MATLAB EXAMPE RUNS FAILED

Question

When I try to run the exampe: ModulationClassificationWithDeepLearningExample, this error accurs:
"类 'helperModClassFrameStore' 中的属性 'OutputFormat' 的默认值无效:
无法解析名称 FrameStoreOutputFormat.IQAsRows。"
matlab version:2019b
How can I solve this poblem?
Thanks a lot for your answers!

刘 檬 · Accepted Answer

I had the same problem,but i did`nt find the real reason,i think you write the wrong code or less a line code,you can try this code.
% 使用深度学习进行调制分类 —— CNN训 练部分
% 您将生成合成的、信道减损的波形。使用生成的波形作为训练数据，训练 CNN 进行调制分类。
% 
% 目录
% 1 生成用于训练的波形
%     1.1 信道创建
%         1.1.1 AWGN
%         1.1.2 莱斯多径衰落
%         1.1.3 时钟偏移
%         1.1.4 合成后的信道
%     1.2 波形生成
% 2 训练 CNN
% 3 进一步探查
% 4 参考文献
% 5 附录：辅助函数
% 6 附录：调制器
% 
% 
clear all;
clc;


%% 1 生成用于训练的波形
% 为每种调制类型生成 10000 个帧，其中 80% 用于训练，10% 用于验证，10% 用于测试。我们在网络训练阶段使用训练和验证帧。使用测试帧获得最终分类准确度。每帧的长度为 1024 个样本，采样率为 200 kHz。对于数字调制类型，八个样本表示一个符号。网络根据单个帧而不是多个连续帧（如视频）作出每个决定。假设数字和模拟调制类型的中心频率分别为 900 MHz 和 100 MHz。
% 要快速运行此示例，请使用经过训练的网络并生成少量训练帧。要在计算机上训练网络，请选择 “train network now” 选项(即将 trainNow 设置为true)。
trainNow = false;
if trainNow == true
  numFramesPerModType = 100;
else
  numFramesPerModType = 100;
end
percentTrainingSamples = 80;
percentValidationSamples = 10;
percentTestSamples = 10;

sps = 8;                % Samples per symbol
spf = 1024;             % Samples per frame
symbolsPerFrame = spf / sps;
fs = 200e3;             % Sample rate
fc = [902e6 100e6];     % Center frequencies

%% 1.1 信道创建
% 让每帧通过信道并具有
% AWGN
% 莱斯多径衰落
% 时钟偏移，导致中心频率偏移和采样时间偏移
% 由于本示例中的网络基于单个帧作出决定，因此每个帧必须通过独立的信道

%% 1.1.1 AWGN
% 添加 SNR 为 30 dB 的 AWGN。由于帧经过归一化，因此噪声标准差可以计算为
SNR = 30;
std = sqrt(10.^(-SNR/10))
% 使用 comm.AWGNChannel 实现
awgnChannel = comm.AWGNChannel(...
  'NoiseMethod', 'Signal to noise ratio (SNR)', ...
  'SignalPower', 1, ...
  'SNR', SNR);

%% 1.1.2 莱斯多径衰落
% 使用 comm.RicianChannel System object 实现通过莱斯多径衰落信道。假设延迟分布为 [0 1.8 3.4] 个样本，对应的平均路径增益为 [0 -2 -10] dB。K 因子为 4，最大多普勒频移为 4 Hz，等效于 900 MHz 的步行速度。使用以下设置实现信道。
multipathChannel = comm.RicianChannel(...
  'SampleRate', fs, ...
  'PathDelays', [0 1.8 3.4]/fs, ...
  'AveragePathGains', [0 -2 -10], ...
  'KFactor', 4, ...
  'MaximumDopplerShift', 4);

%% 1.1.3 时钟偏移
% .时钟偏移是发送器和接收器的内部时钟源不准确造成的。时钟偏移导致中心频率（用于将信号下变频至基带）和数模转换器采样率不同于理想值。信道仿真器使用时钟偏移因子 C，表示为 C=1+Δclock106，其中 Δclock 是时钟偏移。对于每个帧，通道基于 [−maxΔclock maxΔclock] 范围内一组均匀分布的值生成一个随机 Δclock 值，其中 maxΔclock 是最大时钟偏移。时钟偏移以百万分率 (ppm) 为单位测量。对于本示例，假设最大时钟偏移为 5 ppm。
maxDeltaOff = 5;
deltaOff = (rand()*2*maxDeltaOff) - maxDeltaOff;
C = 1 + (deltaOff/1e6);
% 频率偏移
% 基于时钟偏移因子 C 和中心频率，对每帧进行频率偏移。使用 comm.PhaseFrequencyOffset 实现信道。
offset = -(C-1)*fc(1);
frequencyShifter = comm.PhaseFrequencyOffset(...
  'SampleRate', fs, ...
  'FrequencyOffset', offset);
% 采样率偏移
% 基于时钟偏移因子 C，对每帧进行采样率偏移。使用 interp1 函数实现通道，以 C×fs 的新速率对帧进行重新采样。

%% 1.1.4 合成后的信道
% 使用 helperModClassTestChannel 对象对帧应用所有三种信道衰落
channel = helperModClassTestChannel(...
  'SampleRate', fs, ...
  'SNR', SNR, ...
  'PathDelays', [0 1.8 3.4] / fs, ...
  'AveragePathGains', [0 -2 -10], ...
  'KFactor', 4, ...
  'MaximumDopplerShift', 4, ...
  'MaximumClockOffset', 5, ...
  'CenterFrequency', 902e6)
% 您可以使用 info 对象函数查看有关通道的基本信息。
chInfo = info(channel);



%% 1.2 波形生成
% 创建一个循环，它为每种调制类型生成信道衰落的帧，并将这些帧及其对应标签存储在 frameStore 中。从每帧的开头删除随机数量的样本，以去除瞬变并确保帧相对于符号边界具有随机起点。
% Set the random number generator to a known state to be able to regenerate
% the same frames every time the simulation is run
rng(1235)
tic

modulationTypes = categorical(["BPSK", "QPSK", "8PSK", ...
  "16QAM", "64QAM", "PAM4", "GFSK", "CPFSK", ...
  "B-FM", "DSB-AM", "SSB-AM"]);

numModulationTypes = length(modulationTypes);

channelInfo = info(channel);
frameStore = helperModClassFrameStore(...
  numFramesPerModType*numModulationTypes,spf,modulationTypes);
transDelay = 50;


for modType = 1:numModulationTypes
  fprintf('%s - Generating %s frames
', ...
    datestr(toc/86400,'HH:MM:SS'), modulationTypes(modType))
  numSymbols = (numFramesPerModType / sps);
  dataSrc = getSource(modulationTypes(modType), sps, 2*spf, fs);
  modulator = getModulator(modulationTypes(modType), sps, fs);
  if contains(char(modulationTypes(modType)), {'B-FM','DSB-AM','SSB-AM'})
    % Analog modulation types use a center frequency of 100 MHz
    channel.CenterFrequency = 100e6;
  else
    % Digital modulation types use a center frequency of 902 MHz
    channel.CenterFrequency = 902e6;
  end
  
  for p=1:numFramesPerModType
    % Generate random data
    x = dataSrc();
    
    % Modulate
    y = modulator(x);
    
    % Pass through independent channels
    rxSamples = channel(y);
    
    % Remove transients from the beginning, trim to size, and normalize
    frame = helperModClassFrameGenerator(rxSamples, spf, spf, transDelay, sps);
    
    % Add to frame store
    add(frameStore, frame, modulationTypes(modType));
  end
end

% 接下来，将帧分为训练数据、验证数据和测试数据。默认情况下，frameStore 将 I/Q 基带样本按行放置在输出帧中。输出帧的大小为 [2xspf×1×N]，其中第一行是同相采样，第二行是正交采样。
[mcfsTraining,mcfsValidation,mcfsTest] = splitData(frameStore,...
  [percentTrainingSamples,percentValidationSamples,percentTestSamples]);
[rxTraining,rxTrainingLabel] = get(mcfsTraining);
[rxValidation,rxValidationLabel] = get(mcfsValidation);
[rxTest,rxTestLabel] = get(mcfsTest);


size(rxTraining)

% Plot the amplitude of the real and imaginary parts of the example frames
% against the sample number
plotTimeDomain(rxTest,rxTestLabel,modulationTypes,fs)

% Plot a spectrogram of the example frames
plotSpectrogram(rxTest,rxTestLabel,modulationTypes,fs,sps)

% 通过确保标签（调制类型）分布均匀，避免训练数据中的类不平衡。绘制标签分布图，以检查生成的标签是否分布均匀。
% Plot the label distributions
figure
subplot(3,1,1)
histogram(rxTrainingLabel)
title("Training Label Distribution")
subplot(3,1,2)
histogram(rxValidationLabel)
title("Validation Label Distribution")
subplot(3,1,3)
histogram(rxTestLabel)
title("Test Label Distribution")



%% 2 训练 CNN
% 本示例使用的 CNN 由六个卷积层和一个全连接层组成。除最后一个卷积层外，每个卷积层后面都有一个批量归一化层、修正线性单元 (ReLU) 激活层和最大池化层。在最后一个卷积层中，最大池化层被一个平均池化层取代。输出层具有 softmax 激活。有关网络设计指导原则，请参阅Deep Learning Tips and Tricks。
dropoutRate = 0.5;
numModTypes = numel(modulationTypes);
netWidth = 1;
filterSize = [1 sps];
poolSize = [1 2];
modClassNet = [
  imageInputLayer([2 spf 1], 'Normalization', 'none', 'Name', 'Input Layer')
  
  convolution2dLayer(filterSize, 16*netWidth, 'Padding', 'same', 'Name', 'CNN1')
  batchNormalizationLayer('Name', 'BN1')
  reluLayer('Name', 'ReLU1')
  maxPooling2dLayer(poolSize, 'Stride', [1 2], 'Name', 'MaxPool1')
  
  convolution2dLayer(filterSize, 24*netWidth, 'Padding', 'same', 'Name', 'CNN2')
  batchNormalizationLayer('Name', 'BN2')
  reluLayer('Name', 'ReLU2')
  maxPooling2dLayer(poolSize, 'Stride', [1 2], 'Name', 'MaxPool2')
  
  convolution2dLayer(filterSize, 32*netWidth, 'Padding', 'same', 'Name', 'CNN3')
  batchNormalizationLayer('Name', 'BN3')
  reluLayer('Name', 'ReLU3')
  maxPooling2dLayer(poolSize, 'Stride', [1 2], 'Name', 'MaxPool3')
  
  convolution2dLayer(filterSize, 48*netWidth, 'Padding', 'same', 'Name', 'CNN4')
  batchNormalizationLayer('Name', 'BN4')
  reluLayer('Name', 'ReLU4')
  maxPooling2dLayer(poolSize, 'Stride', [1 2], 'Name', 'MaxPool4')
  
  convolution2dLayer(filterSize, 64*netWidth, 'Padding', 'same', 'Name', 'CNN5')
  batchNormalizationLayer('Name', 'BN5')
  reluLayer('Name', 'ReLU5')
  maxPooling2dLayer(poolSize, 'Stride', [1 2], 'Name', 'MaxPool5')
  
  convolution2dLayer(filterSize, 96*netWidth, 'Padding', 'same', 'Name', 'CNN6')
  batchNormalizationLayer('Name', 'BN6')
  reluLayer('Name', 'ReLU6')
  
  averagePooling2dLayer([1 ceil(spf/32)], 'Name', 'AP1')
  
  fullyConnectedLayer(numModTypes, 'Name', 'FC1')
  softmaxLayer('Name', 'SoftMax')
  
  classificationLayer('Name', 'Output') ]

% 接下来配置 TrainingOptionsSGDM 以使用小批量大小为 256 的 SGDM 求解器。将最大轮数设置为 12，因为更多轮数不会提供进一步的训练优势。通过将执行环境设置为 'gpu'，在 GPU 上训练网络。将初始学习率设置为 2x10−2。每 9 轮后将学习率降低十分之一。将 'Plots' 设置为“training-progress' 以对训练进度绘图。在 NVIDIA Titan Xp GPU 上，网络需要大约 25 分钟来完成训练。
maxEpochs = 12;
miniBatchSize = 256;
validationFrequency = floor(numel(rxTrainingLabel)/miniBatchSize);
options = trainingOptions('sgdm', ...
  'InitialLearnRate',2e-2, ...
  'MaxEpochs',maxEpochs, ...
  'MiniBatchSize',miniBatchSize, ...
  'Shuffle','every-epoch', ...
  'Plots','training-progress', ...
  'Verbose',false, ...
  'ValidationData',{rxValidation,rxValidationLabel}, ...
  'ValidationFrequency',validationFrequency, ...
  'LearnRateSchedule', 'piecewise', ...
  'LearnRateDropPeriod', 9, ...
  'LearnRateDropFactor', 0.1, ...
  'ExecutionEnvironment', 'gpu');


% 或者训练网络，或者使用已经过训练的网络。默认情况下，此示例使用经过训练的网络。
if trainNow == true
  fprintf('%s - Training the network
', datestr(toc/86400,'HH:MM:SS'))
  trainedNet = trainNetwork(rxTraining,rxTrainingLabel,modClassNet,options);
else
  load trainedModulationClassificationNetwork
end
% 如训练进度图所示，网络在大约 12 轮后收敛于几乎 90% 的准确度。


% 通过获得测试帧的分类准确度来评估经过训练的网络。结果表明，该网络对这组波形实现的准确度达到 95% 左右。
fprintf('%s - Classifying test frames
', datestr(toc/86400,'HH:MM:SS'))
rxTestPred = classify(trainedNet,rxTest);
testAccuracy = mean(rxTestPred == rxTestLabel);
disp("Test accuracy: " + testAccuracy*100 + "%")

% 绘制测试帧的混淆矩阵。如矩阵所示，网络混淆了 16-QAM 和 64-QAM 帧。此问题是预料之中的，因为每个帧只携带 128 个符号，而 16-QAM 是 64-QAM 的子集。该网络还混淆了 QPSK 和 8-PSK 帧，因为在信道衰落和频率偏移引发相位旋转后，这些调制类型的星座图看起来相似。
figure
cm = confusionchart(rxTestLabel, rxTestPred);
cm.Title = 'Confusion Matrix for Test Data';
cm.RowSummary = 'row-normalized';
cm.Parent.Position = [cm.Parent.Position(1:2) 740 424];

%% 3 进一步探查
% 要提高准确度，可以优化网络参数，例如过滤器数量、过滤器大小；或者优化网络结构，例如添加更多层，使用不同激活层等。
% Communication Toolbox 提供了更多调制类型和信道减损。有关详细信息，请参阅Modulation (Communications Toolbox) 和 部分。您还可以使用 LTE Toolbox、WLAN Toolbox 和 5G Toolbox 添加标准特定信号。您还可以使用 Phased Array System Toolbox 添加雷达信号。
% 附录：“调制器”部分提供用于生成调制信号的 MATLAB 函数。您还可以探查以下函数和 System object 以获得详细信息：
% helperModClassTestChannel.m
% helperModClassFrameGenerator.m
% helperModClassFrameStore.m
% 4 参考文献
% O'Shea, T. J., J. Corgan, and T. C. Clancy. "Convolutional Radio Modulation Recognition Networks." Preprint, submitted June 10, 2016. https://arxiv.org/abs/1602.04105
% O'Shea, T. J., T. Roy, and T. C. Clancy. "Over-the-Air Deep Learning Based Radio Signal Classification." IEEE Journal of Selected Topics in Signal Processing. Vol. 12, Number 1, 2018, pp. 168–179.
% Liu, X., D. Yang, and A. E. Gamal. "Deep Neural Network Architectures for Modulation Classification." Preprint, submitted January 5, 2018. https://arxiv.org/abs/1712.00443v3

%% 5 附录：辅助函数
function modulator = getModulator(modType, sps, fs)
%getModulator Modulation function selector
%   MOD = getModulator(TYPE,SPS,FS) returns the modulator function handle
%   MOD based on TYPE. SPS is the number of samples per symbol and FS is
%   the sample rate.

switch modType
  case "BPSK"
    modulator = @(x)bpskModulator(x,sps);
  case "QPSK"
    modulator = @(x)qpskModulator(x,sps);
  case "8PSK"
    modulator = @(x)psk8Modulator(x,sps);
  case "16QAM"
    modulator = @(x)qam16Modulator(x,sps);
  case "64QAM"
    modulator = @(x)qam64Modulator(x,sps);
  case "GFSK"
    modulator = @(x)gfskModulator(x,sps);
  case "CPFSK"
    modulator = @(x)cpfskModulator(x,sps);
  case "PAM4"
    modulator = @(x)pam4Modulator(x,sps);
  case "B-FM"
    modulator = @(x)bfmModulator(x, fs);
  case "DSB-AM"
    modulator = @(x)dsbamModulator(x, fs);
  case "SSB-AM"
    modulator = @(x)ssbamModulator(x, fs);
end
end

function src = getSource(modType, sps, spf, fs)
%getSource Source selector for modulation types
%    SRC = getSource(TYPE,SPS,SPF,FS) returns the data source
%    for the modulation type TYPE, with the number of samples
%    per symbol SPS, the number of samples per frame SPF, and
%    the sampling frequency FS.

switch modType
  case {"BPSK","GFSK","CPFSK"}
    M = 2;
    src = @()randi([0 M-1],spf/sps,1);
  case {"QPSK","PAM4"}
    M = 4;
    src = @()randi([0 M-1],spf/sps,1);
  case "8PSK"
    M = 8;
    src = @()randi([0 M-1],spf/sps,1);
  case "16QAM"
    M = 16;
    src = @()randi([0 M-1],spf/sps,1);
  case "64QAM"
    M = 64;
    src = @()randi([0 M-1],spf/sps,1);
  case {"B-FM","DSB-AM","SSB-AM"}
    src = @()getAudio(spf,fs);
end
end

function x = getAudio(spf,fs)
%getAudio Audio source for analog modulation types
%    A = getAudio(SPF,FS) returns the audio source A, with the
%    number of samples per frame SPF, and the sample rate FS.

persistent audioSrc audioRC

if isempty(audioSrc)
  audioSrc = dsp.AudioFileReader('audio_mix_441.wav',...
    'SamplesPerFrame',spf,'PlayCount',inf);
  audioRC = dsp.SampleRateConverter('Bandwidth',30e3,...
    'InputSampleRate',audioSrc.SampleRate,...
    'OutputSampleRate',fs);
  [~,decimFactor] = getRateChangeFactors(audioRC);
  audioSrc.SamplesPerFrame = ceil(spf / fs * audioSrc.SampleRate / decimFactor) * decimFactor;
end

x = audioRC(audioSrc());
x = x(1:spf,1);
end

function frames = getNNFrames(rx,modType)
%getNNFrames Generate formatted frames for neural networks
%   F = getNNFrames(X,MODTYPE) formats the input X, into frames
%   that can be used with the neural network designed in this
%   example, and returns the frames in the output F.

frames = helperModClassFrameGenerator(rx,1024,1024,32,8);
frameStore = helperModClassFrameStore(10,1024,categorical({modType}));
add(frameStore,frames,modType);
frames = get(frameStore);
end

function plotScores(score,labels)
%plotScores Plot classification scores of frames
%   plotScores(SCR,LABELS) plots the classification scores SCR as a stacked
%   bar for each frame. SCR is a matrix in which each row is the score for a
%   frame.

co = [0.08 0.9 0.49;
  0.52 0.95 0.70;
  0.36 0.53 0.96;
  0.09 0.54 0.67;
  0.48 0.99 0.26;
  0.95 0.31 0.17;
  0.52 0.85 0.95;
  0.08 0.72 0.88;
  0.12 0.45 0.69;
  0.22 0.11 0.49;
  0.65 0.54 0.71];
figure; ax = axes('ColorOrder',co,'NextPlot','replacechildren');
bar(ax,[score; nan(2,11)],'stacked'); legend(categories(labels),'Location','best');
xlabel('Frame Number'); ylabel('Score'); title('Classification Scores')
end

function plotTimeDomain(rxTest,rxTestLabel,modulationTypes,fs)
%plotTimeDomain Time domain plots of frames

numRows = ceil(length(modulationTypes) / 4);
spf = size(rxTest,2);
t = 1000*(0:spf-1)/fs;
if size(rxTest,1) == 2
  IQAsRows = true;
else
  IQAsRows = false;
end
for modType=1:length(modulationTypes)
    subplot(numRows, 4, modType);
  idxOut = find(rxTestLabel == modulationTypes(modType), 1);
  if IQAsRows
    rxI = rxTest(1,:,1,idxOut);
    rxQ = rxTest(2,:,1,idxOut);
  else
    rxI = rxTest(1,:,1,idxOut);
    rxQ = rxTest(1,:,2,idxOut);
  end
  plot(t,squeeze(rxI), '-'); grid on; axis equal; axis square
  hold on
  plot(t,squeeze(rxQ), '-'); grid on; axis equal; axis square
  hold off
  title(string(modulationTypes(modType)));
  xlabel('Time (ms)'); ylabel('Amplitude')
end
end

function plotSpectrogram(rxTest,rxTestLabel,modulationTypes,fs,sps)
%plotSpectrogram Spectrogram of frames

if size(rxTest,1) == 2
  IQAsRows = true;
else
  IQAsRows = false;
end
numRows = ceil(length(modulationTypes) / 4);
for modType=1:length(modulationTypes)
  subplot(numRows, 4, modType);
  idxOut = find(rxTestLabel == modulationTypes(modType), 1);
  if IQAsRows
    rxI = rxTest(1,:,1,idxOut);
    rxQ = rxTest(2,:,1,idxOut);
  else
    rxI = rxTest(1,:,1,idxOut);
    rxQ = rxTest(1,:,2,idxOut);
  end
  rx = squeeze(rxI) + 1i*squeeze(rxQ);
  spectrogram(rx,kaiser(sps),0,1024,fs,'centered');
  title(string(modulationTypes(modType)));
end
h = gcf; delete(findall(h.Children, 'Type', 'ColorBar'))
end

function flag = isPlutoSDRInstalled
%isPlutoSDRInstalled Check if ADALM-PLUTO Radio HSP is installed

spkg = matlabshared.supportpkg.getInstalled;
flag = ~isempty(spkg) && any(contains({spkg.Name},'ADALM-PLUTO','IgnoreCase',true));
end

function flag = isUSRPInstalled
%isUSRPInstalled Check if USRP Radio HSP is installed

spkg = matlabshared.supportpkg.getInstalled;
flag = ~isempty(spkg) && any(contains({spkg.Name},'USRP','IgnoreCase',true));
end
%% 6 附录：调制器
function y = bpskModulator(x,sps)
%bpskModulator BPSK modulator with pulse shaping
%   Y = bpskModulator(X,SPS) BPSK modulates the input X, and returns the
%   root-raised cosine pulse shaped signal Y. X must be a column vector
%   of values in the set [0 1]. The root-raised cosine filter has a
%   roll-off factor of 0.35 and spans four symbols. The output signal
%   Y has unit power.

persistent filterCoeffs
if isempty(filterCoeffs)
  filterCoeffs = rcosdesign(0.35, 4, sps);
end
% Modulate
syms = pskmod(x,2);
% Pulse shape
y = filter(filterCoeffs, 1, upsample(syms,sps));

end

function y = qpskModulator(x,sps)
%qpskModulator QPSK modulator with pulse shaping
%   Y = qpskModulator(X,SPS) QPSK modulates the input X, and returns the
%   root-raised cosine pulse shaped signal Y. X must be a column vector
%   of values in the set [0 3]. The root-raised cosine filter has a
%   roll-off factor of 0.35 and spans four symbols. The output signal
%   Y has unit power.

persistent filterCoeffs
if isempty(filterCoeffs)
  filterCoeffs = rcosdesign(0.35, 4, sps);
end
% Modulate
syms = pskmod(x,4,pi/4);
% Pulse shape
y = filter(filterCoeffs, 1, upsample(syms,sps));
end

function y = psk8Modulator(x,sps)
%psk8Modulator 8-PSK modulator with pulse shaping
%   Y = psk8Modulator(X,SPS) 8-PSK modulates the input X, and returns the
%   root-raised cosine pulse shaped signal Y. X must be a column vector
%   of values in the set [0 7]. The root-raised cosine filter has a
%   roll-off factor of 0.35 and spans four symbols. The output signal
%   Y has unit power.

persistent filterCoeffs
if isempty(filterCoeffs)
  filterCoeffs = rcosdesign(0.35, 4, sps);
end
% Modulate
syms = pskmod(x,8);
% Pulse shape
y = filter(filterCoeffs, 1, upsample(syms,sps));
end

function y = qam16Modulator(x,sps)
%qam16Modulator 16-QAM modulator with pulse shaping
%   Y = qam16Modulator(X,SPS) 16-QAM modulates the input X, and returns the
%   root-raised cosine pulse shaped signal Y. X must be a column vector
%   of values in the set [0 15]. The root-raised cosine filter has a
%   roll-off factor of 0.35 and spans four symbols. The output signal
%   Y has unit power.

persistent filterCoeffs
if isempty(filterCoeffs)
  filterCoeffs = rcosdesign(0.35, 4, sps);
end
% Modulate and pulse shape
syms = qammod(x,16,'UnitAveragePower',true);
% Pulse shape
y = filter(filterCoeffs, 1, upsample(syms,sps));
end

function y = qam64Modulator(x,sps)
%qam64Modulator 64-QAM modulator with pulse shaping
%   Y = qam64Modulator(X,SPS) 64-QAM modulates the input X, and returns the
%   root-raised cosine pulse shaped signal Y. X must be a column vector
%   of values in the set [0 63]. The root-raised cosine filter has a
%   roll-off factor of 0.35 and spans four symbols. The output signal
%   Y has unit power.

persistent filterCoeffs
if isempty(filterCoeffs)
  filterCoeffs = rcosdesign(0.35, 4, sps);
end
% Modulate
syms = qammod(x,64,'UnitAveragePower',true);
% Pulse shape
y = filter(filterCoeffs, 1, upsample(syms,sps));
end

function y = pam4Modulator(x,sps)
%pam4Modulator PAM4 modulator with pulse shaping
%   Y = pam4Modulator(X,SPS) PAM4 modulates the input X, and returns the
%   root-raised cosine pulse shaped signal Y. X must be a column vector
%   of values in the set [0 3]. The root-raised cosine filter has a
%   roll-off factor of 0.35 and spans four symbols. The output signal
%   Y has unit power.

persistent filterCoeffs amp
if isempty(filterCoeffs)
  filterCoeffs = rcosdesign(0.35, 4, sps);
  amp = 1 / sqrt(mean(abs(pammod(0:3, 4)).^2));
end
% Modulate
syms = amp * pammod(x,4);
% Pulse shape
y = filter(filterCoeffs, 1, upsample(syms,sps));
end

function y = gfskModulator(x,sps)
%gfskModulator GFSK modulator
%   Y = gfskModulator(X,SPS) GFSK modulates the input X and returns the
%   signal Y. X must be a column vector of values in the set [0 1]. The
%   BT product is 0.35 and the modulation index is 1. The output signal
%   Y has unit power.

persistent mod meanM
if isempty(mod)
  M = 2;
  mod = comm.CPMModulator(...
    'ModulationOrder', M, ...
    'FrequencyPulse', 'Gaussian', ...
    'BandwidthTimeProduct', 0.35, ...
    'ModulationIndex', 1, ...
    'SamplesPerSymbol', sps);
  meanM = mean(0:M-1);
end
% Modulate
y = mod(2*(x-meanM));
end

function y = cpfskModulator(x,sps)
%cpfskModulator CPFSK modulator
%   Y = cpfskModulator(X,SPS) CPFSK modulates the input X and returns
%   the signal Y. X must be a column vector of values in the set [0 1].
%   the modulation index is 0.5. The output signal Y has unit power.

persistent mod meanM
if isempty(mod)
  M = 2;
  mod = comm.CPFSKModulator(...
    'ModulationOrder', M, ...
    'ModulationIndex', 0.5, ...
    'SamplesPerSymbol', sps);
  meanM = mean(0:M-1);
end
% Modulate
y = mod(2*(x-meanM));
end

function y = bfmModulator(x,fs)
%bfmModulator Broadcast FM modulator
%   Y = bfmModulator(X,FS) broadcast FM modulates the input X and returns
%   the signal Y at the sample rate FS. X must be a column vector of
%   audio samples at the sample rate FS. The frequency deviation is 75 kHz
%   and the pre-emphasis filter time constant is 75 microseconds.

persistent mod
if isempty(mod)
  mod = comm.FMBroadcastModulator(...
    'AudioSampleRate', fs, ...
    'SampleRate', fs);
end
y = mod(x);
end

function y = dsbamModulator(x,fs)
%dsbamModulator Double sideband AM modulator
%   Y = dsbamModulator(X,FS) double sideband AM modulates the input X and
%   returns the signal Y at the sample rate FS. X must be a column vector of
%   audio samples at the sample rate FS. The IF frequency is 50 kHz.

y = ammod(x,50e3,fs);
end

function y = ssbamModulator(x,fs)
%ssbamModulator Single sideband AM modulator
%   Y = ssbamModulator(X,FS) single sideband AM modulates the input X and
%   returns the signal Y at the sample rate FS. X must be a column vector of
%   audio samples at the sample rate FS. The IF frequency is 50 kHz.

y = ssbmod(x,50e3,fs);
end

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