With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

The widespread deployment of unmanned aerial vehicles has raised growing privacy and security concerns, creating an urgent need for effective drone detection and identification. To address these challenges, this paper proposes a semantic learning framework termed Drone Time–Frequency-Semantic Network (DroneTFS-SemNet) for drone recognition. The main contributions are as follows. A Temporal Enhancement module is designed to improve model adaptability and robustness through radio frequency (RF) signal noise simulation, feature pre-extraction, learnable frequency-band partitioning, and an attention mechanism. A Cross-Channel Recalibration Module is introduced to promote cross-channel interaction of RF features via dilated convolutions and Channel Shuffle, thereby enhancing the model’s learning capability. Third, Feature Fusion Attention is employed to weight, align, and fuse RF features from different levels, thereby improving the model’s ability to represent differences among drone categories and operation modes. A semantic construction and decision module is further developed to extract high-level semantic features and accomplish drone detection, classification, and recognition. Experimental results on the DroneRF dataset show that DroneTFS-SemNet achieves a drone classification accuracy of 99.73% and an operation mode recognition accuracy of 99.65%, outperforming multiple comparison methods. These results demonstrate that the proposed model has strong recognition capability on this public dataset and under controlled experimental conditions, providing an effective method for RF-based drone recognition research.