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Signal Processing Techniques for Optical Transmission Based on Eigenvalue Communication

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posted on 23.03.2021, 20:32 by Jonas KochJonas Koch, Ken Chan, Christian G. Schaeffer, Stephan Pachnicke
A minimum mean squared error (MMSE) equalizer is a way to effectively increase transmission performance for nonlinear Fourier transform (NFT) based communication systems. Other equalization schemes, based on nonlinear equalizer approaches or neural networks, are interesting for NFT transmission due to their ability to deal with nonlinear correlations of the NFTs’ eigenvalues and their coefficients. We experimentally investigated single- and dual-polarization long haul transmission with several modulation schemes and compared different equalization techniques including joint detection equalization and the use of neural networks. We observed that joint detection equalization provides range increases for shorter transmission distances while having low numeric complexity. We could further achieve bit error rates (BER) under HD-FEC for significant longer transmission distances in comparison to no equalization with different equalizers.

Manuscript received August 6, 2020; revised November 2, 2020; accepted December 8, 2020. Date of publication December 16, 2020;

Funding

SPP 2111:  Electronic-Photonic Integrated Systems for Ultrafast Signal Processing

Deutsche Forschungsgemeinschaft

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Email Address of Submitting Author

jonas.koch@tf.uni-kiel.de

ORCID of Submitting Author

0000-0002-3924-9474

Submitting Author's Institution

Kiel University, Chair of Communications

Submitting Author's Country

Germany