MDSCNet: A Lightweight Complex Convolutional Network for Automatic Modulation Classification

The electromagnetic spectrum grows increasingly crowded with the rapid expansion of mobile, satellite and Internet of Things communications, making intelligent spectrum sensing and efficient management an urgent priority. Automatic modulation classification (AMC) serves as the core of cognitive radio and intelligent communication. Existing deep models often suffer from a large number of parameters and low storage efficiency. To overcome these limitations, we propose MDSCNet, a multi-scale depth-wise separable complex network. Built upon complex depth-wise separable convolution, the network makes full use of the phase information in in-phase and quadrature signals while naturally preserving the symmetric relationship between the in-phase and quadrature components (IQ). The asymmetric multi-scale structure combined with the embedded lightweight attention module jointly forms the overall feature extraction process. The overall parameter count is kept extremely low, at only 47.739 k. Experiments on the RML2016.10a and RML2016.10b datasets show that MDSCNet delivers recognition performance under low signal-to-noise ratios (SNR), reaching 63.42% and 66.71% respectively. More importantly, it outperforms mainstream methods in both parameter count and storage efficiency.