TY - GEN
T1 - Pilot-Aided Deep Learning Based Phase Estimation for OFDM Systems with Wiener Phase Noise
AU - Wang, Qian
AU - Chen, Xingke
AU - Qian, Liping
AU - Du, Xinwei
AU - Yu, Changyuan
AU - Kam, Pooi Yuen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/11
Y1 - 2023/11
N2 - Orthogonal frequency-division multiplexing (OFDM) and high-order modulations have a wide range of applications in coherent optical communications. However, the existence of phase noise will greatly affect the system performance. To overcome this issue, this paper proposes a pilot-aided deep learning (PADL)-based phase estimation scheme, since deep learning has been a hot trend to be applied on digital signal processing in communications. To be specific, preliminary phase noise is first estimated by the pilot-aided (PA) method, and then the estimates are fed into the neural network to train for more accurate estimation. Simulation results show that the mean square error performance of the proposed method is much better than the conventional PA method. Especially for high-order modulations (M>64), the bit error rate of the PADL-based receiver is smaller than that with even double pilots used for phase estimation, which verifies a stronger robustness in our design using limited spectrum resources.
AB - Orthogonal frequency-division multiplexing (OFDM) and high-order modulations have a wide range of applications in coherent optical communications. However, the existence of phase noise will greatly affect the system performance. To overcome this issue, this paper proposes a pilot-aided deep learning (PADL)-based phase estimation scheme, since deep learning has been a hot trend to be applied on digital signal processing in communications. To be specific, preliminary phase noise is first estimated by the pilot-aided (PA) method, and then the estimates are fed into the neural network to train for more accurate estimation. Simulation results show that the mean square error performance of the proposed method is much better than the conventional PA method. Especially for high-order modulations (M>64), the bit error rate of the PADL-based receiver is smaller than that with even double pilots used for phase estimation, which verifies a stronger robustness in our design using limited spectrum resources.
KW - deep learning
KW - estimation
KW - OFDM
KW - phase noise
UR - http://www.scopus.com/inward/record.url?scp=85183297116&partnerID=8YFLogxK
U2 - 10.1109/ACP/POEM59049.2023.10368832
DO - 10.1109/ACP/POEM59049.2023.10368832
M3 - Conference article published in proceeding or book
AN - SCOPUS:85183297116
T3 - 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
BT - 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
Y2 - 4 November 2023 through 7 November 2023
ER -