Few-shot specific emitter identification (FS-SEI) is crucial for distinguishing individual transmitters when only limited labeled samples are available. Existing FS-SEI methods often rely heavily on deep feature extraction, while the resulting representations usually lack clear physical interpretability, thereby limiti...
Min-Yu Guo, Lai Xu, Tian-Tian Tang et al.· IEEE Communications Letters· 0 citations
Accurate basin-scale prediction of hydro-meteorological variables is central to water-resource management and environmental monitoring, but target-basin observations are often insufficient for training high-capacity sequence models from scratch. RAPID-Hydrology extends the TimeDART diffusion-autoregressive backbone int...
Collaborative edge computing (CEC) enables edge servers to cooperatively process computation-intensive tasks from industrial Internet of Things devices, while computation reuse (CR) leverages cached task results to reduce response delay and energy consumption for similar tasks. However, existing studies fail to adequat...
Bei-Bei Zhang, Zi-Xuan He, Yan-Jing Sun et al.· IEEE Transactions on Cogniti...· 0 citations
Specific Emitter Identification (SEI) is important for enhancing physical-layer security in resource-constrained Internet of Things (IoT) networks. However, Deep Learning (DL)-based SEI methods often incur high computational complexity and suffer performance degradation under noisy conditions. To address these issues,...
Yan Xiao, Lai Xu, Tian-Tian Tang et al.· IEEE Communications Letters· 0 citations
Specific emitter identification (SEI) enables physical-layer authentication of wireless devices by exploiting hardware-induced radio-frequency (RF) fingerprints embedded in received signals. However, existing deep learning–based SEI methods often suffer from limited cross-domain generalization, as models tend to overfi...
Lu-Yao Wang, Zhen-Xin Cai, Fan Wang et al.· IEEE Transactions on Cogniti...· 0 citations
Accurate identification of uncrewed aerial vehicles (UAVs) is crucial to ensure airspace security and effective threat assessment. Traditional UAV classification systems usually rely on single-modal sensing data, which limits their ability to capture the diversity and complementarity of UAV characteristics under differ...
Rui Wang, Siqi Lou, Zhen-Xin Cai et al.· IEEE Transactions on Cogniti...· 0 citations
Automatic modulation classification (AMC) has been a critical task in non-cooperative communication systems, yet the application of deep learning to this domain faces challenges. In this letter, we propose a multi-level spatiotemporal framework for AMC. The model integrates a series of fundamental deep learning compone...
Unmanned aerial vehicle (UAV) recognition has become increasingly important for securing the low-altitude economy. Radio frequency (RF) signal analysis using deep learning (DL) has emerged as a promising approach to this task. However, training robust DL models relies on large-scale labeled datasets, which are costly t...
Radio-FM is presented, a scalable family of foundation models designed for universal radio signal representation learning that employs dual-channel processing specifically optimized for I/Q independence while capturing cross-channel interactions through a lightweight attention mechanism.
Jin-Chao Zhou, Wu-Peng Xie, Zhuang-Zhi Chen et al.· 0 citations
Wi-Fi-based human activity recognition (HAR) using channel state information (CSI) provides a nonintrusive and device-free sensing solution for smart cities, healthcare monitoring, and smart homes. However, recognition performance often degrades when models trained on limited users are applied to unseen users due to va...
Fucheng Miao, Osamu Takyu, Shan Lin et al.· IEEE Internet of Things Jour...· 0 citations
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