[2307.05374] Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems
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Electrical Engineering and Systems Science > Signal Processing

arXiv:2307.05374 (eess)
[Submitted on 4 Jul 2023 (v1), last revised 3 Nov 2023 (this version, v3)]

Title:Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

Authors:Sasipim Srivallapanondh, Pedro J. Freire, Ashraful Alam, Nelson Costa, Bernhard Spinnler, Antonio Napoli, Egor Sedov, Sergei K. Turitsyn, Jaroslaw E. Prilepsky
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Abstract:For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.
Comments: 4 pages, European Conference on Optical Communication (ECOC)
Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG)
Cite as: arXiv:2307.05374 [eess.SP]
  (or arXiv:2307.05374v3 [eess.SP] for this version)
  https://doi.org/10.48550/arXiv.2307.05374

Submission history

From: Sasipim Srivallapanondh [view email]
[v1] Tue, 4 Jul 2023 08:56:09 UTC (721 KB)
[v2] Tue, 26 Sep 2023 09:41:34 UTC (721 KB)
[v3] Fri, 3 Nov 2023 15:38:47 UTC (721 KB)
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