文件列表:

Modulation_Recognition_CNN_Model.ipynb (255143, 2019-07-19)
Modulation_Recognition_Data.ipynb (30469, 2019-07-19)
Modulation_Recognition_Evaluation.ipynb (504883, 2019-07-19)
Modulation_Recognition_FC_Model.ipynb (205102, 2019-07-19)

# Problem Statment DeepSig Dataset: RadioML 2016.04C A synthetic dataset, generated with GNU Radio, 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. # About Data Data version used is available from [here](http://opendata.deepsig.io/datasets/2016.10/RML2016.10b.tar.bz2). Every sample is presented using two vectors each of them has 128 elements. The dataset has the signals with raw features so we extracted more features from it which are: ○ First Derivative in Time ○ Integral in Time We used extracted features and created a combination of them in order to have more datasets with different features to train the model, the combinations are: ○ Derivative Features + Raw Features ○ Integral Features + Raw Features ○ Integral Features + Derivative Features ○ Integral Features + Derivative Features + Raw Features As the size of datasets with the combination of features was very large to fit b in the memory, we converted them to **T ensorflow Records** so we can train the model with data loaded on disk. Each dataset is splitted into 50% for training/validation and 50% for testing # Fully connected Model Approach **Architecture used for the FCN is:** ○ Input layer of size (2,128) ○ First hidden layer of size 512 ○ Second hidden layer of size 512 ○ Flatten Layer ○ Softmax output layer of size 10 Activation function of neurons is ReLU. The optimizer used is Adam. The loss function used is Categorical Cross Entropy. **Results: ** Best model from this approach was the one using the first derivative in time and raw features combination and scored a 45.86% testing accuracy in the evaluation. # Convolutional Neural Network Model Approach **Architecture used for CNN:** ○ Input layer of size (128,2,1) ○ Convolutional layer with *** filters of size (3,1) ○ Max Pooling Layer ○ Convolutional Layer with 16 filters of size (3,2) ○ Flatten Layer ○ Dropout Layer ○ Fully connected layer of size 128 ○ Softmax output Layer **Results:** Best model fromn this approach was the one using the integral in time features and scored 5***6% testing accuracy in evaluation. **More results and diagrams comprising the accuracy and realtion with respect to SNR can be found in the Evalution notebook** # References and papers used: 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) 3-[N. West, T. O’shea “Deep Architectures for Modulation Recognition”](https://arxiv.org/pdf/1703.09197.pdf) 5-[K. Karra, S. Kuzdeba, J. Peterson “Modulation recognition using hierarchical deep neural networks”](http://ieeexplore.ieee.org/document/7920746/?anchor=authors)

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