How to turn the data in mini batch into a deep learning array

I am using a deep learning network to predict the distanes of the codewords of a for wireless communications systems. I am trying to update the parameters of my network with the adam algorithm.

I need help of how to turn the data inside the mini batch into data for the dl array.

Thank you in advance

clc
clear all 

S=100E3;
SNR=10;

s_1=-1i;              % these are used as the labels
s_2=-1;
s_3=1i;
s_4=1;

m=round(rand(1,2*S));
A=reshape(m,2,S);     % the 1x2S row vector is transformed into a 2xS matrix A

x = zeros(1,6);
s = 0;
s = s+(sum(abs(A-[0;0]*ones(1,S)))<0.1)*s_1;
s = s+(sum(abs(A-[0;1]*ones(1,S)))<0.1)*s_2;
s = s+(sum(abs(A-[1;1]*ones(1,S)))<0.1)*s_3;
s = s+(sum(abs(A-[1;0]*ones(1,S)))<0.1)*s_4; % label keeps track of what is originally transmitted
s_= reshape(s,2,S/2);

    %% Generation of the complex channel
    H = (randn(2,S)+1i*randn(2,S))/2; % Normalised 2x2 Rayleigh MIMO fading channel 
    n=(randn(2,S/2)+1i*randn(2,S/2))/sqrt(2*10^(SNR/10));
    for (k=1:S/2)
         nchr_deta= [s_1 s_2 s_3 s_4];
         H_ = H(:,2*k-1:2*k); 
%         x(:,k)=H_*s_(:,k);                                                                  % This line has been added to compute the numerical SNR: 10*log10(var(reshape(x,1,S))/var(reshape(n,1,S))) (not necessary otherwi
         
         y = H_*s_(:,k)+ n(:,k);
         
         y_arrayReal(:,k) = real(y); %real values of y
         y_arrayImag(:,k) = imag(y); %imaginary values of y
         H_arrayReal(:,k) = real(H_(1,1));
         H_arrayImag(:,k) = imag(H_(1,1));
         
         %% x processsing
         x(k,1:2) = [y_arrayReal(1,k) y_arrayReal(2,k)];         
         x(k,3:4) = [y_arrayImag(1,k) y_arrayImag(2,k)];
         x(k,5)   = H_arrayReal(k);
         x(k,6)   = H_arrayImag(k);
         
         %Gerenation of the list of radius for the DNN using MMSE 
        W_MMSE=(H_'*H_+10^(-SNR/10)*eye(2,2))^-1*H_';                            % MMSE equalisation matrix
        
        s_hat_MMSE(:,k)=W_MMSE*y; 
        outDNN(k,:) = s_hat_MMSE(:,k); %output of the DNN 
        
        
    end 
out1 = [real(outDNN(:,1)),imag(outDNN(:,1)),real(outDNN(:,2)),imag(outDNN(:,2))]; %outg of the DNN

%% Defining the network  


layers = [
    
    imageInputLayer([50000 6],'Name','Input layer','Mean',ones([50000 6]))
    convolution2dLayer([10000 6],5,'Name','conv1')
    reluLayer('Name', 'relu1')
%     convolution2dLayer([10 6], 1,'Name','conv2')
%     reluLayer('Name','relu2')
%     convolution2dLayer([1000 6],1,'Name','conv3')
%     reluLayer('Name','relu3')
    fullyConnectedLayer(5,'Name','Output layer');
    ]
lgraph = layerGraph(layers);

%% Creation of the dl network 
dlnet = dlnetwork(lgraph);

%% Specifying the training options 
miniBatchSize = 128;
numEpochs = 20;
numObservations = numel(x);
numIterationsPerEpoch = floor(numObservations./miniBatchSize);

%% Initialising average gradient, squared average gradient and iteration counter
averageGrad = [];
averageSqGrad = [];
iteration = 1;

%% Initialise the training progress plot
plots = "training-progress";
if plots == "training-progress"
    figure
    lineLossTrain = animatedline;
    xlabel("Total Iterations")
    ylabel("Loss")
end

%% Train the network while updating its parameters using adamupdate funtion
for epoch = 1:numEpochs
     % Convert mini-batch of data to dlarray.
           X = dlarray(x(:,:,:,idx),'SSCB');
           
           % Evaluate the model gradients using dlfeval and the
           % modelGradients function defined below.
           gradients = dlfeval(@modelGradients,net,X,(out1:,idx));
%Update the network using adam optimiser 
[dlnet,averageGrad,averageSqGrad] = adamupdate(dlnet,grad,averageGrad,averageSqGrad,iteration);

% Display the training progress.
        if plots == "training-progress"
            addpoints(lineLossTrain,iteration,double(gather(extractdata(loss))))
            title("Loss During Training: Epoch - " + epoch + "; Iteration - ")
            drawnow
        end
% Increment the iteration counter.
        iteration = iteration + 1;
    end

Answer 1

[Divya Gaddipati]

dlarray is used to convert the data to deep learning array, which you are already doing here:

X = dlarray(x(:,:,:,idx),'SSCB');

You can refer to the following link for more information

https://www.mathworks.com/help/deeplearning/ref/dlarray.html