Spectrum Classification Application

Introduction

The goal of this project is to create an application that will run on a receiver node and processes signals. It will take the received signal as an input, analyze the components and details of the signal, and classify the signal based on the analysis. This will require machine learning techniques to perform the classification.

The program will receive signals, determine what modulation scheme was used to modulate the signal, and then demodulate the signal with the found scheme. This can also be expanded to creating a modem that will also choose the best modulation scheme to modulate a signal depending on the SNR of the given range of wireless frequencies.

Background

​Summary of Project

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.

Once the modulation scheme classification is done we will implement a modem system using the technology.

Tools Used

TensorFlow: Neural network library

Scikit-learn: Machine learning library

​RadioML Dataset Signal Training Data

GNURadio: Software defined radio toolkit

CUDA: NVIDIA Parallel Processing framework

Anaconda: Python powered data science focused platform

Presentations

​Week One

​Week Two

​Week Three

​Week Four

​Week Five

​Week Six

​Week Seven

​Week Eight

The Team

Avanish Mishra Brendan Bruce