ABSTRACT 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. INDEX TERMS Modulation classification, data driven, dictionary learning, block coordinate descent, sparse representation.
The author proposed AMC framework based on dictionary learning in REF , where first a dictionary is formed using signals with well-known modulation schemes and by using sparse representation on the dictionary, modulation formats of unknown signals are determined then.
A Dictionary Learning Based Automatic Modulation Classification Method
IEEE Access
IEEE Access
Computer Science