MOD3NN: A Framework for Automatic Signal Modulation Detection Using 3D CNN.

In this work, we present an application of a three-dimensional convolutional neural network for the task of automatic modulation recognition from raw I/Q signal data. Raw I/Q signal data exhibits a special “helical” structure that can be exploited with three-dimensional convolutions (3D convolutions) to learn spatio-temporal features from the signal for the problem of modulation recognition. By tweaking the convolutional filters to learn the helical symmetry of the data, we can design a shallow network for automatic modulation recognition (AMR). We present the results of our experiments with raw I/Q signal data collected in an uncalibrated radio frequency (RF) environment using several different modulation schemes. We show that with our methods and implementation, we can achieve around 99 % accuracy for automatic modulation recognition, for a variety of practical modulation techniques without the need for explicit feature engineering.