A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification.

ABSTRACT: Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition. Experimental results demonstrate that the CC-MSNet performs well on three benchmark datasets, RML2016.10a,

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11436720 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature