Joint probabilistic shaping and pre-equalization for hollow-core fiber transmission using end-to-end learning

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.