Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors

Understand

This paper looks into the technology classification problem for a distributed wireless spectrum sensing network.

• First, a new data-driven model for Automatic Modulation Classification (AMC) based on long short term memory (LSTM) is proposed.
• The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments.
• Analyses show that the proposed model yields an average classification accuracy of close to 90% at varying SNR conditions ranging from 0dB to 20dB.