We are trying to build different machine learning models to solve the Signal Modulation Classification problem.
With the dataset from RadioML, we work from 2 approaches to improve the classification performance for the dataset itself and its subset:
For this model, we use a GTX-980Ti GPU to speed up the execution time.
With our new architecture, the CNN model has the total data's Validation Accuracy improved to 56.04% from 49.49%, normal data's Validation Accuracy improved to 82.21% from 70.45%, with the running time for each epoch decreased to 13s from 15s(With the early stopping mechanism, it usually takes 40-60 epochs to train the model).
Layer (type) Output Shape Param #
=================================================================
reshape_1 (Reshape) (None, 2, 128, 1) 0
_________________________________________________________________
zero_padding2d_1 (ZeroPadding) (None, 2, 132, 1) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 2, 129, 64) 320
_________________________________________________________________
dropout_1 (Dropout) (None, 2, 129, 64) 0
_________________________________________________________________
zero_padding2d_2 (ZeroPadding) (None, 2, 133, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 1, 130, 64) 32832
_________________________________________________________________
dropout_2 (Dropout) (None, 1, 130, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 1, 123, 128) 65664
_________________________________________________________________
dropout_3 (Dropout) (None, 1, 123, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 1, 116, 128) 131200
_________________________________________________________________
dropout_4 (Dropout) (None, 1, 116, 128) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 14848) 0
_________________________________________________________________
dense1 (Dense) (None, 256) 3801344
_________________________________________________________________
dropout_5 (Dropout) (None, 256) 0
_________________________________________________________________
dense2 (Dense) (None, 11) 2827
_________________________________________________________________
reshape_2 (Reshape) (None, 11) 0
=================================================================
Total params: 4,034,187
Trainable params: 4,034,187
Non-trainable params: 0
In our second approach, we converted the given data set into spectrogram images of size 41px x 108px and ran CNN models on the image data set. Sice this is a highly time and memory intensive process, we chose a smaller subets of the data. The subsets chosen are:
- Modulations - BPSK, QAM16, AM-DSB, WBFM with SNR ranging from +8 to +18 dB with steps of 2
- Modulations - BPSK, QAM16, AM-DSB, WBFM with SNR ranging from 10 to +8 dB with steps of 2
- Modulations - BPSK, QAM16, AM-DSB, WBFM, AB-SSB, QPSK with SNR ranging from 0 to +18 dB with steps of 2
The results of the model are shown below:
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