Radio Frequency Database Construction and Modulation Recognition in Wireless Sensor Networks

Article Chemistry, Analytical

Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Kuiyu Chen et al.

Summary: An intelligent modulation recognition method based on intrapulse features of radar signals is proposed in this study, which can achieve intelligent recognition of various radar signals under low signal-to-noise ratios. Through image processing techniques and deep learning for classification in TFDIs, an overall successful recognition probability of 94.1% is achieved.

SENSORS (2021)

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Article Engineering, Electrical & Electronic

Wireless Sensor Networks for Detection and Localization of Subsea Oil Leakages

Gianluca Tabella et al.

Summary: This study focuses on the impact of using WSNs in oil spill detection and localization in Subsea Production Systems. By comparing different localization algorithms and utilizing ROC-based objective function for threshold design, the study aims to provide a more reliable global decision through fusion of local decisions.

IEEE SENSORS JOURNAL (2021)

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Article Computer Science, Information Systems

Spectrum Sensing for Cognitive Radio Network with Multiple Receive Antennas Under Impulsive Noise Environments

Seungwon Lee et al.

Summary: In this study, a non-linear combining scheme for spectrum sensing with multiple receive antennas in impulsive noise environments is proposed based on order statistics. Computer simulations demonstrate that the proposed scheme outperforms conventional schemes in impulsive noise environments with Rayleigh fading.

JOURNAL OF COMMUNICATIONS AND NETWORKS (2021)

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Article Computer Science, Information Systems

A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition

Hang Zhang et al.

Summary: The research proposes a multi-level feature extraction algorithm based on deep learning, utilizing a one-dimensional convolutional layer to enrich signal feature representation and emphasize the mutual influence between orthogonal signals. The algorithm performs well, achieving a recognition accuracy of 92.68% at high signal-to-noise ratio and significantly improving recognition accuracy for 16QAM and 64QAM.

WIRELESS NETWORKS (2021)

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Article Computer Science, Information Systems

Deep Learning Based Modulation Recognition With Multi-Cue Fusion

Tuo Wang et al.

Summary: A novel multi-cue fusion (MCF) network for automatic modulation recognition was proposed, achieving experimental results that outperformed the state-of-the-art works. The network consists of a signal cue multi-stream module and a visual cue discrimination module, utilizing CNN and IndRNN for modeling spatial-temporal correlations and extracting structural information from different data forms.

IEEE WIRELESS COMMUNICATIONS LETTERS (2021)

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Article Engineering, Electrical & Electronic