Evaluating the Performance Trade-off Between DenseNet and CNN2 for Automatic Modulation Recognition

Convolutional neural networks (CNNs) have become the mainstream approach for AMR tasks. However, model selection still faces a trade-off among "performance", "complexity" and "adaptability". Current literature primarily focuses on model innovation itself while lacking systematic comparative analysis of different types of CNN models under the same data conditions. This study investigates the performance differences between two deep neural networksDenseNet and CNN2across signal-to-noise ratio (SNR) conditions ranging from 0 dB to 20 dB, based on the standard dataset (RadioML 2018.01A) and evaluates the performance by combining dimensions such as accuracy rate, convergence curve, confusion matrix, etc. The experimental results demonstrate that DenseNet achieves superior recognition accuracy under resource-constrained conditions, particularly at medium-to-high SNR levels. At 20 dB SNR, DenseNet attains an accuracy of 82.1%, outperforming CNN2 by 3.6 percentage points. However, at low SNR, especially at 0 dB, CNN2 demonstrated stronger robustness and generalization ability, but DenseNet was difficult to converge. These findings establish both empirical benchmarks and a theoretical framework for optimizing AMR network architecture selection across diverse communication environments.