Modulation Classification

Classifying different signals modulations into their right modulations using different baseline classifiers and a CNN model

Dataset

DeepSig Dataset: RadioML 2016.04B

Download Link: opendata.deepsig.io/datasets/2016.10/RML2016.10b.tar.bz2

Description: A synthetic dataset, generated with GNU Radio [1], consisting of 11 modulations. This is a variable-SNR dataset with moderate LO drift, light fading, and numerous different labeled SNR increments for use in measuring performance across different signal and noise power scenarios.

• It has 1,200,000 samples
• Each sample is presented using two vectors each of them has 128 elements.
• The data is split into 70% for training/validation and 30% for testing.
• 5% of the training and validation dataset for validation.

Feature Spaces

4 different features spaces are used and results are compared

• Raw time series as given (two channels)
• First derivative in time (two channels)
• Integral in time (two channels)
• Combinations of 1,2 and 3. (More channels)

Baseline Classifiers

• Logistic Regression Classifier
• Decision Tree
• Random Forest
• A fully connected dense layer:
  • Non-linear function: Relu
  • Optimizer: ADAM
  • Early stopping

CNN Model

Architecture: [2]

• Input : 2 x 128
• Conv Relu : 64 x (1 x 3)
• Conv Relu : 16 x (2 x 3)
• Dense Relu : 128
• Dense Softmax : 10
• Output : 10

References

[1] T. O’shea, N. West. “Radio Machine Learning Dataset Generation with GNU Radio”, https://pubs.gnuradio.org/index.php/grcon/article/download/11/10/

[2] T. O’Shea, J. Corgan, and T. Clancy. “Convolutional Radio Modulation Recognition Networks” https://arxiv.org/pdf/1602.04105.pdf