VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition
Most existing wideband signal detection and recognition (WSDR) methods rely on diverse,
large-scale, and well-labeled training data, which are often difficult to obtain in practical …
large-scale, and well-labeled training data, which are often difficult to obtain in practical …
Integrating Prior Knowledge and Contrast Feature for Signal Modulation Classification
With the advancement of Internet of Things technology, the need for sophisticated signal
modulation classification (SMC) has intensified, ensuring seamless communication and …
modulation classification (SMC) has intensified, ensuring seamless communication and …
LSTM Framework for Classification of Radar and Communications Signals
V Clerico, J González-López, G Agam… - 2023 IEEE Radar …, 2023 - ieeexplore.ieee.org
Although radar and communications signal classification are usually treated separately, they
share similar characteristics, and methods applied in one domain can be potentially applied …
share similar characteristics, and methods applied in one domain can be potentially applied …
A Data-Driven Target Signal Extraction Method Based on Multimodal Clues for Co-Channel Interference Cancellation
W Deng, X Wang, Z Huang - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Interference cancellation (IC) is crucial for ensuring the continuous operability of wireless
communication systems based on the Internet of Things (IoT). This study focuses on blind …
communication systems based on the Internet of Things (IoT). This study focuses on blind …
APL: Integrated Discriminative Features and Robust Boundary for Modulation Open-Set Recognition
As the electromagnetic environment becomes increasingly complex, traditional Automatic
Modulation Recognition (AMR) methods cannot handle unknown modulation types that may …
Modulation Recognition (AMR) methods cannot handle unknown modulation types that may …
MCLHN: Towards Automatic Modulation Classification via Masked Contrastive Learning with Hard Negatives
Recently, contrastive learning (CL) has exhibited considerable advantages for automatic
modulation classification (AMC) with a scarcity of labeled samples. Nevertheless, the …
modulation classification (AMC) with a scarcity of labeled samples. Nevertheless, the …
Lightweight Automatic Modulation Classification Based on Efficient Convolution and Graph Sparse Attention in Low-Resource Scenarios
Z Cai, C Wang, W Ma, X Li… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Automatic Modulation Classification (AMC) is essential in non-cooperative communication
systems, since it enables the automatic recognition of signal modulation types. The recent …
systems, since it enables the automatic recognition of signal modulation types. The recent …
STARNet: An Efficient Spatiotemporal Feature Sharing Reconstructing Network for Automatic Modulation Classification
X Zhang, Z Wang, X Wang, T Luo… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Automatic Modulation Classification (AMC) is a crucial task in the field of wireless
communication, allowing for the identification of the modulation scheme of a received radio …
communication, allowing for the identification of the modulation scheme of a received radio …
Improving Recognition of Sub-GHz LPWANs: A Deep Learning Approach With the UPC-LPWAN-1 Dataset
E Maya-Olalla, M García-Lozano… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Deep neural networks (DNNs) have emerged as an effective technique for
modulation/system recognition but rely heavily on representative datasets. This paper …
modulation/system recognition but rely heavily on representative datasets. This paper …
单通道通信信号盲分离方法的研究进展综述
邓文， 黄知涛， 王翔 - 通信学报, 2023 - infocomm-journal.com
单通道盲信号分离(SCBSS) 技术相关理论与实践应用不断完善, SCBSS 方法的研究取得了较大
的进展. 在分析国内外大量学术研究成果的基础上, 基于通信信号盲信号分离(BSS) …
的进展. 在分析国内外大量学术研究成果的基础上, 基于通信信号盲信号分离(BSS) …