  Automatic modulation classification (AMC) is a promising technology to
realize intelligent wireless communications in the sixth generation (6G)
wireless communication networks. Recently, many data-and-knowledge dual-driven
AMC schemes have achieved high accuracy. However, most of these schemes focus
on generating additional prior knowledge or features of blind signals, which
consumes longer computation time and ignores the interpretability of the model
learning process. To solve these problems, we propose a novel knowledge graph
(KG) driven AMC (KGAMC) scheme by training the networks under the guidance of
domain knowledge. A modulation knowledge graph (MKG) with the knowledge of
modulation technical characteristics and application scenarios is constructed
and a relation-graph convolution network (RGCN) is designed to extract
knowledge of the MKG. This knowledge is utilized to facilitate the signal
features separation of the data-oriented model by implementing a specialized
feature aggregation method. Simulation results demonstrate that KGAMC achieves
superior classification performance compared to other benchmark schemes,
especially in the low signal-to-noise ratio (SNR) range. Furthermore, the
signal features of the high-order modulation are more discriminative, thus
reducing the confusion between similar signals.
