Domain-Adaptive RFI Detection Using Fine-Tuned Time-Frequency Deep Models and Visual Explainability

Abstract

Radio Frequency Interference (RFI) remains a significant threat to the reliability of modern wireless systems, particularly as signal environments grow increasingly diverse and congested. This paper introduces a novel domain-adaptive deep learning framework for robust RFI detection across heterogeneous wireless environments. Unlike existing approaches that use static pre-trained convolutional neural networks (CNNs), we propose a two-stage transfer learning strategy wherein ResNet50 and AlexNet models are selectively fine-tuned on domain-specific signal datasets represented as spectrograms and scalograms. These time-frequency transformations capture complementary spectral characteristics—spectrograms model persistent interference patterns, while scalograms highlight transient, bursty anomalies. The fine-tuned networks extract high-level semantic features that are then adaptively weighted using an attention mechanism, enabling the model to emphasize the most informative representations from each domain. The fused features are classified via a lightweight CNN, which balances accuracy with computational efficiency. To promote transparency and model trustworthiness, we further integrate Grad-CAM-based visual explanations that highlight the discriminative regions within the time-frequency maps responsible for the model’s decisions. Experimental evaluations across multiple signal domains, including synthetic and real-world datasets, demonstrate that the proposed approach not only achieves state-of-the-art accuracy (98.1%) but also generalizes effectively to unseen interference types. This framework offers a scalable, explainable, and transferable solution for real-time RFI detection in complex wireless, satellite, and edge-based IoT systems.