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Joint Signal Detection and Automatic Modulation Classification via Deep Learning
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DOI：10.1109/TWC.2024.3450972.png)
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Abstract
En 中文
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 resources at https://github.com/Singingkettle/ChangShuoRadioData.
Keywords:
Feature extraction
Signal detection
Frequency modulation
Time-frequency analysis
Signal to noise ratio
Industries
Deep learning
Automatic modulation classification
dataset design
hierarchical classification head
Journal
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10.7
Papers:
1.3W
Citations:
5.3W