  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).
