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Emerging Technologies for 6G Space–Air–Ground Integrated Networks (SAGIN)

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 1867

Special Issue Editors

School of Cyberspace Science and Technology/Beijing Institute of Technology, Beijing 100081, China
Interests: UAV Communications; next-generation radio access; information theory; non-orthogonal multiple access; coding theory and machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: space–air–ground integrated networks; space information networks; satellite communications

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Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: space-air communications; mobile communications; social governance; signal processing

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Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: space-air-ground integrated networks; physical layer security; satellite communications

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Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: wireless and mobile communications; information theory and coding; information learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: satellite ground integrated networks

Special Issue Information

Dear Colleagues,

Future 6G sensor networks will witness a paradigm shift towards heterogeneous devices, massive connections, and ubiquitous distributions towards 2030. In particular, it is expected that 6G networks can realize stringent seamless coverage with nearly 100% geographical areas including oceans, forests, and mountainous areas. Existing terrestrial networks suffer from restricted connectivity, low expandability, and limited spectral efficiency. There is a growing interest in technologies capable of coping with these challenges. Space–air–ground integrated networks (SAGIN) exhibit ubiquitous coverage, all-weather capability, and strong flexibility, which can meet the requirements of future 6G networks. Research on SAGIN needs to cover the design of network topology, physical layer technology, transmission security, etc. This Special Issue of Sensors is aiming at collecting state-of-the-art research papers related to the emerging technologies of 6G SAGIN for future sensor networks. Topics of interest include, but are not limited to, the following:

  • Physical-layer technologies design for SAGIN;
  • Multiple access technologies for SAGIN;
  • Random access technologies for SAGIN;
  • Transmission security for SAGIN;
  • Routing technologies for SAGIN;
  • Machine learning methods in SAGIN;
  • Spectrum sensing technologies for SAGIN;
  • Network architectures/transmission protocols for SAGIN;
  • SAGIN for IoT/IIOT/IoE;
  • Sensing/positioning/detection in SAGIN.

Sensors are used to collect interesting data on diversified scenarios, which enable smart cities and other ubiquitous monitoring applications. This Special Issue pays attention to the transmission of the data collected by sensors. In the future, sensor networks are expected to support sensing for ubiquitous areas, especially remote areas that cannot be easily covered by terrestrial networks. SAGIN can effectively realize ubiquitous coverage, massive connection, and robust data transmission. This is particularly attractive for supporting the emerging requirements of the future ubiquitous sensor networks.

Dr. Neng Ye
Dr. Di Zhou
Dr. Ruide Li
Dr. Jinpeng Song
Dr. Xiangming Li
Dr. Ran Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • 6G
  • space–air–ground integrated networks
  • physical layer technology
  • networking
  • security
  • massive connections

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Published Papers (2 papers)

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Research

20 pages, 7907 KiB  
Article
Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite–Ground Scenario
by Hui Cao, Shujun Han, Rui Meng, Xiaodong Xu and Ping Zhang
Sensors 2025, 25(1), 269; https://doi.org/10.3390/s25010269 - 6 Jan 2025
Viewed by 767
Abstract
Satellite–ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel [...] Read more.
Satellite–ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications. Nevertheless, current SemCom methods still struggle to adapt to varying channel conditions. To address this, we propose a SemCom transmission model based on a Channel Code-Book (CCB) for adaptive image transmission in diverse channel environments. Our model reconstructs and restores the original image by documenting fading and noise states under various channel conditions and dynamically adjusting the denoiser’s model parameters. Extensive experimental results demonstrate that our CCB model outperforms three representative baseline models, including Deep JSCC, ASCN, and WITT in various environments and task conditions, achieving an advantage of more than 10 dB under high signal-to-noise ratio conditions. Full article
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24 pages, 3764 KiB  
Article
Connectivity Recovery Based on Boundary Nodes and Spatial Triangle Fermat Points for Three-Dimensional Wireless Sensor Networks
by Hongsheng Chen and Ke Shi
Sensors 2024, 24(24), 7876; https://doi.org/10.3390/s24247876 - 10 Dec 2024
Viewed by 676
Abstract
In recent years, wireless sensor networks have been widely used, especially in three-dimensional environments such as underwater and mountain environments. However, in harsh environments, wireless sensor networks may be damaged and split into many isolated islands. Therefore, restoring network connectivity to transmit data [...] Read more.
In recent years, wireless sensor networks have been widely used, especially in three-dimensional environments such as underwater and mountain environments. However, in harsh environments, wireless sensor networks may be damaged and split into many isolated islands. Therefore, restoring network connectivity to transmit data effectively in a timely manner is particularly important. However, the problem of finding the minimum relay nodes is NP-hard, so heuristics methods are preferred. This paper presents a novel connectivity recovery strategy based on boundary nodes and spatial triangle Fermat points for three-dimensional wireless sensor networks. The isolated islands are represented as the boundary nodes, and the connectivity recovery problem is modeled as a graph connectivity problem. Three heuristics algorithms—the variant Kruskal algorithm, the variant Prim algorithm, and the spatial triangle Fermat point algorithm—are proposed to solve this problem. The variant Kruskal algorithm and the variant Prim algorithm connect the isolated islands by constructing the minimum spanning tree to link all the boundary nodes and placing relay nodes along the edges of this tree. We derive an accurate formula to determine the coordinates of spatial triangle Fermat points. Based on this formula, the spatial triangle Fermat point algorithm constructs a Steiner tree to restore network connectivity. Extensive simulation experiments demonstrate that our proposed algorithms perform better than the existing algorithm. Full article
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