A Data-defined Naïve Bayes (DNB) based Decision Scheme for the Nonlinear Mitigation for OAM Mode Division Multiplexed Optical Fiber Communication

In this manuscript, a data-defined naive Bayes (DNB)-based decision scheme for nonlinear mitigation is presented for an orbital angular momentum (OAM) mode-division multiplexed optical fiber communication system. Due to the inherent nonlinearity characteristic of opto-electronic devices, the strong nonlinear impairments are deemed inevitable in OAM mode-division multiplexed transmission, leading to severely nonlinear effects. A DNB algorithm based on the prior probability distribution is adopted to mitigate the strong device nonlinearity of the OAM communication system, which is hard to solve using the conventional approaches due to the complex theoretical model of opto-electronic devices. An experiment using eight-mode OAM with a 32GBaud Nyquist QPSK signal optical fiber communication system is carried out with ring core fiber (RCF) transmission over 10 km to verify the effectiveness of the proposed scheme. The experimental results demonstrate that the nonlinear effects on OAM transmission can be effectively mitigated using a DNB-based decision with a bit error rate (BER) reduction of at most 66%. Moreover, compared with other nonlinear decision algorithms based on machine learning, such as support vector machine (SVM) or k-nearest neighbors (KNN), the digital signal processing complexity of the DNB algorithm is significantly reduced.