【信号处理】使用CNN对RF调制信号进行分类

Modulation Classification

Using CNN to classify RF modulation data.

Dataset is from: DATA LINK

paper: Over the Air Deep Learning Based Radio Signal Classification

Data Preprocessing

Data is processed. Column data are a two variable label composed of the Modulation and SNR, Row 0 is the binary encoded version of the Modulation and SNR, Row 1 is the actual data, each column is a 2, 128 array of I and Q data for the specified Modulation and SNR in the column label.

Build the CNN

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Conv1D, MaxPooling1D, GlobalAveragePooling1D, Flatten

verbose, epochs, batch_size = 1, 256, 1024
n_timesteps, n_features, n_outputs = xTrain.shape[1], xTrain.shape[2], yTrain.shape[1]
print('timesteps=', n_timesteps, 'features=', n_features, 'outputs=', n_outputs)
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps, n_features)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
#model.compile(RAdam(), loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())

原文地址：https://blog.csdn.net/Glass_Gun/article/details/143594734

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