Joint Signal Detection and Automatic Modulation Classification via Deep Learning
<|reference_start|>Joint Signal Detection and Automatic Modulation Classification via Deep Learning: 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).<|reference_end|>
@article{xing2024joint,
    title={Joint Signal Detection and Automatic Modulation Classification via Deep
  Learning},
    author={Huijun Xing, Xuhui Zhang, Shuo Chang, Jinke Ren, Zixun Zhang, Jie Xu,
  Shuguang Cui},
    journal={arXiv preprint arXiv:2405.00736},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00736},
    primaryClass={eess.SP cs.LG}
}