Abstract
Accurate and generalized channel modeling is a fundamental requirement for the design and optimization of a next generation hollow-core fiber (HCF) communication systems. Neural networks have recently emerged as powerful tools for channel emulation, but existing models may still be limited in fully characterizing the unique stochastic impairments of HCFs. These impairments include phase noise arising from inter-modal interference (IMI) and surface scattering. Conventional real-valued neural networks typically process the in-phase and quadrature (I/Q) components of the optical signal as independent variables. This separation may reduce the ability of the network to capture the intrinsic phase-related characteristics of the channel. In this paper, we propose a complex-valued diffusion (CV-diffusion) model for high-fidelity channel emulation. By treating the I/Q components as a unified complex entity, the emulator preserves phase correlations and jointly captures amplitude–phase distortions. In addition, the progressive diffusion formulation decomposes channel modeling into tractable steps, improving generalization across varying channel conditions. An experiment was conducted in a 20-Gbaud dual-polarization 64-quadrature amplitude modulation (DP-64QAM) 40-channel wavelength-division multiplexed (WDM) hollow-core-fiber (HCF) communication system, and the results showed that the proposed CV-diffusion emulator outperformed a real-valued conditional generative adversarial network (CGAN) emulator and a real-valued diffusion (RV-diffusion) emulator. At an OSNR of 22.5 dB, the proposed CV-diffusion emulator achieved its maximum performance gain, with modeling-accuracy improvements of 38.6% and 31.1% compared with the CGAN and RV-diffusion emulators, respectively. Our experimental results demonstrate that the proposed CV-diffusion emulator is a promising candidate for accurate and generalized channel modeling in HCF optical communication systems.
| Original language | English |
|---|---|
| Pages (from-to) | 20458-20472 |
| Number of pages | 15 |
| Journal | Optics Express |
| Volume | 34 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 1 Jun 2026 |
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