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Aerospace
Special Issues
AI-Enabled Signal Processing for Space–Air–Ground Integrated Networks
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AI-Enabled Signal Processing for Space–Air–Ground Integrated Networks

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 15 October 2024 | Viewed by 1327

Special Issue Editor

Dr. Xin Liu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: cognitive radio; internet of things; spectrum sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the modern landscape of communication systems, the integration of satellites, aerospace vehicles, aircraft, and ground cellular networks, namely Space–Air–Ground Integrated Networks (SAGINs), presents unparalleled opportunities alongside unique challenges. SAGINs signify a paradigm shift in the telecommunications sector, offering seamless connectivity and ubiquitous coverage across diverse terrains and altitudes. However, the effective operation of such networks necessitates complex signal processing solutions that are capable of adapting to rapidly changing environmental conditions, mobility patterns, and user demands. Artificial intelligence (AI)-enabled signal processing holds transformative potential, providing unprecedented capabilities in real-time data analysis, adaptive resource allocation, and intelligent decision making. Traditional signal processing algorithms often falter in scenarios characterized by rapid mobility, frequent handovers, and unpredictable channel conditions, posing significant obstacles to reliable communication. However, through the integration of AI, innovative solutions can be designed to not only mitigate the impact of dynamic environments but also leverage them to enhance network efficiency and resilience. This Special Issue aims to explore the profound intersection of AI and signal processing within SAGINs, with a primary focus on enhancing network performance and overcoming the inherent challenges posed by the high dynamics of aircraft and satellites.

Topics of interest include but are not limited to the following:

  • Adaptive signal processing techniques for dynamic environments;
  • Dynamic detection of aircraft and satellites in SAGINs;
  • Resource allocation for SAGINs using AI algorithms;
  • Machine learning for SAGIN optimization;
  • Security and privacy enhancement in SAGINs;
  • Adaptive beamforming and MIMO techniques for SAGINs;
  • Edge computing in SAGINs;
  • Adaptive routing algorithms for SAGINs.

Dr. Xin Liu
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Aerospace is an international peer-reviewed open access monthly 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 networks
  • signal processing
  • SAGIN

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Published Papers (2 papers)

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Research

21 pages, 2886 KiB  
Article
Hybrid Detection Method for Multi-Intent Recognition in Air–Ground Communication Text
by Weijun Pan, Zixuan Wang, Zhuang Wang, Yidi Wang and Yuanjing Huang
Aerospace 2024, 11(7), 588; https://doi.org/10.3390/aerospace11070588 - 18 Jul 2024
Viewed by 402
Abstract
In recent years, the civil aviation industry has actively promoted the automation and intelligence of control processes with the increasing use of various artificial intelligence technologies. Air–ground communication, as the primary means of interaction between controllers and pilots, typically involves one or more [...] Read more.
In recent years, the civil aviation industry has actively promoted the automation and intelligence of control processes with the increasing use of various artificial intelligence technologies. Air–ground communication, as the primary means of interaction between controllers and pilots, typically involves one or more intents. Recognizing multiple intents within air–ground communication texts is a critical step in automating and advancing the control process intelligently. Therefore, this study proposes a hybrid detection method for multi-intent recognition in air–ground communication text. This method improves recognition accuracy by using different models for single-intent texts and multi-intent texts. First, the air–ground communication text is divided into two categories using multi-intent detection technology: single-intent text and multi-intent text. Next, for single-intent text, the Enhanced Representation through Knowledge Integration (ERNIE) 3.0 model is used for recognition; while the A Lite Bidirectional Encoder Representations from Transformers (ALBERT)_Sequence-to-Sequence_Attention (ASA) model is proposed for identifying multi-intent texts. Finally, combining the recognition results from the two models yields the final result. Experimental results demonstrate that using the ASA model for multi-intent text recognition achieved an accuracy rate of 97.84%, which is 0.34% higher than the baseline ALBERT model and 0.15% to 0.87% higher than other improved models based on ALBERT and ERNIE 3.0. The single-intent recognition model achieved an accuracy of 96.23% when recognizing single-intent texts, which is at least 2.18% higher than the multi-intent recognition model. The results indicate that employing different models for various types of texts can substantially enhance recognition accuracy. Full article
(This article belongs to the Special Issue AI-Enabled Signal Processing for Space–Air–Ground Integrated Networks)
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19 pages, 1320 KiB  
Article
Interference Study of 5G System on Civil Aircraft Airborne Beidou RDSS System in Takeoff and Landing Phase
by Wantong Chen, Yuyin Tian, Shuguang Sun and Ruihua Liu
Aerospace 2024, 11(7), 522; https://doi.org/10.3390/aerospace11070522 - 27 Jun 2024
Cited by 1 | Viewed by 465
Abstract
Radio Determination Satellite Service (RDSS) is a characteristic service of BeiDou, which can provide users with short message communication services. Since the working frequency of an RDSS system is close to that of a 5G system, the RDSS system is very susceptible to [...] Read more.
Radio Determination Satellite Service (RDSS) is a characteristic service of BeiDou, which can provide users with short message communication services. Since the working frequency of an RDSS system is close to that of a 5G system, the RDSS system is very susceptible to interference from 5G out-of-band radiation. This paper analyzes the compatibility of 5G interference with an RDSS system from the perspective of the signal and the system. Firstly, the compatibility assessment is carried out from the perspective of the signal, the impact of interference on the capture and tracking performance of BeiDou is illustrated, and the safe coexistence distance of the two systems from the perspective of capture probability is obtained from the perspective of the signal. Subsequently, based on the link budget criterion, the interference of 5G base stations and 5G terminals to RDSS receivers under different frequency isolation and the required distance isolation for safe coexistence are analyzed from the system perspective. Finally, from the perspective of civil aviation safety, the aggregate interference is used as an evaluation index to evaluate the interference suffered by the aircraft during takeoff and landing and to obtain the interference suffered by the ground-based 5G base station during the takeoff and landing of the aircraft on different routes and in different 5G propagation environments. The simulation results show that when the airplane is closer to the ground, the ground 5G base stations will cause harmful interference to the RDSS receiver. In this study, the real flight data are combined with the simulation model to obtain the exact influence range of 5G interference on the RDSS system under different viewpoints. Full article
(This article belongs to the Special Issue AI-Enabled Signal Processing for Space–Air–Ground Integrated Networks)
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