TY - GEN
T1 - Signal Classification for Safety Critical Aeronautical Communications for Anti-Jamming using Artificial Intelligence
AU - Asif, Rameez
AU - Hu, Yim Fun
AU - Ali, Muhammad
AU - Li, Jian Ping
AU - Abdo, Kanaan
N1 - 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.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Aeronautical communications
KW - Anti Jamming
KW - Artificial Intelligence
KW - Machine Learning
KW - Modulation Classification
KW - RF Signal Classification
UR - http://www.scopus.com/inward/record.url?scp=85122811241&partnerID=8YFLogxK
U2 - 10.1109/DASC52595.2021.9594496
DO - 10.1109/DASC52595.2021.9594496
M3 - Conference contribution
AN - SCOPUS:85122811241
T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings
BT - 40th Digital Avionics Systems Conference, DASC 2021 - Proceedings
PB - The Institute of Electrical and Electronics Engineers (IEEE)
T2 - 40th IEEE/AIAA Digital Avionics Systems Conference, DASC 2021
Y2 - 3 October 2021 through 7 October 2021
ER -