55 citations · 168 across the 5 of their papers we have counts for
5 papers
Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation
Xiang Lin, Shenghang Luo, Sunish Kumar Orappanpara Soman +4
Derived from the regular perturbation treatment of the nonlinear Schrodinger equation, a machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is p…
Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links
Abdelkerim Amari, Xiang Lin, Octavia A. Dobre +2
Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effe…
A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation
Abdelkerim Amari, Xiang Lin, Octavia A. Dobre +2
We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and…
Robust Faster-than-Nyquist PDM-mQAM Systems with Tomlinson-Harashima Precoding
Deyuan Chang, Oluyemi Omomukuyo, Xiang Lin +3
A training-based channel estimation algorithm is proposed for the faster-than-Nyquist PDM-mQAM (m = 4, 16, 64) systems with Tomlinson-Harashima precoding (THP). This is robust to t…
Modulation Classification Using Received Signal's Amplitude Distribution for Coherent Receivers
Xiang Lin, Yahia A. Eldemerdash, Octavia A. Dobre +2
In this letter, we propose a modulation classification algorithm which is based on the received signal's amplitude for coherent optical receivers. The proposed algorithm classifies…