Over-the-Air Federated Transfer Learning Over UAV Swarm for Automatic Modulation Recognition in V2X Radio Monitoring

The increasing number of smart vehicles is leading to an increasing scarcity of spectrum resources for the internet of vehicles (IoV), which has given rise to an urgent requirement for automatic modulation classification (AMC) in cognitive radio (CR) systems. Meanwhile, for the flexibility of unmanned aerial vehicles (UAVs), the AMC implemented based on UAVs is considered an effective method to achieve reliable communication between intelligent vehicles. However, for distributed UAV task implementation, real-time radio data needs to be transmitted between UAVs and a cloud server. This process requires maintaining a high-capacity, secure channel environment, which is difficult to accomplish. In this paper, we propose a federated transfer learning framework to implement AMC in a distributed scenario, which avoids radio data transmission in each UAV. To reduce data dependence, the pre-trained deep learning (DL)-based model is sent to each UAV node and performs transfer learning, which brings more focused learning of the channel environment in which various UAVs are located. The simulation results show that federated transfer learning-based AMC offers better recognition accuracy than centralized approach. Compared to the centralized training methods, the federated transfer learning algorithm achieves an improvement of 1.04% to 12.05% in classification accuracy for each node with less training data. Besides, the effect of different fine-tuning layers on the accuracy is investigated, showing that fine-tuning three layers could achieve optimal accuracy. Additionally, different numbers of UAVs are employed to verify the impact on the results. The experimental results show that the number of UAVs can improve the results but to a limited extent. Furthermore, we evaluate the proposed method by various measurements, such as accuracy, precision, and F1-score. Accordingly, compared with the baseline methods, the proposed scheme achieves an improvement of 1% to 14% over them.