TY - GEN
T1 - QAM Signal Classification and Timing Jitter Identification Based on Eye Diagrams and Deep Learning
AU - Almarhabi, Alhussain
AU - Alhazmi, Hatim
AU - Samarkandi, Abdullah
AU - Alymani, Mofadal
AU - Alhazmi, Mohsen H.
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - automatic modulation classification
KW - deep learning
KW - eye-diagram
KW - jitters
KW - signal integrity
KW - spectrum awareness
UR - http://www.scopus.com/inward/record.url?scp=85123441237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123441237&partnerID=8YFLogxK
U2 - 10.1109/WOCC53213.2021.9603028
DO - 10.1109/WOCC53213.2021.9603028
M3 - Conference contribution
AN - SCOPUS:85123441237
T3 - 2021 30th Wireless and Optical Communications Conference, WOCC 2021
SP - 1
EP - 5
BT - 2021 30th Wireless and Optical Communications Conference, WOCC 2021
T2 - 30th Wireless and Optical Communications Conference, WOCC 2021
Y2 - 7 October 2021 through 8 October 2021
ER -