Reservoir computing for vector-mode decomposition in deep learning frameworks

Abstract This paper introduces an optical mode decomposition (OMD) framework for the core-shifted few-mode fiber (FMF) channel, leveraging the nonlinear mapping capabilities of reservoir computing (RC) integrated with deep learning architectures. The core-shift nature, characterized by radial and phase offsets, transforms the originally simpler OMD task—focused solely on mode weights and phases—into a significantly more complex problem. We integrate RC with deep learning, where RC replaces the nonlinear processing layer in traditional neural networks for hardware-friendly implementations. Simulation results demonstrate that the proposed network maintains a high correlation coefficient of 94% while reducing computational complexity by 32% for two vector modes. The framework’s robustness is further validated for cases involving more vector modes, confirming its potential for practical hardware implementation. This study not only advances the field of OMD by addressing the complexities introduced by core shift but also provides a novel pathway for the hardware realization of deep learning models in optical communication systems.