Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code - Cong Hu; Yuanxiang Chen; Ying Han; Jianguo Yu (IEEE Photonics Journal, 2024)
In this article, 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.