| 15 | | To recognize the modulation scheme of the modulated signal received, we will train a classifier by using training data that encompasses many signals modulated with varying modulation schemes. Then, we will test the classifier using signals created from GNU Radio to confirm that it works properly. In practice, it will be running on an ORBIT node and receiving at a given frequency, try to determine if the given signals are just noise, and will demodulate any determined signals. |
| | 15 | To recognize the modulation scheme of a received signal, we will train a classifier with a synthetic dataset that contains signals modulated with varying modulation schemes with different parameters (SNR, different noise distributions). Once trained, it will be run on an ORBIT node and tested on signals transmitted/received from SDRs. |
| | 16 | |
| | 17 | Once the modulation scheme classification is done we will implement a modem system using the technology. |
| 19 | | Classifier - TensorFlow - Gives us a neural network library for smarter machine learning algorithms |
| 20 | | Signal Training Data - [https://radioml.com/datasets/radioml-2016-04-dataset/ RadioML Dataset] |
| 21 | | Testing Data - GNURadio - Comes with SDR Toolkit to send, receive, and plot signals |
| | 21 | TensorFlow: Neural network library |
| | 22 | |
| | 23 | Scikit-learn: Machine learning library |
| | 24 | |
| | 25 | [https://radioml.com/datasets/radioml-2016-04-dataset/ RadioML Dataset Signal Training Data] |
| | 26 | |
| | 27 | GNURadio: Software defined radio toolkit |
| | 28 | |
| | 29 | CUDA: NVIDIA Parallel Processing framework |
| | 30 | |
| | 31 | Anaconda: Python powered data science focused platform |
