Volume 15, Number 1

Ensemble Learning Approach for Digital Communication Modulation’s Classification

  Authors

Yahya Benremdane, Said Jamal, Oumaima Taheri, Jawad Lakziz and Said Ouaskit, University Hassan II, Morocco

  Abstract

This work uses artificial intelligence to create an automatic solution for the modulation's classification of various radio signal kinds. This project is a component of a lengthy communications intelligence process that aims to find an automated method for demodulating, decoding, and interpreting communication signals. As a result, the work we did involved selecting the database required for supervised deep learning, assessing the performance of current methods on unprocessed communication signals, and suggesting a deep learning network-based method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy. In order to use the current automatic classification models as a guide, we first conducted study on them. As a result, we suggested an ensemble learning strategy based on Transformer Neural Network and adjusted ResNet that takes into account the difficulty of forecasting in low Signal Noise Ratio (SNR) scenarios while also being effective at extracting multi-scale characteristics from the raw I/Q sequence data. Ultimately, we produced an architecture for communication signals that is simple to work with and implement. With an accuracy of up to 95%, this solution's optimum and sturdy architecture decides the type of modulation on its own.

  Keywords

Automatic modulation classification, Artificial Intelligence, Deep Learning & Modulation recognition,