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main02_TrainNetwork.m
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% These MATLAB scripts are prepared by A.M.E for the following paper,
% Ahmet M. Elbir, "CNN-based Precoder and Combiner Design in mmWave MIMO Systems", IEEE Communications Letters, in press.
% please cite the above work if you use this file. For any comments and
% questions please email: ahmetmelbir@gmail.com
% clearvars -except fileNameLabelsTrain
% fileNameLabelsTrain = 'scenarioID_52_52'; % Train data.
% load(fileNameLabelsTrain)
%% CNN AS classification type
dataAntennaSelection = XAS;
labelsAntennaSelection = categorical(Y);
sizeInputAntennaSelection = size(dataAntennaSelection);
Qf = numel(categories(labelsAntennaSelection));
%% CNN RF for regression type
dataFRFChainSelection = XRF;
labelsRFChainSelection = YFRFr;
sizeInputFRFChainSelection = size(dataFRFChainSelection);
sizeOutputFRFChainSelection = size(labelsRFChainSelection);
dataWRFChainSelection = XRF;
labelsWRFChainSelection = YWRFr;
sizeInputWRFChainSelection = size(dataWRFChainSelection);
sizeOutputWRFChainSelection = size(labelsWRFChainSelection);
%% val. for regression.
idx = randperm(size(dataAntennaSelection,4),floor(.3*sizeInputAntennaSelection(end)));
valDataAntennaSelection = dataAntennaSelection(:,:,:,idx);
valLabelsAntennaSelection = labelsAntennaSelection(:,idx);
dataAntennaSelection(:,:,:,idx) = [];
labelsAntennaSelection(:,idx) = [];
idx = randperm(size(dataFRFChainSelection,4),floor(.3*sizeInputFRFChainSelection(end)));
valDataRFChainSelection = dataFRFChainSelection(:,:,:,idx);
valLabelsFRFChainSelection = labelsRFChainSelection(idx,:);
dataFRFChainSelection(:,:,:,idx) = [];
labelsRFChainSelection(idx,:) = [];
idx = randperm(size(dataFRFChainSelection,4),floor(.3*sizeInputFRFChainSelection(end)));
valDataWRFChainSelection = dataWRFChainSelection(:,:,:,idx);
valLabelsWRFChainSelection = labelsWRFChainSelection(idx,:);
dataWRFChainSelection(:,:,:,idx) = [];
labelsWRFChainSelection(idx,:) = [];
%% settings.
miniBatchSize = 200;
numValidationsPerEpoch = 5000;
validationFrequency = 50;
%% DNN Layers.
layersAntennaSelection = [imageInputLayer([sizeInputAntennaSelection(1:3)],'Normalization', 'zerocenter');
convolution2dLayer(2,2^6);
batchNormalizationLayer
reluLayer();
% maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(2,2^6);
batchNormalizationLayer
reluLayer();
% maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(2,2^6);
batchNormalizationLayer
reluLayer();
% maxPooling2dLayer(2,'Stride',2);
% convolution2dLayer(2,2^6);
% batchNormalizationLayer
% reluLayer();
fullyConnectedLayer(2^7);
reluLayer();
dropoutLayer();
fullyConnectedLayer(2^7);
reluLayer();
dropoutLayer();
fullyConnectedLayer(Qf);
softmaxLayer();
classificationLayer();
]
optsAntennaSelection = trainingOptions('sgdm',...
'Momentum', 0.9,...
'InitialLearnRate',0.01,... % The default value is 0.01.
'MaxEpochs',5000,...
'MiniBatchSize',miniBatchSize,... % The default is 128.
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',0.9,...
'LearnRateDropPeriod',30,...
'L2Regularization',0.0001,... % The default is 0.0001.
'ExecutionEnvironment', 'auto',...
'ValidationData',{valDataAntennaSelection,valLabelsAntennaSelection},...
'ValidationFrequency',validationFrequency,...
'ValidationPatience', 200,...
'Plots','none',...
'Shuffle','every-epoch',...
'OutputFcn',@(info)stopIfAccuracyNotImproving(info,3));
fprintf(2,['Train CNN For Antenna Selection\n'])
%%
warning off parallel:gpu:device:DeviceLibsNeedsRecompiling
try
gpuArray.eye(2)^2;
catch ME
end
try
nnet.internal.cnngpu.reluForward(1);
catch ME
end
%%
convnetAntennaSelection = trainNetwork(dataAntennaSelection, labelsAntennaSelection, layersAntennaSelection, optsAntennaSelection);
%% DNN for HB.
layersFRFChainSelection = [imageInputLayer([sizeInputFRFChainSelection(1:3)],'Normalization', 'zerocenter');
% convolution2dLayer(2^1,2^3);
% batchNormalizationLayer
% reluLayer();
% maxPooling2dLayer([2 2],'Stride',2);
% convolution2dLayer(2^1,2^3);
% batchNormalizationLayer
% reluLayer();
% convolution2dLayer(2,2^6);
% batchNormalizationLayer
% reluLayer();
% fullyConnectedLayer(2^10);
fullyConnectedLayer(2^13);
% reluLayer();
% dropoutLayer();
fullyConnectedLayer(2^8);
% fullyConnectedLayer(2^7);
% reluLayer();
% dropoutLayer();
% fullyConnectedLayer(QFRF);
% softmaxLayer();
% classificationLayer();
fullyConnectedLayer(sizeOutputFRFChainSelection(2))
regressionLayer()
];
optsFRFSelection = trainingOptions('sgdm',...
'Momentum', 0.9,...
'InitialLearnRate',0.0005,... % The default value is 0.01.
'MaxEpochs',5000,...
'MiniBatchSize',miniBatchSize,... % The default is 128.
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',.9,...
'LearnRateDropPeriod',30,...
'L2Regularization',0.0001,... % The default is 0.0001.
'ExecutionEnvironment', 'auto',...
'ValidationData',{valDataRFChainSelection,valLabelsFRFChainSelection},...
'ValidationFrequency',validationFrequency,...
'ValidationPatience', 200,...
'Plots','none',...
'Shuffle','every-epoch',...
'OutputFcn',@(info)stopIfAccuracyNotImproving(info,3));
fprintf(2,['Train CNN For Frf \n'])
timeCNNRF = tic;
convnetFRFSelection = trainNetwork(dataFRFChainSelection, labelsRFChainSelection, layersFRFChainSelection, optsFRFSelection);
% stopp
layersWRFChainSelection = [layersFRFChainSelection(1:end-2);
fullyConnectedLayer(sizeOutputWRFChainSelection(2))
regressionLayer()
];
optsWRFSelection = trainingOptions('sgdm',...
'Momentum', 0.9,...
'InitialLearnRate',0.0005,... % The default value is 0.01.
'MaxEpochs',5000,...
'MiniBatchSize',miniBatchSize,... % The default is 128.
'LearnRateSchedule','piecewise',...
'LearnRateDropFactor',.9,...
'LearnRateDropPeriod',30,...
'L2Regularization',0.0001,... % The default is 0.0001.
'ExecutionEnvironment', 'auto',...
'ValidationData',{valDataWRFChainSelection,valLabelsWRFChainSelection},...
'ValidationFrequency',validationFrequency,...
'ValidationPatience', 200,...
'Plots','none',...
'Shuffle','every-epoch',...
'OutputFcn',@(info)stopIfAccuracyNotImproving(info,3));
fprintf(2,['Train CNN For Wrf \n'])
convnetWRFSelection = trainNetwork(dataWRFChainSelection, labelsWRFChainSelection, layersWRFChainSelection, optsWRFSelection);
timeCNNRF = toc(timeCNNRF)
% stopp
% xLabels(:,1) = valLabelsAntennaSelection;
% xLabels(:,2) = classify(convnetAntennaSelection,valDataAntennaSelection);
% accuracySourceVal = [mean(xLabels(:,2) == xLabels(:,1))];
%% Save.
% stopp
% load('scenarioIndex')
% iExp = iExp + 1;
% fileNameSavedNetwork = ['scenarioID_' num2str(fileNameLabelsTrain(12:end)), '_Exp' num2str(iExp)];
% fileNameLabelsTrainNet = varToSave(iLabels,iTrain,iExp,fileNameSavedNetwork,NtRF,Nr,NrRF,Nrs,...
% bestAntennas,subSet,XAS,XRF,Y,YFRFr,YWRFr,Z,opts,convnetAntennaSelection,convnetFRFSelection,convnetWRFSelection,idx);
% save('scenarioIndex','iLabels','iTrain','iExp')
% fileNameLabelsTrainNet
% %% Run performance test
% beep
run main03_TestNetwork.m