240 citations · 579 across the 25 of their papers we have counts for
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Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation
Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe +1
Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theo…
Learning for Perturbation-Based Fiber Nonlinearity Compensation
Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe +2
Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed b…
Joint PMD Tracking and Nonlinearity Compensation with Deep Neural Networks
Prasham Jain, Lutz Lampe, Jeebak Mitra
Overcoming fiber nonlinearity is one of the core challenges limiting the capacity of optical fiber communication systems. Machine learning based solutions such as learned digital b…
Probabilistic Amplitude Shaping and Nonlinearity Tolerance: Analysis and Sequence Selection Method
Mohammad Taha Askari, Lutz Lampe, Jeebak Mitra
Probabilistic amplitude shaping (PAS) is a practical means to achieve a shaping gain in optical fiber communication. However, PAS and shaping in general also affect the signal-depe…