TY - GEN
T1 - Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning
AU - Alhazmi, Hatim
AU - Almarhabi, Alhussain
AU - Samarkandi, Abdullah
AU - Alymani, Mofadal
AU - Alhazmi, Mohsen H.
AU - Sheng, Zikang
AU - Yao, Yu Dong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.
AB - Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.
KW - Phase noise
KW - constellation diagram
KW - deep learning
KW - phase shift keying
UR - http://www.scopus.com/inward/record.url?scp=85091917539&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091917539&partnerID=8YFLogxK
U2 - 10.1109/WOCC48579.2020.9114928
DO - 10.1109/WOCC48579.2020.9114928
M3 - Conference contribution
AN - SCOPUS:85091917539
T3 - 2020 29th Wireless and Optical Communications Conference, WOCC 2020
BT - 2020 29th Wireless and Optical Communications Conference, WOCC 2020
T2 - 29th Wireless and Optical Communications Conference, WOCC 2020
Y2 - 1 May 2020 through 2 May 2020
ER -