对比多种调制识别模型，给出可复现的性能基准。
AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future Possibilities
- 统一数据集与硬件，复现并比较主流调制识别模型。
- 在不同信噪比下测试准确率，揭示模型复杂度与性能关系。
- 公开代码供研究者参考，推动未来调制识别技术发展。
本文综述了近年来高性能自动调制识别（AMR）模型在分类射频（RF）调制方式方面的研究进展。通过复现这些模型，并在相同数据集（RadioML-2016A）、相同硬件和一致测试准确率定义下进行性能比较，为未来AMR研究提供了基准。超参数依据原论文建议设定，以尽可能还原原始结果。所有复现模型均已开源，便于进一步分析。我们还绘制了模型测试准确率与其参数量的关系图，揭示其复杂度与性能之间的权衡。基于此分析，提出提升模型性能的策略。最后，展望了通过新架构、数据处理或训练方法实现进一步突破的可能性。
原文摘要 · Abstract (English)
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.
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