[OFC 2025] Recent advances on machine learning-aided DSP for short-reach and long-haul optical comm. скачать в хорошем качестве

[OFC 2025] Recent advances on machine learning-aided DSP for short-reach and long-haul optical comm.

Invited presentation at the Optical Fiber Communications Conference (OFC) 2025 in San Francisco, CA, USA. How to cite: L. Schmalen, V. Lauinger, J. Ney, N. Wehn, P. Matalla, S. Randel, A. von Bank and E. M. Edelmann, "Recent advances on machine learning-aided DSP for short-reach and long-haul optical communications," _Proc. Opt. Fiber Commun. Conf. (OFC)_, San Francisco, CA, USA, Mar. 2025, invited presentation, https://arxiv.org/abs/2411.10101 Download the associated paper at: https://arxiv.org/abs/2411.10101 Further links to presentations and videos referenced in the presentation: Part 1: Machine Learning-aided DSP for Coherent Long-haul Communications: 1) V. Lauinger, F. Buchali and L. Schmalen, "Blind equalization and channel estimation in coherent optical communications using variational autoencoders," IEEE J. Sel. Areas Commun. , vol. 40, no. 9, pp. 2529-2539, Sep. 2022, https://arxiv.org/abs/2204.11776 2) A. Rode, M. Farsi, V. Lauinger, M. Karlsson, E. Agrell, L. Schmalen and C. Häger, "Machine learning opportunities for integrated polarization sensing and communication in optical fibers," Optical Fiber Technology , vol. 90, no. 104047, Mar. 2025 3)    • [OFC 2023] Improving the Bootstrap of Blin...   (V. Lauinger, F. Buchali and L. Schmalen, "Improving the bootstrap of blind equalizers with variational autoencoders," Proc. Opt. Fiber Commun. Conf. (OFC) , San Diego, CA, USA, Mar. 2023, https://arxiv.org/abs/2301.06576) 4)    • [ITG Workshop KT 3.1 2022] Carrier Recover...   (Vincent Lauinger, "Carrier Recovery in Coherent Optical Communications Using Variational Autoencoders". Presented at the Workshop of the ITG Expert Group KT 3.1 "Modeling and Simulation of Photonic Components and Systems" ) 5)    • [SPPCom 2021] Blind Equalization for Coher...   (V. Lauinger, F. Buchali and L. Schmalen, "Blind equalization for coherent optical communications based on variational inference," Advanced Photonic Congress: Signal Processing in Photonic Communications (SPPCom) , pp. SpTh1D.6, Jul. 2021) Part 2: Machine Learning-aided DSP for Short Reach Communications 1) J. Song, V. Lauinger, Y. Wu, C. Häger, J. Schröder, A. Graell i Amat, L. Schmalen, and H. Wymeersch, "Blind channel equalization using vector-quantized variational autoencoders," preprint https://arxiv.org/abs/2302.11687 2) V. Lauinger, P. Matalla, J. Ney, N. Wehn, S. Randel and L. Schmalen, "Fully-blind neural network based equalization for severe nonlinear distortions in 112 Gbit/s passive optical networks," Proc. OFC , San Diego, CA, USA, Mar. 2024, https://arxiv.org/abs/2401.09579 3) J. Ney, C. Füllner, V. Lauinger, L. Schmalen, S. Randel and N. Wehn, "From algorithm to implementation: enabling high throughput CNN-based equalization on FPGA for optical communications," Proc. Samos Conf. , Jul. 2023 4) J. Ney, C. Füllner, V. Lauinger, L. Schmalen, S. Randel, N. Wehn, "CNN-based equalization for communications: Achieving gigabit throughput with a flexible FPGA hardware architecture,” submitted to Intl. J. Parallel Programming , 2024, https://arxiv.org/abs/2405.02323 5) J. Ney and N. Wehn, "Achieving high throughput with a trainable neural-network-based equalizer for communications on FPGA," in Proc. DSD , Aug. 2024, https://arxiv.org/abs/2407.02967 Part 3: Towards Neuromorphic Implementation: 1)    • [APC-SPPCOM 2023] Spiking neural network d...   (A. von Bank, E. Edelmann and L. Schmalen, "Spiking neural network decision feedback equalization for IM/DD systems," Proc. SPPCom , Jul. 2023, https://arxiv.org/abs/2304.14152) 2) A. von Bank, E. Edelmann and L. Schmalen, "Energy efficient spiking neural network equalization for IM/DD systems with optimized neural encoding," Proc. OFC , Mar. 2024, https://arxiv.org/abs/2312.12909 3)    • [SCC-WSA 2023] Spiking neural network deci...   (E. M. Bansbach, A. von Bank and L. Schmalen, "Spiking neural network decision feedback equalization," Proc. Int. ITG Workshop on Smart Antennas and Conf. on Systems, Communications, and Coding (WSA-SCC) , Braunschweig, Germany, Feb. 2023, https://arxiv.org/abs/2211.04756) 4) E. Arnold, E.-M. Edelmann, A. von Bank, E. Müller, L. Schmalen and J. Schemmel, "Short-reach optical communications: a real-world task for neuromorphic hardware," Proc. Neuro Inspired Computational Elements Conference (NICE) , Heidelberg, Germany, Mar. 2025, https://arxiv.org/abs/2412.03129 Contents of the video: 0:00 - Introduction, Machine Learning and Digital Signal Processing 06:08 - Machine Learning-aided DSP for Coherent Long-haul Communications 23:24 - Machine Learning-aided DSP for Short Reach Communications 33:40 - Towards Neuromorphic Implementation 40:03 - Conclusions and Outlook This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101001899). Parts of this work were carried out in the framework of the CELTIC-NEXT project AI-NET-ANTILLAS (C2019/3-3, grant 16KIS1316 and 16KIS1317), funded by the German Federal Ministry of Education and Research (BMBF).