Joint Signal Detection and Automatic Modulation Classification via Deep Learning

TL;DR

This paper introduces a deep learning framework for joint signal detection and automatic modulation classification in complex scenarios with multiple coexisting signals, supported by a new dataset and extensive simulations.

Contribution

It presents a novel joint detection and classification framework tailored for multiple coexisting signals, along with a new realistic dataset and open-source code.

Findings

Effective joint detection and classification demonstrated on CRML23 dataset

Framework outperforms existing methods in complex multi-signal scenarios

Open-source code and dataset facilitate future research

Abstract

Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different carrier frequencies. We first generate a coexisting RADIOML dataset (CRML23) to facilitate the joint design. Different from the publicly available AMC dataset ignoring the signal detection step and containing only one signal, our synthetic dataset covers the more realistic multiple-signal coexisting scenario. Then, we present a joint framework for detection and classification (JDM) for such a multiple-signal coexisting environment, which consists of two modules for signal detection and AMC, respectively. In particular, these two modules are interconnected using a designated data structure called "proposal". Finally, we conduct extensive simulations over the newly developed dataset, which demonstrate the effectiveness of our designs. Our code and dataset are now available as open-source (https://github.com/Singingkettle/ChangShuoRadioData).