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Intelligent Sensor Fusion and AI Applications in Wireless Communication Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 908

Special Issue Editor


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Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4RN, UK
Interests: small-target detection; reinforce-learning; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As wireless networks become more complex and demand increases, integrating intelligent sensor fusion and AI is critical for optimizing performance, efficiency, and reliability. This issue delves into innovative methodologies and applications, highlighting how AI algorithms can process and analyze vast amounts of data from diverse sensors to improve network management, fault detection, and resource allocation. Key topics include the development of AI-driven models for real-time data fusion, advancements in machine learning techniques for predictive maintenance, and the implementation of intelligent systems for adaptive communication protocols. Additionally, the issue examines the role of sensor fusion in enhancing network security and enabling the Internet of Things (IoT) through seamless connectivity and interoperability. By showcasing cutting-edge research and practical applications, this Special Issue aims to sit at the frontier of the transformative impact of AI and sensor fusion on the future of wireless communication networks, paving the way for more resilient, efficient, and intelligent network infrastructures.

Dr. Xiaoyang Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • sensor fusion
  • network management
  • Internet of Things (IoT)
  • wireless communication networks

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Published Papers (1 paper)

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Research

17 pages, 1807 KiB  
Article
Spectrum Sensing in Very Low SNR Environment Using Multi-Scale Temporal Correlation Perception with Residual Attention
by Song Hong and Weiqiang Xu
Sensors 2025, 25(2), 528; https://doi.org/10.3390/s25020528 - 17 Jan 2025
Cited by 1 | Viewed by 610
Abstract
Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) [...] Read more.
Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales. Our simulations show that MSTC-PANet significantly improves the detection of channel occupancy at low signal-to-noise ratios (SNR), particularly in untrained scenarios with lower SNR conditions and modulation uncertainties. The analysis of the ROC curve indicates that at an SNR of -20 dB, the proposed MSTC-PANet achieves a detection rate of 98% with a false alarm rate of 10%. Furthermore, MSTC-PANet, which has been trained using digital modulation techniques, also demonstrates applicability to analog modulation. Full article
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