English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a multi-impairment compensation scheme based on deep learning and low-density parity-check (LDPC) code for the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. We first propose a multi-impairment compensation autoencoder (MICAE) based on deep neural network (DNN). Then we combine the proposed MICAE with LDPC code and design a DNN-based decoder for LDPC decoding to replace the traditional belief propagation (BP) decoder. The proposed scheme can compensate for multiple impairments simultaneously and has faster decoding speed, greatly improving the performance of the CO-OFDM transmission system. We demonstrate the superiority of the proposed scheme through simulations in different CO-OFDM transmission systems. Simulation results show that the proposed scheme can effectively improve the Q-factor and reduce the bit error rate (BER) of the system, and is more suitable for complex long-distance and high-speed optical transmission scenarios.

Multi-impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code