CPPCNet: High-Performance and Low-Complexity Automatic Modulation Classification for Resource-Limited IoT Communication

Automatic modulation classification (AMC) enables the identification of modulation schemes without prior information, facilitating efficient signal processing. Recently, deep-learning (DL)-based AMC has significantly advanced signal detection and recognition across various domains, including Internet of Things (IoT) systems and industrial cognitive communication systems. While high-performing AMC models achieve remarkable accuracy, their substantial storage and computational demands hinder deployment in resource-limited IoT communication systems. To address this challenge, we propose CPPCNet, a high-performance, lightweight complex-valued partial pointwise convolutional neural network. By leveraging complex-valued operations for automatic feature extraction, CPPCNet preserves phase information, enhancing classification performance. To alleviate the computational burden of complex-valued operations in resource-limited IoTs, we introduce complex-valued partial pointwise convolution (CPPC), which optimally balances accuracy and model complexity. Experimental results show that CPPCNet, with only 65302 parameters, achieves a state-of-the-art (SOTA) accuracy of 66.38% on RML2016.10b among all existing AMC models. On RML2016.10a, it also achieves a strong performance with an accuracy of 62.25%. Furthermore, it achieves 83.5% accuracy on the HisarMod2019.1 dataset, which includes more realistic channel impairments, outperforming existing lightweight AMC models in both accuracy and inference speed. These results highlight CPPCNet’s strong generalization ability and its ability to balance performance and efficiency, making it a promising solution for AMC applications in resource-constrained and dynamically changing environments.