A Lightweight Deep Learning Model for Automatic Modulation Classification using Dual Path Deep Residual Shrinkage Network

-- Efficient spectrum utilization is critical to meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying modulation schem es in received signals -- an essential capability for dynamic spectrum allocation and interference mitigation, particularly in cognitive radio (CR) systems. With the increasing deployment of smart edge devices, such as IoT nodes with limited computational and memory resources, there is a pressing need for lightweight AMC models that balance low complexity with high classification accuracy . This paper proposes a low - complexity, lightweight deep learning (DL) AMC model optimized for resource - constrained edge devices. We introduce a dual - path deep residual shrinkage network (DP - DRSN) with Garrote thresholding for effective signal denoising and design a compact hybrid CNN - LSTM architecture comprising only 27,000 training parameters. The proposed model achieves average classification accuracies of 61.20%, 63.78%, and 62.13% on the RML2016.10a, These results underscore the model's potential for enabling accurate and efficient AMC on - edge devices with limited resources . Spectrum is a limited and valuable physical resource, and its efficient utilization is essential to support the growing data demands of wireless communication networks. In the dynamic landscape of modern communication systems, maximizing radio spectrum efficiency is paramount. Automatic Modulation Classification (AMC) is a key technology that significantly contributes to this goal.