A Dictionary Learning Based Automatic Modulation Classification Method

• Main Authors: Kezhong Zhang, Easton Li Xu, Zhiyong Feng, Ping Zhang
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Modulation classification; data driven; dictionary learning; block coordinate descent; sparse representation
• Online Access: https://ieeexplore.ieee.org/document/8262636/

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.