Fast mode decomposition for few-mode fiber based on lightweight neural network

In this paper, we present a fast mode decomposition method for few-mode fibers, utilizing a lightweight neural network called MobileNetV3-Light. This method can quickly and accurately predict the amplitude and phase information of different modes, enabling us to fully characterize the optical field without the need for expensive experimental equipment. We train the MobileNetV3-Light using simulated near-field optical field maps, and evaluate its performance using both simulated and reconstructed near-field optical field maps. To validate the effectiveness of this method, we conduct mode decomposition experiments on a few-mode fiber supporting six linear polarization(LP) modes(LP01, LP11e, LP11o, LP21e, LP21o, LP02). The results demonstrate a remarkable average correlation of 0.9995 between our simulated and reconstructed near-field lightfield maps. And the mode decomposition speed is about 6 ms per frame, indicating its powerful real-time processing capability. In addition, the proposed network model is compact, with a size of only 6.5 MB, making it well suited for deployment on portable mobile devices.