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Emerging Trends in Space–Air–Ground Integrated Networking and Communication Systems
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Emerging Trends in Space–Air–Ground Integrated Networking and Communication Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 1060

Special Issue Editors

Dr. Chao Zhu

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: satellite internet; Internet of Vehicle (IoV) network; edge computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Yaxi Liu

E-Mail Website
Guest Editor
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: unmanned aerial vehicle (UAV) network; wireless network; integrated sensing and communication (ISAC) network; MIMO
Dr. Xuhui Ding

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: satellite communications; cognitive radio; MIMO communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As society undergoes a comprehensive transformation towards intelligence and digitalization, the demand for truly ubiquitous and seamless connectivity is extending beyond terrestrial boundaries. This has led to the emergence of Space–Air–Ground Integrated (SAGI) Networking as a key paradigm for future sixth-generation (6G) systems. This Special Issue aims to explore the new network paradigms required as we transition from fifth-generation (5G) to 6G, with a specific focus on the architecture, technologies, and applications of SAGI systems.

The vision for 6G includes stringent new demands for global coverage, extreme capacity, ultra-low latency, and massive connectivity. SAGI networks, by deeply integrating satellite constellations, unmanned aerial vehicle (UAV) swarms, and terrestrial communication networks (including radio access networks and mobile vehicle networks), are uniquely positioned to meet these challenges. This integration creates a hierarchical, resilient, and intelligent infrastructure capable of providing seamless service continuity and ubiquitous connectivity, from dense urban centers to remote and underserved areas.

Furthermore, the pervasive presence of dense, intelligent Internet of Things (IoT) devices and user devices within this integrated network opens up new possibilities. These ubiquitous devices can act as edge nodes, collaborating with the SAGI network to enable advanced cloud, fog, and edge computing services, thus bringing computation closer to the data source. In addition, these advanced SAGI networks are expected to deeply integrate other promising 6G technologies to further enhance performance, such as Integrated Sensing and Communication (ISAC), Intelligent Reflecting Surfaces (IRS), terahertz (THz) communications, and massive MIMO. We aim to bring together cutting-edge research that addresses the fundamental challenges of these future networks in terms of scalability, efficiency, intelligence, and security.

The aim of this Special Issue of Electronics is to present the emerging trends in SAGI networking and communication systems. We invite researchers to contribute original and unique articles, as well as sophisticated review articles. Topics include, but are not limited to, the following areas:

  • Satellite networking and communication systems;
  • Unmanned Aerial Vehicle (UAV) networking and communication systems;
  • 5G-A/6G radio access network;
  • Internet of Things (IoT) networking and communication systems;
  • Cloud, fog, and edge computing paradigms;
  • AI-empowered SAGI network;
  • Federated Learning in SAGI network;
  • Integrated Sensing and Communication (ISAC)-empowered SAGI network;
  • Intelligent Reflecting Surfaces (IRS)-empowered SAGI network;
  • Terahertz (THz)-empowered SAGI network;
  • Massive MIMO-empowered SAGI network;
  • Security and privacy preservation in SAGI network.

We look forward to receiving your contributions.

Dr. Chao Zhu
Dr. Yaxi Liu
Dr. Xuhui Ding
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • space–air–ground integrated (SAGI) networking
  • AI-empowered SAGI network
  • IoT networking and communication systems
  • UAV networking
  • integrated sensing and communication
  • massive MIMO-security and privacy preservation

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Published Papers (2 papers)

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Research

24 pages, 813 KB  
Article
TopoAgent: A Constraint-Structured Reinforcement Learning Agent for Heterogeneous Satellite Mission Scheduling
by Yi Ren, Shuyi Liu, Xiao Chen, Yuan Gao, Zeyu Zhang and Ruide Li
Electronics 2026, 15(11), 2456; https://doi.org/10.3390/electronics15112456 - 4 Jun 2026
Abstract
With more satellites, richer payload resources, and more diverse service functions, satellite systems are increasingly operated as large space–ground networks. These networks must schedule arriving missions under changing topology, gateway access, beam availability, weather-affected links, spectrum compatibility, and mission time windows. Offline optimization [...] Read more.
With more satellites, richer payload resources, and more diverse service functions, satellite systems are increasingly operated as large space–ground networks. These networks must schedule arriving missions under changing topology, gateway access, beam availability, weather-affected links, spectrum compatibility, and mission time windows. Offline optimization can compute high-quality schedules when the mission set, satellite visibility windows, and resource states are known before execution, but repeated replanning is costly for asynchronous arrivals. Online heuristics make faster decisions from local route rules, but they do not evaluate how an accepted service path changes the capacity left for later requests. Reinforcement-learning schedulers can adapt from delayed scheduling outcomes. However, many generic policies rely on fixed-step state updates or flat compound-action scores, whereas online satellite scheduling makes decisions at irregular arrivals over continuously evolving topology and capacity-coupled service paths. We propose TopoAgent, an online reinforcement-learning agent for heterogeneous satellite mission scheduling. TopoAgent models each request as a service-path decision, propagates compound feasibility through the satellite–gateway–beam hierarchy, and uses a capacity-aware policy to choose among feasible paths. A deterministic constraint manager places the selected path in time, while SRV guides the policy toward assignments that preserve reusable beam capacity. In a high-fidelity simulator, TopoAgent achieves a 74.7% mission completion rate and a 75.5% high-priority completion ratio over five seeds. Full article
(This article belongs to the Special Issue Emerging Trends in Space–Air–Ground Integrated Networking and Communication Systems)
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23 pages, 1857 KB  
Article
ObsBattery: Position-Aware Federated Learning with Dueling DQN Clustering and Training Adaptation for Satellite Battery Prediction
by Shuo Jiang, Boyu Wang, Xuan Zhang, Yaoxian Jiang, Shuyi Liu, Zhenyu Zhao, Ruide Li and Xiao Chen
Electronics 2025, 14(23), 4697; https://doi.org/10.3390/electronics14234697 - 28 Nov 2025
Viewed by 652
Abstract
Satellite battery status prediction is crucial for ensuring the healthy operation of future satellite constellations. However, traditional telemetry-based methods, where satellite battery status is transmitted in real time to ground stations for processing, consume significant satellite bandwidth and introduce response delays. Advances in [...] Read more.
Satellite battery status prediction is crucial for ensuring the healthy operation of future satellite constellations. However, traditional telemetry-based methods, where satellite battery status is transmitted in real time to ground stations for processing, consume significant satellite bandwidth and introduce response delays. Advances in onboard computing and federated learning (FL) enable local model training and centralized parameter aggregation, reducing transmission overhead while leveraging distributed satellite data. Nevertheless, the unique orbital motion of satellites presents challenges for FL, primarily due to battery status heterogeneity arising from varying sunlight exposure. Limited onboard energy further necessitates balancing model performance with battery efficiency during local training. To tackle these issues, we propose ObsBattery—a position-aware FL framework that clusters satellites based on their orbital positions to improve model accuracy. ObsBattery employs a Dueling Deep Q-Network to dynamically determine satellite clustering and adapt local training rounds according to power availability, thereby reducing energy consumption during low-power phases. Evaluations on a real-world satellite battery dataset show that ObsBattery significantly improves both prediction accuracy and energy efficiency. Compared to a standard clustered FL approach, it reduces model MAE by 16% and energy consumption ratio by 6% under experimental conditions. Full article
(This article belongs to the Special Issue Emerging Trends in Space–Air–Ground Integrated Networking and Communication Systems)
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