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Integrated Sensing, Communications, and Computing: Enabling Technologies and Applications for Intelligent Communication Networks and Low-Altitude Economy

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 890

Special Issue Editors


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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: ISAC; array signal processing; beamforming; microwave imaging; anti-jamming

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Guest Editor
Department of Information and Communication Technologies, Universidad Politécnica de Cartagena (UPCT), Campus Muralla del Mar, E-30202 Cartagena, Spain
Interests: wireless networks; internet of things; nanocommunications; streaming services
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Special Issue Information

Dear Colleagues,

The convergence of Integrated Sensing, Communications, and Computing (ISCC) is revolutionizing next-generation intelligent communication networks and the emerging low-altitude economy. As advancements in 6G, artificial intelligence (AI), and edge computing accelerate, ISCC is enabling seamless interaction between terrestrial and aerial networks, supporting applications such as unmanned aerial vehicles (UAVs), urban air mobility (UAM), and smart transportation. The integration of ISCC in intelligent communication networks plays a crucial role in enhancing situational awareness, resource allocation, and real-time decision-making for air–ground collaborative systems. This Special Issue aims to explore the latest research, cutting-edge technologies, and real-world applications at the intersection of ISCC, intelligent communication networks, and the low-altitude economy. This Special Issue focuses on the following topics:

  • Waveform, sequence, coding, modulation, and beamforming design for ISCC;
  • MIMO, massive MIMO, and ultra-massive MIMO technologies for ISCC;
  • Sensing and positioning in communication networks;
  • Near-field transmission for ISCC;
  • Radar detection and imaging technologies;
  • ISCC design and optimization in V2X, UAV, and UAM networks;
  • Artificial intelligence for ISCC;
  • Integration of communication and multi-modal sensing.

Dr. Yi Liao
Prof. Dr. Rafael Asorey-Cacheda
Guest Editors

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Keywords

  • integrated sensing, communications, and computing (ISCC)
  • intelligent communication networks
  • waveform design for ISCC
  • sensing and positioning in communication networks
  • multi-modal sensing and communication integration
  • beamforming techniques
  • MIMO, massive MIMO, and ultra-massive MIMO

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Published Papers (1 paper)

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Research

18 pages, 4214 KB  
Article
Frequency-Agility-Based Neural Network with Variable-Length Processing for Deceptive Jamming Discrimination
by Wei Gong, Renting Liu, Yusheng Fu, Deyu Li and Jian Yan
Sensors 2025, 25(17), 5471; https://doi.org/10.3390/s25175471 - 3 Sep 2025
Viewed by 614
Abstract
With the booming development of the low-altitude economy and the widespread application of Unmanned Aerial Vehicles (UAVs), integrated sensing and communication (ISAC) technology plays an increasingly pivotal role in intelligent communication networks. However, low-altitude platforms supporting ISAC, such as UAV swarms, are highly [...] Read more.
With the booming development of the low-altitude economy and the widespread application of Unmanned Aerial Vehicles (UAVs), integrated sensing and communication (ISAC) technology plays an increasingly pivotal role in intelligent communication networks. However, low-altitude platforms supporting ISAC, such as UAV swarms, are highly vulnerable to deception jamming in complex electromagnetic environments. Existing multistatic radar systems face challenges in processing slowly fluctuating targets (like low-altitude UAVs) and adapting to complex electromagnetic environments when fusing multiple pulse echoes. To address this issue, targeting the protection needs of low-altitude targets like UAVs, this paper leverages the characteristic of rapid amplitude fluctuation in frequency-agile radar echoes to analyze the differences between true and false targets in multistatic frequency-agile radar systems, particularly for slowly fluctuating UAV targets, demonstrating the feasibility of discrimination. Building on this, we introduce a neural network approach to deeply extract discriminative features from true and false target echoes and propose a neural network-based variable-length processing method for deception jamming discrimination in multistatic frequency-agile radar. The simulation results show that the proposed method effectively exploits deep-level echo features, significantly improving the discrimination probability between true and false targets, especially for slowly fluctuating UAV targets. Crucially, even when trained on a fixed number of pulses, the model can process input data with varying pulse counts, greatly enhancing its practical deployment capability in dynamic UAV mission scenarios. Full article
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