TY - JOUR
T1 - A modified convolutional neural network-based signal demodulation method for direct detection OFDM/OQAM-PON
AU - Yang, Hui
AU - Zhang, Xianzhuo
AU - Yi, Anlin
AU - Wang, Rui
AU - Lin, Bangjiang
AU - Xing, Huanlai
AU - Sha, Binbin
N1 - Funding Information:
This work was supported in part by National Key Research and Development Program of China (Grant No. 2018YFB2201103 ); Sichuan Science and Technology Program, China (Grant No. 2020YFSY0021 ); Science and Technology Program of Quanzhou, China (Grant No. 2019C010R ); Chunmiao Project of Haixi Institutes, CAS, China .
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) is a promising modulation candidate for passive optical network (PON) due to its high flexibility, better time–frequency focusing characteristic, great resistance to the inter symbol and inter carrier interferences and higher spectrum efficiency compared to OFDM. However, the intrinsic imaginary interference together with linear and nonlinear distortions make it more difficult to recover the transmitted OFDM/OQAM signal at the receiver side. To mitigate the transmission impairments, a modified convolutional neural network (CNN) is utilized to learn the channel state information and the constellation demapping mechanism for OFDM/OQAM-PON. The distorted received signals are equalized implicitly to obtain the transmitted binary bits directly. The simulation results show that the CNN based receiver (Rx) can compensate the linear and nonlinear distortions more effectively compared to traditional pilot-based Rx, especially for the high order modulation long-reach PON.
AB - Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) is a promising modulation candidate for passive optical network (PON) due to its high flexibility, better time–frequency focusing characteristic, great resistance to the inter symbol and inter carrier interferences and higher spectrum efficiency compared to OFDM. However, the intrinsic imaginary interference together with linear and nonlinear distortions make it more difficult to recover the transmitted OFDM/OQAM signal at the receiver side. To mitigate the transmission impairments, a modified convolutional neural network (CNN) is utilized to learn the channel state information and the constellation demapping mechanism for OFDM/OQAM-PON. The distorted received signals are equalized implicitly to obtain the transmitted binary bits directly. The simulation results show that the CNN based receiver (Rx) can compensate the linear and nonlinear distortions more effectively compared to traditional pilot-based Rx, especially for the high order modulation long-reach PON.
KW - Convolutional neural network
KW - Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM)
KW - Passive optical network (PON)
UR - http://www.scopus.com/inward/record.url?scp=85101383999&partnerID=8YFLogxK
U2 - 10.1016/j.optcom.2021.126843
DO - 10.1016/j.optcom.2021.126843
M3 - Article
AN - SCOPUS:85101383999
SN - 0030-4018
VL - 489
JO - Optics Communications
JF - Optics Communications
M1 - 126843
ER -