Joint probabilistic shaping and pre-equalization for hollow-core fiber transmission using end-to-end learning
Qi Xu, Ran Gao, Fei Wang, Zhaohui Cheng, Yi Cui, Zhipei Li, Dong Guo, Huan Chang, Lei Zhu, Qi Zhang, Xiaolong Pan, Shikui Shen, Guangquan Wang, Yanbiao Chang, Zheyu Wu, Xiangjun Xin
Abstract
In this Letter, we propose a novel, to the best of our knowledge, end-to-end (E2E) learning scheme leveraging a time-frequency decoupling network (TFDnet) for joint probabilistic shaping (PS) and pre-equalization in hollow-core fiber (HCF)-based wavelength division multiplexing (WDM) systems. The TFDnet emulator effectively models HCF transmission channels by decoupling signal impairments into high-frequency, linear, and nonlinear distortions. Furthermore, a TFDnet emulator-based E2E strategy for joint PS and pre-equalization is presented with the aim of compensating the signal impairment for the HCF-based WDM systems. An experiment is conducted on a 30-channel HCF-based WDM system over a 10 km HCF. The experimental results demonstrate that the proposed TFDnet-based joint PS and pre-equalization scheme achieves the same bit-error rate (BER) performance with optical signal-to-noise ratio (OSNR) improvements of 1.0 dB and 1.6 dB compared to conditional generative adversarial network (CGAN)-based and traditional joint PS and pre-equalization scheme, respectively, under a 20% hard-decision forward error correction (HD-FEC) threshold. These results highlight the potential of the proposed scheme for ultrahigh-capacity HCF communication systems.