Abstract

In this paper, we report our recent progress related to advanced digital signal processing (DSP) technologies to address the strong inter-core crosstalk (IC-XT) during multicore fiber (MCF) optical transmissions. MCF transmission technology has significant potential to break through the capacity crunch of single-mode fiber (SMF) transmissions. However, strong coupling among cores, namely, inter-core crosstalk (IC-XT), is unavoidable for high-density space-division multiplexing (SDM) transmissions using MCFs with the standard cladding size. To deal with this issue, we propose some novel DSP structures to eliminate IC-XT with considerable simplicity, based on the neural network equalizer (NNE)-based multiple-input and multiple-output digital signal processing (MIMO-DSP). The traditional NNE-based MIMO-DSP method has the ability to process the coupled SDM tributaries transmitted over MCFs; however, it exhibits complexity limitations for practical implementations. The implementation complexity of the NNE-based method is mainly attributed to the time-consumption of the training process and the large weight (neurons) numbers of the equalizers. Thus, we propose two main approaches to simplify NNE-based MIMO-DSP for the practical implementation of MCF transmissions: (1) To reduce the time-consumption of the training process in NNE-based MIMO-DSP, the idea of transfer learning (TL) is employed for initializing the weights, resulting in the faster convergence of the equalizers. (2) IC-XT cancellation is performed along with MIMO-DSP; thus, the dimensionality of MIMO-DSP could be reduced. To validate the performance improvement of the proposed machine learning DSP methods, both simulations and experiments related to transmissions and reception over MCFs were conducted. The results indicate that the proposed novel DSP structures possess the advantages of reduced complexity and improved robustness to IC-XT, which is beneficial for the next-generation high-density SDM transmissions.

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