In this paper, an algorithm based on homomorphic deconvolution is proposed to give an accurate estimation of nonlinear phase in the beat signal in optical frequency domain reflectometry (OFDR). Nonlinearities in the beat signal are obtained by using an auxiliary interferometer. After converting to cepstrum domain and filtering, the nonlinearity can be separated from the beat signal. Then, the deskew filter is used to eliminate the nonlinearity. In the proposed algorithm, no approximations are used, so the estimation is theoretically unbiased. Certain simulations are performed to verify the versatility and effectiveness of the proposed algorithm. The nonlinearities are accurately estimated and eliminated by the method, which improves the spatial resolution of the OFDR system.
A machine learning equalization technique based on KNN for 56Gpbs PAM4-GPONis proposed by nonlinear classification characteristics of KNN. Simulation results show that the proposed method can effectively optimizes the performance of equalization and increase bandwidth of the GPON network.
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