
Ur_rows = [1000 1200]; % user Ur rows

Validation_Size = 6200; % Validation dataset Size
K = 512; % number of subcarriers
miniBatchSize = 500; % Size of the minibatch for the Deep Learning
% Note: The axes of the antennas match the axes of the ray-tracing scenario
Mx = 1; % number of LIS reflecting elements across the x axis
M = Mx.*My.*Mz; % Total number of LIS reflecting elements

% Preallocation of output variables
Rate_DL = zeros(1,length(Training_Size));
Rate_OPT = Rate_DL;
LastValidationRMSE = Rate_DL;

%— Accounting SNR in ach rate calculations
%— Definning Noisy channel measurements
Gt=3; % dBi
Gr=3; % dBi
NF=5; % Noise figure at the User equipment
Process_Gain=10; % Channel estimation processing gain
noise_power_dB=-204+10*log10(BW/K)+NF-Process_Gain; % Noise power in dB
SNR=10^(.1*(-noise_power_dB))*(10^(.1*(Gt+Gr+Pt)))^2; % Signal-to-noise ratio
% channel estimation noise
noise_power_bar=10^(.1*(noise_power_dB))/(10^(.1*(Gt+Gr+Pt)));

No_user_pairs = (Ur_rows(2)-Ur_rows(1))*181; % Number of (Ut,Ur) user pairs
RandP_all = randperm(No_user_pairs).’; % Random permutation of the available dataset

%% Starting the code
disp(‘======================================================================================================================’);
disp([‘ Calculating for M = ‘ num2str(M)]);
Rand_M_bar_all = randperm(M);

%% Beamforming Codebook
% BF codebook parameters
over_sampling_x=1; % The beamsteering oversampling factor in the x direction
over_sampling_y=1; % The beamsteering oversampling factor in the y direction
over_sampling_z=1; % The beamsteering oversampling factor in the z direction

% Generating the BF codebook
[BF_codebook]=sqrt(Mx*My*Mz)*UPA_codebook_generator(Mx,My,Mz,over_sampling_x,over_sampling_y,over_sampling_z,D_Lambda);
codebook_size=size(BF_codebook,2);

%% DeepMIMO Dataset Generation
disp(‘————————————————————-‘);
disp([‘ Calculating for K_DL = ‘ num2str(K_DL)]);
% —— Inputs to the DeepMIMO dataset generation code ———— %
% Note: The axes of the antennas match the axes of the ray-tracing scenario
params.num_ant_x= Mx; % Number of the UPA antenna array on the x-axis
params.num_ant_y= My; % Number of the UPA antenna array on the y-axis
params.num_ant_z= Mz; % Number of the UPA antenna array on the z-axis
params.ant_spacing=D_Lambda; % ratio of the wavelnegth; for half wavelength enter .5
params.bandwidth= BW*1e-9; % The bandiwdth in GHz
params.num_OFDM= K; % Number of OFDM subcarriers
params.OFDM_sampling_factor=1; % The constructed channels will be calculated only at the sampled subcarriers (to reduce the size of the dataset)
params.OFDM_limit=K_DL*1; % Only the first params.OFDM_limit subcarriers will be considered when constructing the channels
params.num_paths=L; % Maximum number of paths to be considered (a value between 1 and 25), e.g., choose 1 if you are only interested in the strongest path
params.saveDataset=0;
disp([‘ Calculating for L = ‘ num2str(params.num_paths)]);

% —————— DeepMIMO "Ut" Dataset Generation —————–%
params.active_user_first=Ut_row;
params.active_user_last=Ut_row;
DeepMIMO_dataset=DeepMIMO_generator(params);
Ht = single(DeepMIMO_dataset{1}.user{Ut_element}.channel);
clear DeepMIMO_dataset

% —————— DeepMIMO "Ur" Dataset Generation —————–%
%Validation part for the actual achievable rate perf eval
Validation_Ind = RandP_all(end-Validation_Size+1:end);
[~,VI_sortind] = sort(Validation_Ind);
[~,VI_rev_sortind] = sort(VI_sortind);
%initialization
Ur_rows_step = 100; % access the dataset 100 rows at a time
Ur_rows_grid=Ur_rows(1):Ur_rows_step:Ur_rows(2);
Delta_H_max = single(0);
for pp = 1:1:numel(Ur_rows_grid)-1 % loop for Normalizing H
clear DeepMIMO_dataset
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
for u=1:params.num_user
Hr = single(conj(DeepMIMO_dataset{1}.user{u}.channel));
Delta_H = max(max(abs(Ht.*Hr)));
if Delta_H >= Delta_H_max
Delta_H_max = single(Delta_H);
end
end
end
clear Delta_H
disp(‘=============================================================’);
disp([‘ Calculating for M_bar = ‘ num2str(M_bar)]);
Rand_M_bar =unique(Rand_M_bar_all(1:M_bar));
Ht_bar = reshape(Ht(Rand_M_bar,:),M_bar*K_DL,1);
DL_input = single(zeros(M_bar*K_DL*2,No_user_pairs));
DL_output = single(zeros(No_user_pairs,codebook_size));
DL_output_un= single(zeros(numel(Validation_Ind),codebook_size));
Delta_H_bar_max = single(0);
count=0;
for pp = 1:1:numel(Ur_rows_grid)-1
clear DeepMIMO_dataset
disp([‘Starting received user access ‘ num2str(pp)]);
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
%% Construct Deep Learning inputs
u_step=100;
Htx=repmat(Ht(:,1),1,u_step);
Hrx=zeros(M,u_step);
for u=1:u_step:params.num_user
for uu=1:1:u_step
Hr = single(conj(DeepMIMO_dataset{1}.user{u+uu-1}.channel));
Hr_bar = reshape(Hr(Rand_M_bar,:),M_bar*K_DL,1);
%— Constructing the sampled channel
n1=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
n2=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
H_bar = ((Ht_bar+n1).*(Hr_bar+n2));
DL_input(:,u+uu-1+((pp-1)*params.num_user))= reshape([real(H_bar) imag(H_bar)].’,[],1);
Delta_H_bar = max(max(abs(H_bar)));
if Delta_H_bar >= Delta_H_bar_max
Delta_H_bar_max = single(Delta_H_bar);
end
Hrx(:,uu)=Hr(:,1);
end
%— Actual achievable rate for performance evaluation
H = Htx.*Hrx;
H_BF=H.’*BF_codebook;
SNR_sqrt_var = abs(H_BF);
for uu=1:1:u_step
if sum((Validation_Ind == u+uu-1+((pp-1)*params.num_user)))
count=count+1;
DL_output_un(count,:) = single(sum(log2(1+(SNR*((SNR_sqrt_var(uu,:)).^2))),1));
end
end
%— Label for the sampled channel
R = single(log2(1+(SNR_sqrt_var/Delta_H_max).^2));
% — DL output normalization
Delta_Out_max = max(R,[],2);
if ~sum(Delta_Out_max == 0)
Rn=diag(1./Delta_Out_max)*R;
end
DL_output(u+((pp-1)*params.num_user):u+((pp-1)*params.num_user)+u_step-1,:) = 1*Rn; %%%%% Normalized %%%%%
end
end
clear u Delta_H_bar R Rn
%– Sorting back the DL_output_un
DL_output_un = DL_output_un(VI_rev_sortind,:);
%— DL input normalization
DL_input= 1*(DL_input/Delta_H_bar_max); %%%%% Normalized from -1->1 %%%%%

%% DL Beamforming
% —————— Training and Testing Datasets —————–%
% Reshape for CNN-LSTM
% Assuming each sample is a sequence of features where each feature vector should be treated as a 1D image (sequence length x 1 x 1)

DL_output_reshaped = reshape(DL_output.’, size(DL_output,2), 1, 1, size(DL_output,1));
DL_output_reshaped_un = reshape(DL_output_un.’, size(DL_output_un,2), 1, 1, size(DL_output_un,1));
DL_input_reshaped = reshape(DL_input, size(DL_input,1), 1, 1, size(DL_input,2));

for dd=1:numel(Training_Size)
disp([‘ Calculating for Dataset Size = ‘ num2str(Training_Size(dd))]);
Training_Ind = RandP_all(1:Training_Size(dd));

% Index the reshaped data for training and validation
XTrain = single(DL_input_reshaped(:,1,:,Training_Ind));
YTrain = single(DL_output_reshaped(:,:,1,Training_Ind));
XValidation = single(DL_input_reshaped(:,1,:,Validation_Ind));
YValidation = single(DL_output_reshaped(:,:,1,Validation_Ind));
YValidation_un = single(DL_output_reshaped_un(:,:,1,:));

%% DL Model definition with adjusted pooling and convolution layers
layers = [
imageInputLayer([size(XTrain,1), 1, 1],’Name’,’input’,’Normalization’,’none’)

convolution2dLayer(3, 64, ‘Padding’, ‘same’, ‘Name’, ‘conv1’)
batchNormalizationLayer(‘Name’, ‘bn1’)
reluLayer(‘Name’, ‘relu1’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool1’)

convolution2dLayer(3, 128, ‘Padding’, ‘same’, ‘Name’, ‘conv2’)
batchNormalizationLayer(‘Name’, ‘bn2’)
reluLayer(‘Name’, ‘relu2’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool2’)

convolution2dLayer(3, 256, ‘Padding’, ‘same’, ‘Name’, ‘conv3’)
batchNormalizationLayer(‘Name’, ‘bn3’)
reluLayer(‘Name’, ‘relu3’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool3’)

flattenLayer(‘Name’, ‘flatten’)
lstmLayer(128, ‘Name’, ‘lstm1’, ‘OutputMode’, ‘sequence’)
lstmLayer(128, ‘Name’, ‘lstm2’, ‘OutputMode’, ‘last’)

fullyConnectedLayer(512, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu4’)
dropoutLayer(0.5, ‘Name’, ‘dropout1’)

fullyConnectedLayer(1024, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu5’)
dropoutLayer(0.5, ‘Name’, ‘dropout2’)

fullyConnectedLayer(2048, ‘Name’, ‘fc3’)
reluLayer(‘Name’, ‘relu6’)
dropoutLayer(0.5, ‘Name’, ‘dropout3’)

fullyConnectedLayer(size(YTrain,3), ‘Name’, ‘fc4’)
regressionLayer(‘Name’, ‘output’)
];

options = trainingOptions(‘rmsprop’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 20, …
‘InitialLearnRate’, 1e-3, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.5, …
‘LearnRateDropPeriod’, 10, …
‘L2Regularization’, 1e-4, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {XValidation, YValidation}, …
‘ValidationFrequency’, 30, …
‘Verbose’, 1, …
‘Plots’, ‘none’, …
‘ExecutionEnvironment’, ‘cpu’);

[~,Indmax_OPT]= max(YValidation,[],3);
Indmax_OPT = squeeze(Indmax_OPT); %Upper bound on achievable rates
MaxR_OPT = single(zeros(numel(Indmax_OPT),1));
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);
YPredicted = predict(trainedNet,XValidation);
% ——————— Achievable Rate ————————–%
[~,Indmax_DL] = maxk(YPredicted,kbeams,2);
MaxR_DL = single(zeros(size(Indmax_DL,1),1)); %True achievable rates
for b=1:size(Indmax_DL,1)
MaxR_DL(b) = max(squeeze(YValidation_un(1,1,Indmax_DL(b,:),b)));
MaxR_OPT(b) = squeeze(YValidation_un(1,1,Indmax_OPT(b),b));
end
Rate_OPT(dd) = mean(MaxR_OPT);
Rate_DL(dd) = mean(MaxR_DL);
LastValidationRMSE(dd) = traininfo.ValidationRMSE(end);
clear trainedNet traininfo YPredicted
clear layers options Rate_DL_Temp MaxR_DL_Temp Highest_Rate
end
end hello everyone
i have error whene i use cnn-lstm
this is the error
Error using trainNetwork (line 191)
Invalid training data. The output size (1024) of the last layer does not match the response size (1).

Error in Main_fn (line 266)
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);

Error in Fig12_generator (line 49)
[Rate_DL,Rate_OPT]=Main_fn(L,My_ar,Mz_ar,M_bar,K_DL,Pt,kbeams(rr),Training_Size);

but whene use cnn onle the code run without error and i make every possible to fix it but it not work and i change the shape of YTrain but still the error
function [Rate_DL,Rate_OPT]=Main_fn(L,My,Mz,M_bar,K_DL,Pt,kbeams,Training_Size)
%% Description:
%
% This is the function called by the main script for ploting Figure 10
% in the original article mentioned below.
%
% version 1.0 (Last edited: 2019-05-10)
%
% The definitions and equations used in this code refer (mostly) to the
% following publication:
%
% Abdelrahman Taha, Muhammad Alrabeiah, and Ahmed Alkhateeb, "Enabling
% Large Intelligent Surfaces with Compressive Sensing and Deep Learning,"
% arXiv e-prints, p. arXiv:1904.10136, Apr 2019.
% [Online]. Available: https://arxiv.org/abs/1904.10136
%
% The DeepMIMO dataset is adopted.
% [Online]. Available: http://deepmimo.net/
%
% License: This code is licensed under a Creative Commons
% Attribution-NonCommercial-ShareAlike 4.0 International License.
% [Online]. Available: https://creativecommons.org/licenses/by-nc-sa/4.0/
% If you in any way use this code for research that results in
% publications, please cite our original article mentioned above.

%% System Model Parameters

params.scenario=’O1_28′; % DeepMIMO Dataset scenario: http://deepmimo.net/
params.active_BS=3; % active basestation(/s) in the chosen scenario
D_Lambda = 0.5; % Antenna spacing relative to the wavelength
BW = 100e6; % Bandwidth

Ut_row = 850; % user Ut row number
Ut_element = 90; % user Ut position from the row chosen above
Ur_rows = [1000 1200]; % user Ur rows

Validation_Size = 6200; % Validation dataset Size
K = 512; % number of subcarriers
miniBatchSize = 500; % Size of the minibatch for the Deep Learning
% Note: The axes of the antennas match the axes of the ray-tracing scenario
Mx = 1; % number of LIS reflecting elements across the x axis
M = Mx.*My.*Mz; % Total number of LIS reflecting elements

% Preallocation of output variables
Rate_DL = zeros(1,length(Training_Size));
Rate_OPT = Rate_DL;
LastValidationRMSE = Rate_DL;

%— Accounting SNR in ach rate calculations
%— Definning Noisy channel measurements
Gt=3; % dBi
Gr=3; % dBi
NF=5; % Noise figure at the User equipment
Process_Gain=10; % Channel estimation processing gain
noise_power_dB=-204+10*log10(BW/K)+NF-Process_Gain; % Noise power in dB
SNR=10^(.1*(-noise_power_dB))*(10^(.1*(Gt+Gr+Pt)))^2; % Signal-to-noise ratio
% channel estimation noise
noise_power_bar=10^(.1*(noise_power_dB))/(10^(.1*(Gt+Gr+Pt)));

No_user_pairs = (Ur_rows(2)-Ur_rows(1))*181; % Number of (Ut,Ur) user pairs
RandP_all = randperm(No_user_pairs).’; % Random permutation of the available dataset

%% Starting the code
disp(‘======================================================================================================================’);
disp([‘ Calculating for M = ‘ num2str(M)]);
Rand_M_bar_all = randperm(M);

%% Beamforming Codebook
% BF codebook parameters
over_sampling_x=1; % The beamsteering oversampling factor in the x direction
over_sampling_y=1; % The beamsteering oversampling factor in the y direction
over_sampling_z=1; % The beamsteering oversampling factor in the z direction

% Generating the BF codebook
[BF_codebook]=sqrt(Mx*My*Mz)*UPA_codebook_generator(Mx,My,Mz,over_sampling_x,over_sampling_y,over_sampling_z,D_Lambda);
codebook_size=size(BF_codebook,2);

%% DeepMIMO Dataset Generation
disp(‘————————————————————-‘);
disp([‘ Calculating for K_DL = ‘ num2str(K_DL)]);
% —— Inputs to the DeepMIMO dataset generation code ———— %
% Note: The axes of the antennas match the axes of the ray-tracing scenario
params.num_ant_x= Mx; % Number of the UPA antenna array on the x-axis
params.num_ant_y= My; % Number of the UPA antenna array on the y-axis
params.num_ant_z= Mz; % Number of the UPA antenna array on the z-axis
params.ant_spacing=D_Lambda; % ratio of the wavelnegth; for half wavelength enter .5
params.bandwidth= BW*1e-9; % The bandiwdth in GHz
params.num_OFDM= K; % Number of OFDM subcarriers
params.OFDM_sampling_factor=1; % The constructed channels will be calculated only at the sampled subcarriers (to reduce the size of the dataset)
params.OFDM_limit=K_DL*1; % Only the first params.OFDM_limit subcarriers will be considered when constructing the channels
params.num_paths=L; % Maximum number of paths to be considered (a value between 1 and 25), e.g., choose 1 if you are only interested in the strongest path
params.saveDataset=0;
disp([‘ Calculating for L = ‘ num2str(params.num_paths)]);

% —————— DeepMIMO "Ut" Dataset Generation —————–%
params.active_user_first=Ut_row;
params.active_user_last=Ut_row;
DeepMIMO_dataset=DeepMIMO_generator(params);
Ht = single(DeepMIMO_dataset{1}.user{Ut_element}.channel);
clear DeepMIMO_dataset

% —————— DeepMIMO "Ur" Dataset Generation —————–%
%Validation part for the actual achievable rate perf eval
Validation_Ind = RandP_all(end-Validation_Size+1:end);
[~,VI_sortind] = sort(Validation_Ind);
[~,VI_rev_sortind] = sort(VI_sortind);
%initialization
Ur_rows_step = 100; % access the dataset 100 rows at a time
Ur_rows_grid=Ur_rows(1):Ur_rows_step:Ur_rows(2);
Delta_H_max = single(0);
for pp = 1:1:numel(Ur_rows_grid)-1 % loop for Normalizing H
clear DeepMIMO_dataset
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
for u=1:params.num_user
Hr = single(conj(DeepMIMO_dataset{1}.user{u}.channel));
Delta_H = max(max(abs(Ht.*Hr)));
if Delta_H >= Delta_H_max
Delta_H_max = single(Delta_H);
end
end
end
clear Delta_H
disp(‘=============================================================’);
disp([‘ Calculating for M_bar = ‘ num2str(M_bar)]);
Rand_M_bar =unique(Rand_M_bar_all(1:M_bar));
Ht_bar = reshape(Ht(Rand_M_bar,:),M_bar*K_DL,1);
DL_input = single(zeros(M_bar*K_DL*2,No_user_pairs));
DL_output = single(zeros(No_user_pairs,codebook_size));
DL_output_un= single(zeros(numel(Validation_Ind),codebook_size));
Delta_H_bar_max = single(0);
count=0;
for pp = 1:1:numel(Ur_rows_grid)-1
clear DeepMIMO_dataset
disp([‘Starting received user access ‘ num2str(pp)]);
params.active_user_first=Ur_rows_grid(pp);
params.active_user_last=Ur_rows_grid(pp+1)-1;
[DeepMIMO_dataset,params]=DeepMIMO_generator(params);
%% Construct Deep Learning inputs
u_step=100;
Htx=repmat(Ht(:,1),1,u_step);
Hrx=zeros(M,u_step);
for u=1:u_step:params.num_user
for uu=1:1:u_step
Hr = single(conj(DeepMIMO_dataset{1}.user{u+uu-1}.channel));
Hr_bar = reshape(Hr(Rand_M_bar,:),M_bar*K_DL,1);
%— Constructing the sampled channel
n1=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
n2=sqrt(noise_power_bar/2)*(randn(M_bar*K_DL,1)+1j*randn(M_bar*K_DL,1));
H_bar = ((Ht_bar+n1).*(Hr_bar+n2));
DL_input(:,u+uu-1+((pp-1)*params.num_user))= reshape([real(H_bar) imag(H_bar)].’,[],1);
Delta_H_bar = max(max(abs(H_bar)));
if Delta_H_bar >= Delta_H_bar_max
Delta_H_bar_max = single(Delta_H_bar);
end
Hrx(:,uu)=Hr(:,1);
end
%— Actual achievable rate for performance evaluation
H = Htx.*Hrx;
H_BF=H.’*BF_codebook;
SNR_sqrt_var = abs(H_BF);
for uu=1:1:u_step
if sum((Validation_Ind == u+uu-1+((pp-1)*params.num_user)))
count=count+1;
DL_output_un(count,:) = single(sum(log2(1+(SNR*((SNR_sqrt_var(uu,:)).^2))),1));
end
end
%— Label for the sampled channel
R = single(log2(1+(SNR_sqrt_var/Delta_H_max).^2));
% — DL output normalization
Delta_Out_max = max(R,[],2);
if ~sum(Delta_Out_max == 0)
Rn=diag(1./Delta_Out_max)*R;
end
DL_output(u+((pp-1)*params.num_user):u+((pp-1)*params.num_user)+u_step-1,:) = 1*Rn; %%%%% Normalized %%%%%
end
end
clear u Delta_H_bar R Rn
%– Sorting back the DL_output_un
DL_output_un = DL_output_un(VI_rev_sortind,:);
%— DL input normalization
DL_input= 1*(DL_input/Delta_H_bar_max); %%%%% Normalized from -1->1 %%%%%

%% DL Beamforming
% —————— Training and Testing Datasets —————–%
% Reshape for CNN-LSTM
% Assuming each sample is a sequence of features where each feature vector should be treated as a 1D image (sequence length x 1 x 1)

DL_output_reshaped = reshape(DL_output.’, size(DL_output,2), 1, 1, size(DL_output,1));
DL_output_reshaped_un = reshape(DL_output_un.’, size(DL_output_un,2), 1, 1, size(DL_output_un,1));
DL_input_reshaped = reshape(DL_input, size(DL_input,1), 1, 1, size(DL_input,2));

for dd=1:numel(Training_Size)
disp([‘ Calculating for Dataset Size = ‘ num2str(Training_Size(dd))]);
Training_Ind = RandP_all(1:Training_Size(dd));

% Index the reshaped data for training and validation
XTrain = single(DL_input_reshaped(:,1,:,Training_Ind));
YTrain = single(DL_output_reshaped(:,:,1,Training_Ind));
XValidation = single(DL_input_reshaped(:,1,:,Validation_Ind));
YValidation = single(DL_output_reshaped(:,:,1,Validation_Ind));
YValidation_un = single(DL_output_reshaped_un(:,:,1,:));

%% DL Model definition with adjusted pooling and convolution layers
layers = [
imageInputLayer([size(XTrain,1), 1, 1],’Name’,’input’,’Normalization’,’none’)

convolution2dLayer(3, 64, ‘Padding’, ‘same’, ‘Name’, ‘conv1’)
batchNormalizationLayer(‘Name’, ‘bn1’)
reluLayer(‘Name’, ‘relu1’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool1’)

convolution2dLayer(3, 128, ‘Padding’, ‘same’, ‘Name’, ‘conv2’)
batchNormalizationLayer(‘Name’, ‘bn2’)
reluLayer(‘Name’, ‘relu2’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool2’)

convolution2dLayer(3, 256, ‘Padding’, ‘same’, ‘Name’, ‘conv3’)
batchNormalizationLayer(‘Name’, ‘bn3’)
reluLayer(‘Name’, ‘relu3’)
maxPooling2dLayer([3,1], ‘Stride’, [3,1], ‘Name’, ‘maxpool3’)

flattenLayer(‘Name’, ‘flatten’)
lstmLayer(128, ‘Name’, ‘lstm1’, ‘OutputMode’, ‘sequence’)
lstmLayer(128, ‘Name’, ‘lstm2’, ‘OutputMode’, ‘last’)

fullyConnectedLayer(512, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu4’)
dropoutLayer(0.5, ‘Name’, ‘dropout1’)

fullyConnectedLayer(1024, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu5’)
dropoutLayer(0.5, ‘Name’, ‘dropout2’)

fullyConnectedLayer(2048, ‘Name’, ‘fc3’)
reluLayer(‘Name’, ‘relu6’)
dropoutLayer(0.5, ‘Name’, ‘dropout3’)

fullyConnectedLayer(size(YTrain,3), ‘Name’, ‘fc4’)
regressionLayer(‘Name’, ‘output’)
];

options = trainingOptions(‘rmsprop’, …
‘MiniBatchSize’, miniBatchSize, …
‘MaxEpochs’, 20, …
‘InitialLearnRate’, 1e-3, …
‘LearnRateSchedule’, ‘piecewise’, …
‘LearnRateDropFactor’, 0.5, …
‘LearnRateDropPeriod’, 10, …
‘L2Regularization’, 1e-4, …
‘Shuffle’, ‘every-epoch’, …
‘ValidationData’, {XValidation, YValidation}, …
‘ValidationFrequency’, 30, …
‘Verbose’, 1, …
‘Plots’, ‘none’, …
‘ExecutionEnvironment’, ‘cpu’);

[~,Indmax_OPT]= max(YValidation,[],3);
Indmax_OPT = squeeze(Indmax_OPT); %Upper bound on achievable rates
MaxR_OPT = single(zeros(numel(Indmax_OPT),1));
[trainedNet,traininfo] = trainNetwork(XTrain,YTrain,layers,options);
YPredicted = predict(trainedNet,XValidation);
% ——————— Achievable Rate ————————–%
[~,Indmax_DL] = maxk(YPredicted,kbeams,2);
MaxR_DL = single(zeros(size(Indmax_DL,1),1)); %True achievable rates
for b=1:size(Indmax_DL,1)
MaxR_DL(b) = max(squeeze(YValidation_un(1,1,Indmax_DL(b,:),b)));
MaxR_OPT(b) = squeeze(YValidation_un(1,1,Indmax_OPT(b),b));
end
Rate_OPT(dd) = mean(MaxR_OPT);
Rate_DL(dd) = mean(MaxR_DL);
LastValidationRMSE(dd) = traininfo.ValidationRMSE(end);
clear trainedNet traininfo YPredicted
clear layers options Rate_DL_Temp MaxR_DL_Temp Highest_Rate
end
end deep learning, cnn, communication MATLAB Answers — New Questions

​

I have the array y1 which consists of 5 sets, and each set consists of 6 elements. For example, the first set is 0.25 1.14 2.20 0.21 1.09 2.16. I need to make the last three elements in each set to be cross hatched with a specific color I choose.. how can I make it. my code is below
x=[1,2,3,4,5];
y1=[0.25 1.14 2.20 0.21 1.09 2.16 ; 0.48 2.26 4.40 0.42 2.20 4.34; 0.72 3.38 6.58 0.74 3.27 5.86 ;1.01 4.56 8.82 0.99 4.34 7.65;1.33 5.76 11.04 1.33 5.50 9.61 ]
figure
h1 = bar(y1);
set(h1, {‘DisplayName’}, {‘textbf{Proposed framework without AES}’,’textbf{Proposed framework with AES-128}’,’textbf{Proposed framework with AES-256}’,’textbf{Delay-energy-aware without AES}’,’textbf{Delay-energy-aware with AES-128}’,’textbf{Delay-energy-aware with AES-256}’}’)
set(gca,’TickLabelInterpreter’,’latex’, ‘LineWidth’, 1,’FontSize’,12, ‘YMinorTick’,’on’);
legend(‘Location’,’northwest’,’Interpreter’,’latex’, ‘FontWeight’,’bold’,’FontSize’,9.5,…
‘FontName’,’Palatino Linotype’,…
‘Location’,’best’);
xlabel(‘$textbf{Number of tasks}$’,’FontWeight’,’bold’,’FontSize’,12,…
‘FontName’,’Palatino Linotype’,’Interpreter’,’latex’);
ylabel(‘$textbf{Total delay [S]}$’,’FontWeight’,’bold’,’FontSize’,12,…
‘FontName’,’Palatino Linotype’,’Interpreter’,’latex’);I have the array y1 which consists of 5 sets, and each set consists of 6 elements. For example, the first set is 0.25 1.14 2.20 0.21 1.09 2.16. I need to make the last three elements in each set to be cross hatched with a specific color I choose.. how can I make it. my code is below
x=[1,2,3,4,5];
y1=[0.25 1.14 2.20 0.21 1.09 2.16 ; 0.48 2.26 4.40 0.42 2.20 4.34; 0.72 3.38 6.58 0.74 3.27 5.86 ;1.01 4.56 8.82 0.99 4.34 7.65;1.33 5.76 11.04 1.33 5.50 9.61 ]
figure
h1 = bar(y1);
set(h1, {‘DisplayName’}, {‘textbf{Proposed framework without AES}’,’textbf{Proposed framework with AES-128}’,’textbf{Proposed framework with AES-256}’,’textbf{Delay-energy-aware without AES}’,’textbf{Delay-energy-aware with AES-128}’,’textbf{Delay-energy-aware with AES-256}’}’)
set(gca,’TickLabelInterpreter’,’latex’, ‘LineWidth’, 1,’FontSize’,12, ‘YMinorTick’,’on’);
legend(‘Location’,’northwest’,’Interpreter’,’latex’, ‘FontWeight’,’bold’,’FontSize’,9.5,…
‘FontName’,’Palatino Linotype’,…
‘Location’,’best’);
xlabel(‘$textbf{Number of tasks}$’,’FontWeight’,’bold’,’FontSize’,12,…
‘FontName’,’Palatino Linotype’,’Interpreter’,’latex’);
ylabel(‘$textbf{Total delay [S]}$’,’FontWeight’,’bold’,’FontSize’,12,…
‘FontName’,’Palatino Linotype’,’Interpreter’,’latex’); I have the array y1 which consists of 5 sets, and each set consists of 6 elements. For example, the first set is 0.25 1.14 2.20 0.21 1.09 2.16. I need to make the last three elements in each set to be cross hatched with a specific color I choose.. how can I make it. my code is below
x=[1,2,3,4,5];
y1=[0.25 1.14 2.20 0.21 1.09 2.16 ; 0.48 2.26 4.40 0.42 2.20 4.34; 0.72 3.38 6.58 0.74 3.27 5.86 ;1.01 4.56 8.82 0.99 4.34 7.65;1.33 5.76 11.04 1.33 5.50 9.61 ]
figure
h1 = bar(y1);
set(h1, {‘DisplayName’}, {‘textbf{Proposed framework without AES}’,’textbf{Proposed framework with AES-128}’,’textbf{Proposed framework with AES-256}’,’textbf{Delay-energy-aware without AES}’,’textbf{Delay-energy-aware with AES-128}’,’textbf{Delay-energy-aware with AES-256}’}’)
set(gca,’TickLabelInterpreter’,’latex’, ‘LineWidth’, 1,’FontSize’,12, ‘YMinorTick’,’on’);
legend(‘Location’,’northwest’,’Interpreter’,’latex’, ‘FontWeight’,’bold’,’FontSize’,9.5,…
‘FontName’,’Palatino Linotype’,…
‘Location’,’best’);
xlabel(‘$textbf{Number of tasks}$’,’FontWeight’,’bold’,’FontSize’,12,…
‘FontName’,’Palatino Linotype’,’Interpreter’,’latex’);
ylabel(‘$textbf{Total delay [S]}$’,’FontWeight’,’bold’,’FontSize’,12,…
‘FontName’,’Palatino Linotype’,’Interpreter’,’latex’); hatched bars, matlab bars, matlab MATLAB Answers — New Questions

​

I am trying to model a 5 DOF robotic system to evaluate my driven kinematic and geometric modelling but this robot includes 5 kinematic chains that makes it somekind of complicated. Also the fact that I am new to robotic modelling adds up to it. I tried to design cad model of robot in Solidworks and import it to matlab simulink using
smimport
command but without getting any errors, my model doesn’t work properly. Everytime I try to fix the model and run the simulink model, I face new problems such as most of the joints not moving or broken assembly. Also I cannot add it to workspace with the command
importrobot
I get some errors like "Targets or motion inputs are specified for every joint around a kinematic loop". I’ll attach the picture of robot model, its simulink generated model and file of simulink and Solidworks model. I’ll be so grateful if anyone could help me or give me a hint.

and all of the related files are in a attached zip fileI am trying to model a 5 DOF robotic system to evaluate my driven kinematic and geometric modelling but this robot includes 5 kinematic chains that makes it somekind of complicated. Also the fact that I am new to robotic modelling adds up to it. I tried to design cad model of robot in Solidworks and import it to matlab simulink using
smimport
command but without getting any errors, my model doesn’t work properly. Everytime I try to fix the model and run the simulink model, I face new problems such as most of the joints not moving or broken assembly. Also I cannot add it to workspace with the command
importrobot
I get some errors like "Targets or motion inputs are specified for every joint around a kinematic loop". I’ll attach the picture of robot model, its simulink generated model and file of simulink and Solidworks model. I’ll be so grateful if anyone could help me or give me a hint.

and all of the related files are in a attached zip file I am trying to model a 5 DOF robotic system to evaluate my driven kinematic and geometric modelling but this robot includes 5 kinematic chains that makes it somekind of complicated. Also the fact that I am new to robotic modelling adds up to it. I tried to design cad model of robot in Solidworks and import it to matlab simulink using
smimport
command but without getting any errors, my model doesn’t work properly. Everytime I try to fix the model and run the simulink model, I face new problems such as most of the joints not moving or broken assembly. Also I cannot add it to workspace with the command
importrobot
I get some errors like "Targets or motion inputs are specified for every joint around a kinematic loop". I’ll attach the picture of robot model, its simulink generated model and file of simulink and Solidworks model. I’ll be so grateful if anyone could help me or give me a hint.

and all of the related files are in a attached zip file matlab, simulink, robotic, modelling, simulation, simscape, rigidboytree, robot MATLAB Answers — New Questions

​