Ultralightweight AMC via Robust Geometric Median Filter Pruning for Edge IoT Devices

Automatic modulation classification (AMC) is a fundamental technology for identifying modulation types in non-cooperative communication systems. It plays a crucial role in various applications, including spectrum monitoring, cognitive radio, and signal intelligence. Recently, deep learning (DL) based AMC methods have achieved remarkable classification accuracy. However, their practical deployment in resource-constrained edge devices remains challenging due to their high computational complexity and excessive model size. To address this limitation, we propose an ultra-lightweight AMC method based on filter pruning via geometric median (FPGM). The key idea is to leverage the geometric median as a robustness-driven filter selection criterion, effectively eliminating redundant convolutional kernels while preserving essential model representations. Specifically, we first determine the geometric median of the filters in each layer, which effectively represents the distribution of filters within that layer. Then, filters near the geometric median are identified and filtered out through the characteristics of the geometric median. Finally, the performance degradation of the model caused by the removal of filters can be restored through fine-tuning. Experimental results demonstrate that the proposed AMC method achieves a 99% reduction in model size while limiting the classification accuracy drop to merely 1.61%, significantly outperforming other lightweight AMC techniques. These results highlight the feasibility of deploying the proposed AMC model on edge Internet of Things (IoT) devices, enabling efficient real-time modulation classification with minimal computational overhead.