AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities
This work provides a standardized benchmark for researchers in radio frequency signal processing, though it is incremental as it focuses on replication and comparison rather than introducing new methods.
The paper reviews and replicates existing automatic modulation recognition models, comparing their accuracy on the RadioML-2016A dataset to provide a benchmark for future studies, with results showing performance variations across signal-to-noise ratios and model complexities.
We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared their performance in terms of accuracy across a range of signal-to-noise ratios. To ensure a fair comparison, we used the same dataset (RadioML-2016A), the same hardware, and a consistent definition of test accuracy as the evaluation metric, thereby providing a benchmark for future AMR studies. The hyperparameters were selected based on the authors' suggestions in the associated references to achieve results as close as possible to the originals. The replicated models are publicly accessible for further analysis of AMR models. We also present the test accuracies of the selected models versus their number of parameters, indicating their complexities. Building on this comparative analysis, we identify strategies to enhance these models' performance. Finally, we present potential opportunities for improvement, whether through novel architectures, data processing techniques, or training strategies, to further advance the capabilities of AMR models.