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A Dictionary Learning Based Automatic Modulation Classification Method
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DOI：10.1109/ACCESS.2018.2794587.png)
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摘要
En 中文
As the process of identifying the modulation format of the received signal, automatic modulation classification (AMC) has various applications in spectrum monitoring and signal interception. In this paper, we propose a dictionary learning-based AMC framework, where a dictionary is trained using signals with known modulation formats and the modulation format of the target signal is determined by its sparse representation on the dictionary. We also design a dictionary learning algorithm called block coordinate descent dictionary learning (BCDL). Furthermore, we prove the convergence of BCDL and quantify its convergence speed in a closed form. Simulation results show that our proposed AMC scheme offers superior performance than the existing methods with low complexity.
Keyword:
Modulation classification
data driven
dictionary learning
block coordinate descent
sparse representation
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期刊
IF:
3.6
论文数:
9.8W
被引数:
31.7W
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