Joint Signal Detection and Automatic Modulation Classification via Deep Learning
This work addresses a realistic scenario in wireless communication systems where multiple signals with different modulations coexist, though it is incremental by extending prior independent approaches to a joint design.
The paper tackles the joint problem of signal detection and automatic modulation classification in wireless communications by introducing a new dataset (CRML23) for multiple coexisting signals and a joint framework (JDM), achieving effective performance as demonstrated through simulations.
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).