Abstract
Radio spectrum awareness is an important topic to overcome many challenges, such as spectrum utilization and sharing appearing with the development of technologies in wireless communications. Some practical tasks of radio spectrum awareness are related to signal detection and identification for improving the system's reliability, efficiency, and security. Eye diagrams are essential for measurement tools used by engineers to simulate, evaluate and debug systems. Eye diagrams reflect many vital parameters for signal integrity degradation, such as timing jitter, crosstalk, and inter-symbol interference. Therefore, using an eye diagram containing a valuable feature from the system could be helpful for spectrum awareness tasks. In this paper, we use deep learning to study and identify classes within quadrature amplitude modulation using eye diagrams and explored related impacts to enable radio spectrum awareness. Our results show that deep learning neural networks capable of classifying quadrature amplitude modulation types with eye diagrams at presenting timing jitter and varying signal-to-noise ratios.