A modified convolutional neural network-based signal demodulation method for direct detection OFDM/OQAM-PON

Hui Yang, Xianzhuo Zhang, Anlin Yi, Rui Wang, Bangjiang Lin, Huanlai Xing, Binbin Sha

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number126843
JournalOptics Communications
Volume489
DOIs
StatePublished - Jun 15 2021
Externally publishedYes

Keywords

  • Convolutional neural network
  • Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM)
  • Passive optical network (PON)

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