English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

An important problem in fiber-optic communications is to invert the nonlinearSchr\"odinger equation in real time to reverse the deterministic effects of thechannel. Interestingly, the popular split-step Fourier method (SSFM) leads to acomputation graph that is reminiscent of a deep neural network. Thisobservation allows one to leverage tools from machine learning to reducecomplexity. In particular, the main disadvantage of the SSFM is that itscomplexity 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 least-squares havebeen used to obtain "cheaper" operators that can be implemented using filters.However, a large number of filter taps are typically required to limittruncation errors. For example, Ip and Kahn showed that for a 10 Gbaud signaland 2000 km optical link, a truncated SSFM with 25 steps would require 70-tapfilters in each step and 100 times more operations than linear equalization. Wefind that, by jointly optimizing all filters with deep learning, the complexitycan be reduced significantly for similar accuracy. Using optimized 5-tap and3-tap filters in an alternating fashion, one requires only around 2-6 times thecomplexity of linear equalization, depending on the implementation.

Deep Learning of the Nonlinear Schr\"odinger Equation in Fiber-Optic Communications