publications

preprints

  1. Real-Time Implementation of Machine-Learning DSP
    Erik Börjeson, Christian Häger, and Per Larsson-Edefors
    Proc. Optical Fiber Communication Conf. (OFC), Invited Paper [to Be Presented], San Diego, CA, 2024
  2. Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
    Mohammad Farsi, Christian Häger, Magnus Karlsson, and Erik Agrell
    Proc. Optical Fiber Communication Conf. (OFC) [to Be Presented], San Diego, CA, 2024
  3. Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
    José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, and Henk Wymeersch
    Proc. Int. Conf. Mach. Learning for Communication and Networking (ICMLCN) [to Be Presented], Stockholm, Sweden, 2024
    [arXiv]
  4. Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication
    José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, and Henk Wymeersch
    submitted to JSTSP, 2023
    [arXiv]
  5. Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
    Jinxiang Song, Vincent Lauinger, Yibo Wu, Christian Häger, Jochen Schröder, Alexandre Graell I Amat, Laurent Schmalen, and Henk Wymeersch
    submitted to TCOM, 2023
    [arXiv]

journals

  1. End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
    Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski, Krzysztof Szczerba, Holger K. Iversen, Mikkel N. Schmidt, Darko Zibar, Jochen Schröder, Anders Larsson, Christian Häger, and Henk Wymeersch
    J. Lightw. Technol., vol. 41, no. 11, pp. 3261–3277, 2023
    [PDF] [arXiv] [DOI]
  2. Spatial Signal Design for Positioning via End-to-End Learning
    Steven Rivetti, José Miguel Mateos-Ramos, Yibo Wu, Jinxiang Song, Musa Furkan Keskin, Vijaya Yajnanarayana, Christian Häger, and Henk Wymeersch
    IEEE Wireless Commun. Lett., vol. 12, no. 3, pp. 525–529, 2023
    [PDF] [arXiv] [DOI]
  3. Data-Driven Estimation of Capacity Upper Bounds
    Christian Häger, and Erik Agrell
    IEEE Commun. Lett., vol. 26, no. 12, pp. 2939–2943, 2022
    [PDF] [arXiv] [Code] [DOI]
  4. Polarization Tracking in the Presence of PDL and Fast Temporal Drift
    Mohammad Farsi, Christian Häger, Magnus Karlsson, and Erik Agrell
    J. Lightw. Technol., vol. 40, no. 19, pp. 6408–6416, 2022
    [PDF] [arXiv] [DOI]
  5. Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM
    Zonglong He, Jinxiang Song, Kovendhan Vijayan, Christian Häger, Alexandre Graell i Amat, Henk Wymeersch, Peter A. Andrekson, Magnus Karlsson, and Jochen Schröder
    J. Lightw. Technol., vol. 40, no. 18, pp. 6168–6178, 2022
    [PDF] [DOI]
  6. Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
    Jinxiang Song, Christian Häger, Jochen Schröder, Timothy J. O’Shea, Erik Agrell, and Henk Wymeersch
    IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 7287–7298, 2022
    [PDF] [arXiv] [Code] [DOI]
  7. Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
    Jinxiang Song, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, and Henk Wymeersch
    IEEE J. Sel. Topics. Quantum Electron., vol. 28, no. 4, 2022
    [PDF] [arXiv] [Code] [DOI]
  8. Autoencoder-Based Unequal Error Protection Codes
    Vukan Ninkovic, Dejan Vukobratovic, Christian Häger, Henk Wymeersch, and Alexandre Graell i Amat
    IEEE Commun. Lett., vol. 25, no. 11, pp. 3575–3579, 2021
    [PDF] [arXiv] [DOI]
  9. Pruning and Quantizing Neural Belief Propagation Decoders
    Andreas Buchberger, Christian Häger, Henry D. Pfister, Laurent Schmalen, and Alexandre Graell i Amat
    IEEE J. Sel. Areas Commun., vol. 39, no. 7, pp. 1957–1966, 2021
    [PDF] [arXiv] [DOI]
  10. Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
    Rick M. Bütler, Christian Häger, Henry D. Pfister, Gabriele Liga, and Alex Alvarado
    J. Lightw. Technol., vol. 39, no. 4, pp. 949–959, 2021
    [PDF] [arXiv] [DOI]
  11. Physics-Based Deep Learning for Fiber-Optic Communication Systems
    Christian Häger, and Henry D. Pfister
    IEEE J. Sel. Areas Commun., vol. 39, no. 1, pp. 280–294, 2021
    [PDF] [arXiv] [Code] [DOI]
  12. Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
    Vinicius Oliari, Sebastiaan Goossens, Christian Häger, Gabriele Liga, Rick M. Bütler, Menno van den Hout, Sjoerd van der Heide, Henry D. Pfister, Chigo Okonkwo, and Alex Alvarado
    J. Lightw. Technol., vol. 38, no. 12, pp. 3114–3124, 2020
    [PDF] [arXiv] [DOI]
  13. Learning Physical-Layer Communication with Quantized Feedback
    Jinxiang Song, Bile Peng, Christian Häger, Henk Wymeersch, and Anant Sahai
    IEEE Trans. Commun., vol. 68, no. 1, pp. 645–653, 2020
    [PDF] [arXiv] [Code] [Video] [DOI]
  14. Approaching Miscorrection-free Performance of Product Codes with Anchor Decoding
    Christian Häger, and Henry D. Pfister
    IEEE Trans. Commun., vol. 66, no. 7, pp. 2797–2808, 2018
    [PDF] [arXiv] [DOI]
  15. Density Evolution for Deterministic Generalized Product Codes on the Binary Erasure Channel at High Rates
    Christian Häger, Henry D. Pfister, Alexandre Graell i Amat, and Fredrik Brännström
    IEEE Trans. Inf. Theory, vol. 63, no. 7, pp. 4357–4378, 2017
    [PDF] [arXiv] [DOI]
  16. On the Information Loss of the Max-Log Approximation in BICM Systems
    Mikhail Ivanov, Christian Häger, Fredrik Brännström, Alexandre Graell i Amat, Alex Alvarado, and Erik Agrell
    IEEE Trans. Inf. Theory, vol. 62, no. 6, pp. 3011–3025, 2016
    [PDF] [arXiv] [DOI]
  17. Terminated and Tailbiting Spatially Coupled Codes with Optimized Bit Mappings for Spectrally Efficient Fiber-Optical Systems
    Christian Häger, Alexandre Graell i Amat, Fredrik Brännström, Alex Alvarado, and Erik Agrell
    J. Lightw. Technol., vol. 33, no. 7, pp. 1275–1285, 2015
    [PDF] [arXiv] [DOI]
  18. Improving Soft FEC Performance for Higher-Order Modulations via Optimized Bit Channel Mappings
    Christian Häger, Alexandre Graell i Amat, Fredrik Brännström, Alex Alvarado, and Erik Agrell
    Opt. Express, vol. 22, no. 12, pp. 14544–14558, 2014
    [PDF] [arXiv] [DOI]
  19. A Low-Complexity Detector for Memoryless Polarization-Multiplexed Fiber-Optical Channels
    Christian Häger, Lotfollah Beygi, Erik Agrell, Pontus Johannisson, Magnus Karlsson, and Alexandre Graell i Amat
    IEEE Commun. Lett., vol. 18, no. 2, pp. 368–371, 2014
    [PDF] [arXiv] [DOI]
  20. Design of APSK Constellations for Coherent Optical Channels with Nonlinear Phase Noise
    Christian Häger, Alexandre Graell i Amat, Alvarado Alvarado, and Erik Agrell
    IEEE Trans. Commun., vol. 61, no. 8, pp. 3362–3373, 2013
    [PDF] [arXiv] [DOI]

conference

  1. Physics-Informed Neural Networks for Studying Charge Dynamics in Air
    O Hjortstam, Á Konrádsson, Y V Serdyuk, and C Häger
    Proc. IEEE Conf. Electrical Insulation and Dielectric Phenomena (CEIDP), East Rutherford, NJ, 2023
    [PDF]
  2. Model-Driven End-to-End Learning for Integrated Sensing and Communication
    José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin, Luc Le Magoarou, and Henk Wymeersch
    Proc. IEEE Int. Conf. Communications (ICC), Rome, Italy, 2023
    [PDF] [arXiv]
  3. FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
    Keren Liu, Erik Börjeson, Christian Häger, and Per Larsson-Edefors
    Proc. Optical Fiber Communication Conf. (OFC), Los Angeles, CA, 2023
    [arXiv] [DOI]
  4. Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
    Vukan Ninkovic, Dejan Vukobratovic, Christian Häger, Henk Wymeersch, and Alexandre Graell I Amat
    Proc. IEEE Int. Conf. Communications (ICC), Rome, Italy, 2023
    [arXiv] [DOI]
  5. Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
    Jinxiang Song, Vincent Lauinger, Christian Häger, Jochen Schröder, Alexandre Graell I Amat, Laurent Schmalen, and Henk Wymeersch
    Proc. European Conf. Optical Communication (ECOC), Glasgow, UK, 2023
  6. Improved Polarization Tracking in the Presence of PDL
    Mohammad Farsi, Christian Häger, Magnus Karlsson, and Erik Agrell
    Proc. European Conf. Optical Communication (ECOC), Basel, Switzerland, 2022
    [PDF] [arXiv]
  7. Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
    Zonglong He, Jinxiang Song, Christian Häger, Alexandre Graell i Amat, Henk Wymeersch, Peter A. Andrekson, Magnus Karlsson, and Jochen Schröder
    Proc. Optical Fiber Communication Conf. (OFC), Washington, D.C., 2022
    [PDF] [DOI]
  8. End-to-End Learning for Integrated Sensing and Communication
    José Miguel Mateos-Ramos, Jinxiang Song, Yibo Wu, Christian Häger, Musa Furkan Keskin, Vijaya Yajnanarayana, and Henk Wymeersch
    Proc. IEEE Int. Conf. Communications (ICC), Seoul, South Korea, 2022
    [PDF] [arXiv] [DOI]
  9. FPGA-based Optical Kerr Effect Emulator
    Keren Liu, Erik Börjeson, Christian Häger, and Per Larsson-Edefors
    Proc. Advanced Photonics Congress (APC), Masstricht, the Netherlands, 2022
    [PDF] [DOI]
  10. Learning Optimal PAM Levels for VCSEL-based Optical Interconnects
    Muralikrishnan Srinivasan, Jinxiang Song, Christian Häger, Krzysztof Szczerba, Henk Wymeersch, and Jochen Schröder
    Proc. European Conf. Optical Communication (ECOC), Basel, Switzerland, 2022
    [PDF]
  11. Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
    Yibo Wu, Jinxiang Song, Christian Häger, Ulf Gustavsson, Alexandre Graell i Amat, and Henk Wymeersch
    Proc. IEEE Int. Conf. Communications (ICC), Seoul, South Korea, 2022
    [PDF] [arXiv]
  12. Learned Decimation for Neural Belief Propagation Decoders
    Andreas Buchberger, Christian Häger, Henry D. Pfister, Laurent Schmalen, and Alexandre Graell i Amat
    Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, 2021
    [PDF] [arXiv] [DOI]
  13. Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
    Zonglong He, Jinxiang Song, Christian Häger, Kovendhan Vijayan, Peter Andrekson, Magnus Karlsson, Alexandre Graell i Amat, Henk Wymeersch, and Jochen Schröder
    Proc. European Conf. Optical Communication (ECOC), Bordeaux, France, 2021
    [PDF] [DOI]
  14. Over-the-Fiber Digital Predistortion Using Reinforcement Learning
    Jinxiang Song, Zonglong He, Christian Häger, Magnus Karlsson, Alexandre Graell i Amat, Henk Wymeersch, and Jochen Schröder
    Proc. European Conf. Optical Communication (ECOC), Bordeaux, France, 2021
    [PDF] [arXiv] [DOI]
  15. End-to-End Autoencoder for Superchannel Transceivers with Hardware Impairment
    Jinxiang Song, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, and Henk Wymeersch
    Proc. Optical Fiber Communication Conf. (OFC), Virtual Conference, 2021
    [PDF] [arXiv]
  16. Pruning Neural Belief Propagation Decoders
    Andreas Buchberger, Christian Häger, Henry D. Pfister, Laurent Schmalen, and Alexandre Graell Amat
    Proc. IEEE Int. Symp. Information Theory (ISIT), Los Angeles, CA, 2020
    [PDF] [arXiv] [DOI]
  17. End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
    Kadir Gümüs, Alex Alvarado, Bin Chen, Christian Häger, and Erik Agrell
    Proc. Optical Fiber Communication Conf. (OFC), San Diego, CA, 2020
    [PDF] [arXiv] [DOI]
  18. Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
    Christian Häger, Henry D Pfister, Rick M. Bütler, Gabriele Liga, and Alex Alvarado
    Proc. Optical Fiber Communication Conf. (OFC), San Diego, CA, 2020
    [PDF] [arXiv] [Slides] [DOI]
  19. Decoding Reed-Muller Codes Using Redundant Code Constraints
    Mengke Lian, Christian Häger, and Henry D. Pfister
    Proc. IEEE Int. Symp. Information Theory (ISIT), Los Angeles, CA, 2020
    [PDF] [DOI]
  20. Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
    Jinxiang Song, Christian Häger, Jochen Schröder, Tim O’Shea, and Henk Wymeersch
    Proc. IEEE Glob. Communication Conf. (GLOBECOM), Taipei, Taiwan, 2020
    [PDF] [arXiv] [Code] [DOI]
  21. Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
    Fabrizio Carpi, Christian Häger, Marco Martalò, Riccardo Raheli, and Henry D. Pfister
    Proc. Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, 2019
    [PDF] [arXiv] [Code] [DOI]
  22. Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
    Christian Häger, Henry D. Pfister, Rick M. Bütler, Gabriele Liga, and Alex Alvarado
    Proc. European Conf. Optical Communication (ECOC), Dublin, Ireland, 2019
    [PDF] [arXiv] [Slides] [DOI]
  23. Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
    Mengke Lian, Fabrizio Carpi, Christian Häger, and Henry D. Pfister
    Proc. IEEE Int. Symp. Information Theory (ISIT), Paris, France, 2019
    [PDF] [arXiv] [DOI]
  24. ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters
    Christoffer Fougstedt, Christian Häger, Lars Svensson, Henry D. Pfister, and Per Larsson-Edefors
    Proc. European Conf. Optical Communication (ECOC), Rome, Italy, 2018
    [PDF] [arXiv] [DOI]
  25. Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
    Christian Häger, and Henry D. Pfister
    Proc. European Conf. Optical Communication (ECOC), Rome, Italy, 2018
    [PDF] [arXiv] [Slides] [DOI]
  26. Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
    Christian Häger, and Henry D. Pfister
    Proc. IEEE Int. Symp. Information Theory (ISIT), Vail, CO, 2018
    [PDF] [arXiv] [Slides] [Code] [DOI]
  27. Nonlinear Interference Mitigation via Deep Neural Networks
    Christian Häger, and Henry D. Pfister
    Proc. Optical Fiber Communication Conf. (OFC), San Diego, CA, 2018
    [PDF] [arXiv] [Slides] [Code] [DOI]
  28. What Can Machine Learning Teach Us about Communications?
    Mengke Lian, Christian Häger, and Henry D. Pfister
    Proc. IEEE Information Theory Workshop (ITW), Guangzhou, China, 2018
    [PDF] [arXiv] [DOI]
  29. Decoding Reed-Muller Codes Using Minimum-Weight Parity Checks
    Elia Santi, Christian Häger, and Henry D. Pfister
    Proc. IEEE Int. Symp. Information Theory (ISIT), Vail, CO, 2018
    [PDF] [arXiv] [DOI]
  30. On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems
    Alireza Sheikh, Alexandre Graell i Amat, Gianluigi Liva, Christian Häger, and Henry D. Pfister
    Proc. Int. Symp. Turbo Codes and Iterative Information Processing (ISTC), Hong Kong, 2018
    [PDF] [arXiv] [DOI]
  31. Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
    Shen Li, Christian Häger, Nil Garcia, and Henk Wymeersch
    Proc. European Conf. Optical Communication (ECOC), Rome, Italy, 2018
    [PDF] [arXiv] [Code] [DOI]
  32. Miscorrection-Free Decoding of Staircase Codes
    Christian Häger, and Henry D. Pfister
    Proc. European Conf. Optical Communication (ECOC), Gothenburg, Sweden, 2017
    [PDF] [arXiv] [Poster] [DOI]
  33. Deterministic and Ensemble-Based Spatially-Coupled Product Codes
    Christian Häger, Henry D. Pfister, Alexandre Graell i Amat, and Fredrik Brännström
    Proc. IEEE Int. Symp. Information Theory (ISIT), Barcelona, Spain, 2016
    [PDF] [arXiv] [Slides] [Code] [DOI]
  34. Density Evolution for Deterministic Generalized Product Codes with Higher-Order Modulation
    Christian Häger, Alexandre Graell i Amat, Henry D. Pfister, and Fredrik Brännström
    Proc. Int. Symp. Turbo Codes and Iterative Information Processing (ISTC), Brest, France, 2016
    [PDF] [arXiv] [Slides] [DOI]
  35. A Deterministic Construction and Density Evolution Analysis for Generalized Product Codes
    Christian Häger, Henry D. Pfister, Alexandre Graell i Amat, Fredrik Brännström, and Erik Agrell
    Proc. Int. Zurich Seminar on Communications (IZS), Zürich, Switzerland, 2016
    [PDF] [Slides] [Code] [DOI]
  36. Density Evolution and Error Floor Analysis of Staircase and Braided Codes
    Christian Häger, Henry D. Pfister, Alexandre Graell i Amat, and Fredrik Brännström
    Proc. Optical Fiber Communication Conf. (OFC), Anaheim, CA, 2016
    [PDF] [Poster] [DOI]
  37. Spatially-Coupled Codes for Optical Communications: State-of-the-Art and Open Problems
    Alexandre Graell i Amat, Christian Häger, Fredrik Brännström, and Erik Agrell
    Proc. Optoelectronics and Communications Conf. (OECC), Shanghai, China, 2015
    [PDF] [Slides] [DOI]
  38. On Parameter Optimization for Staircase Codes
    Christian Häger, Alexandre Graell i Amat, Henry D. Pfister, Alex Alvarado, Fredrik Brännström, and Erik Agrell
    Proc. Optical Fiber Communication Conf. (OFC), Los Angeles, CA, 2015
    [PDF] [Slides] [DOI]
  39. Comparison of Terminated and Tailbiting Spatially Coupled LDPC Codes With Optimized Bit Mapping for PM-64-QAM
    Christian Häger, Alexandre Graell i Amat, Fredrik Brännström, Alex Alvarado, and Erik Agrell
    Proc. European Conf. Optical Communication (ECOC), Cannes, France, 2014
    [PDF] [Slides] [DOI]
  40. Optimized Bit Mappings for Spatially Coupled LDPC Codes over Parallel Binary Erasure Channels
    Christian Häger, Alexandre Graell i Amat, Alex Alvarado, Fredrik Brännström, and Erik Agrell
    Proc. IEEE Int. Conf. Communications (ICC), Sydney, Australia, 2014
    [PDF] [arXiv] [Poster] [DOI]
  41. Constellation Optimization for Coherent Optical Channels Distorted by Nonlinear Phase Noise
    Christian Häger, Alexandre Graell i Amat, Alex Alvarado, and Erik Agrell
    Proc. IEEE Glob. Communication Conf. (GLOBECOM), Anaheim, CA, 2012
    [PDF] [Slides] [DOI]

Patents

  1. Systems and methods for decoding forward error correction codes based on component codes
    Henry D. Pfister and Christian Häger (US 10,693,500 B2), filed: Sep. 17, 2018
    [PDF]

Thesis

  1. Analysis and Design of Spatially-Coupled Codes with Application to Fiber-Optical Communications
    PhD thesis, Chalmers University of Technology, May 2016
    [PDF] [Slides] [CPL]
  2. On Signal Constellations and Coding for Long-Haul Fiber-Optical Systems
    Licentiate thesis, Chalmers University of Technology, April 2014
    [PDF] [Slides] [CPL]
  3. Bidirectional Wireless Communication via Relay Nodes using Lattices
    Diploma thesis, Ulm University, July 2011
    [PDF]
  4. Turbo Equalization Performance: The Effect of Precoding and Application of Turbo Codes
    Study thesis, Ulm University, 2009
    [PDF]


Disclaimer: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.