TY - GEN
T1 - Modulation Classification Based on Eye Diagrams and Deep Learning
AU - Almarhabi, Alhussain
AU - Alhazmi, Hatim
AU - Samarkandi, Abdullah
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - New emerging technologies such as the Internet of Things (IoT) and fifth generation wireless communication New Radio (5G NR) are introducing challenges in spectrum and systems' complexity. Radio spectrum awareness is overcoming many challenges through tasks related to signal detection and channel identification that improves overall the system's reliability, efficiency, and security. The eye diagram is one of the signal representations useful in many applications for simulation and debugging of the system. The eye diagram shows vital parameters such as timing jitter and inter-symbol interference. The eye diagram contains essential features that could be used for spectrum awareness tasks. This paper uses deep learning with an eye diagram to study and identify modulated signals in narrowband fading channels, e.g., Rayleigh and Rician fading. Our results show that deep learning neural networks can classify modulated signals with the impact of the fading channel using an eye diagram.
AB - New emerging technologies such as the Internet of Things (IoT) and fifth generation wireless communication New Radio (5G NR) are introducing challenges in spectrum and systems' complexity. Radio spectrum awareness is overcoming many challenges through tasks related to signal detection and channel identification that improves overall the system's reliability, efficiency, and security. The eye diagram is one of the signal representations useful in many applications for simulation and debugging of the system. The eye diagram shows vital parameters such as timing jitter and inter-symbol interference. The eye diagram contains essential features that could be used for spectrum awareness tasks. This paper uses deep learning with an eye diagram to study and identify modulated signals in narrowband fading channels, e.g., Rayleigh and Rician fading. Our results show that deep learning neural networks can classify modulated signals with the impact of the fading channel using an eye diagram.
KW - automatic modulation classification
KW - deep learning
KW - eye-diagram
KW - internet of things
KW - narrow-band
KW - spectrum awareness
UR - http://www.scopus.com/inward/record.url?scp=85139188050&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139188050&partnerID=8YFLogxK
U2 - 10.1109/WOCC55104.2022.9880590
DO - 10.1109/WOCC55104.2022.9880590
M3 - Conference contribution
AN - SCOPUS:85139188050
T3 - 2022 31st Wireless and Optical Communications Conference, WOCC 2022
SP - 35
EP - 40
BT - 2022 31st Wireless and Optical Communications Conference, WOCC 2022
T2 - 31st Wireless and Optical Communications Conference, WOCC 2022
Y2 - 11 August 2022 through 12 August 2022
ER -