  Automatic modulation recognition (AMR) detects the modulation scheme of the
received signals for further signal processing without needing prior
information, and provides the essential function when such information is
missing. Recent breakthroughs in deep learning (DL) have laid the foundation
for developing high-performance DL-AMR approaches for communications systems.
Comparing with traditional modulation detection methods, DL-AMR approaches have
achieved promising performance including high recognition accuracy and low
false alarms due to the strong feature extraction and classification abilities
of deep neural networks. Despite the promising potential, DL-AMR approaches
also bring concerns to complexity and explainability, which affect the
practical deployment in wireless communications systems. This paper aims to
present a review of the current DL-AMR research, with a focus on appropriate DL
models and benchmark datasets. We further provide comprehensive experiments to
compare the state of the art models for single-input-single-output (SISO)
systems from both accuracy and complexity perspectives, and propose to apply
DL-AMR in the new multiple-input-multiple-output (MIMO) scenario with
precoding. Finally, existing challenges and possible future research directions
are discussed.
