Signal Classification for Safety Critical Aeronautical Communications for Anti-Jamming using Artificial Intelligence

Asif, Rameez, Hu, Yim Fun, Ali, Muhammad, Li, Jian Ping and Abdo, Kanaan (2021) Signal Classification for Safety Critical Aeronautical Communications for Anti-Jamming using Artificial Intelligence. In: 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings. AIAA/IEEE Digital Avionics Systems Conference - Proceedings . The Institute of Electrical and Electronics Engineers (IEEE), USA. ISBN 9781665434201

Full text not available from this repository. (Request a copy)


Artificial intelligence (AI) techniques such as machine learning (ML) and specifically deep learning (DL) has brought significant success to many applications areas such as marketing, computer vision, medical imaging etc. However, the use of these techniques in the wireless communications domain has not been very well explored. In fact, artificial intelligence can play a vital role for communication systems that require a high degree of availability and reliability such as in the field of aeronautical communications for air traffic control. With the ever-growing increase in the air traffic any loss of communication due to jamming can result in devastating effects. For such safety critical communications, the deep learning based intelligent systems can play an important role to support anti-jamming. In this paper, the performance of a deep learning based convolutional neural network for signal modulation classification in safety critical aeronautical communications has been explored as an alternative to traditional methods.

Item Type: Book Section
Additional Information: Funding Information: This research has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation program under grant agreement No 892002. Funding Information: This research has also received funding from the CS2 Joint Undertaking and the European Union as part of Horizon 2020 program. Opinions expressed in this work reflect the authors’ views only, and the SJU shall not be considered liable for them or for any use that may be made of the information contained herein. Publisher Copyright: © 2021 IEEE.
Uncontrolled Keywords: aeronautical communications,anti jamming,artificial intelligence,machine learning,modulation classification,rf signal classification,aerospace engineering,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2202
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 23 Aug 2022 13:30
Last Modified: 14 Mar 2023 08:38
DOI: 10.1109/DASC52595.2021.9594496

Actions (login required)

View Item View Item