Signal Classification for Safety Critical Aeronautical Communications for Anti-Jamming using Artificial Intelligence
Rameez Asif, Yim Fun Hu, Muhammad Ali, Jianping Li, Kanaan Abdo · 2021
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.