Recent advances in deep learning (DL) have led many contemporary automatic modulation classification (AMC) techniques to use deep networks in classifying the modulation type of incoming signals at the receiver. However, current DL-based methods face scalability challenges, particularly when encountering unseen modulations or input signals from environments not present during model training, making them less suitable for real-world applications like software-defined radio devices. In this paper, we introduce a scalable AMC scheme that provides flexibility for new modulations and adaptability to input signals with diverse configurations. We propose the Meta-Transformer, a meta-learning framework based on few-shot learning (FSL) to acquire general knowledge and a learning method for AMC tasks. This approach empowers the model to identify new unseen modulations using only a very small number of samples, eliminating the need for complete model retraining. Furthermore, we enhance the scalability of the classifier by leveraging main-sub transformer-based encoders, enabling efficient processing of input signals with diverse setups. Extensive evaluations demonstrate that the proposed AMC method outperforms existing techniques across all signal-to-noise ratios (SNRs) on RadioML2018.01A. The source code and pre-trained models are released at https://github.com/cheeseBG/meta-transformer-amc .

In modern wireless networks, Wi-Fi technology serves a dual role — supporting both data communication and environmental sensing. This dual functionality has positioned Wi-Fi sensing as a key enabler of Integrated Sensing and Communication (ISAC), with broad applications across the Internet of Things (IoT). However, this integration poses a significant challenge to Wi-Fi sensing, as data transmission for communication interferes with sensing data collection and degrades the overall performance of sensing models. In this work, we propose a multi-link-based CSI sampling and integration framework combined with a CSI data loss recovery preprocessing method to enhance network resource efficiency, mitigate data loss, and improve the robustness of Wi-Fi sensing models. The proposed preprocessing method utilizes a Context Encoder-based inpainting approach to effectively restore lost CSI data, ensuring reliable system performance even under high-loss conditions. Experimental results show that our approach consistently outperforms existing methods, achieving higher data recovery accuracy and maintaining stable performance despite increasing loss rates.

With the growing prevalence of large-scale intelligent surveillance camera systems, the burden on real-time video analytics pipelines has significantly increased due to continuous video transmission from numerous cameras. To mitigate this strain, recent approaches focus on filtering irrelevant video frames early in the pipeline, at the camera or edge device level. In this paper, we propose Wi-Filter, an innovative filtering method that leverages Wi-Fi signals from wireless edge devices, such as Wi-Fi-enabled cameras, to optimize filtering decisions dynamically. Wi-Filter utilizes channel state information (CSI) readily available from these wireless cameras to detect human motion within the field of view, adjusting the filtering threshold accordingly. The motion-sensing models in Wi-Filter (Wi-Fi assisted Filter) are trained using a self-supervised approach, where CSI data are automatically annotated via synchronized camera feeds. We demonstrate the effectiveness of Wi-Filter through real-world experiments and prototype implementation. Wi-Filter achieves motion detection accuracy exceeding 97.2% and reduces false positive rates by up to 60% while maintaining a high detection rate, even in challenging environments, showing its potential to enhance the efficiency of video analytics pipelines.