Math @ Duke

Publications [#337694] of Henry Pfister
Papers Published
 Hager, C; Pfister, HD, Deep Learning of the Nonlinear Schrödinger Equation in FiberOptic Communications,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 15901594, IEEE [doi]
(last updated on 2019/02/16)
Abstract: © 2018 IEEE. An important problem in fiberoptic communications is to invert the nonlinear Schrödinger equation in real time to reverse the deterministic effects of the channel. Interestingly, the popular splitstep Fourier method (SSFM) leads to a computation graph that is reminiscent of a deep neural network. This observation allows one to leverage tools from machine learning to reduce complexity. In particular, the main disadvantage of the SSFM is that its complexity using M steps is at least M times larger than a linear equalizer. This is because the linear SSFM operator is a dense matrix. In previous work, truncation methods such as frequency sampling, wavelets, or leastsquares have been used to obtain 'cheaper' operators that can be implemented using filters. However, a large number of filter taps are typically required to limit truncation errors. For example, Ip and Kahn showed that for a 10 Gbaud signal and 2000 km optical link, a truncated SSFM with 25 steps would require 70tap filters in each step and 100 times more operations than linear equalization. We find that, by jointly optimizing all filters with deep learning, the complexity can be reduced significantly for similar accuracy. Using optimized 5tap and 3tap filters in an alternating fashion, one requires only around 26 times the complexity of linear equalization, depending on the implementation.


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