72 citations · 410 across the 39 of their papers we have counts for
26 papers · 1 filter
End-to-End Modelling of Earthquake-Induced Polarisation Perturbations in Submarine Optical Fibres
Rick M. Butler, José Núñez-Kasaneva, Gabriele Liga +3
We present an end-to-end model of earthquake-perturbed signal propagation in submarine fibres. Ground displacements are converted to fibre strain via ocean pressure, and injected i…
Accurate Multi-perturbation Localization in Optical Fibers with Polarization-based Forward Sensing
Lampros Lanaras, Rick M. Butler, Christian Häger +1
We present a method to accurately localize multiple perturbations from fiber outputs. We cross-correlate received polarizations between two wavelengths, apply peak detection, and i…
PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation
Zicong Jiang, Magnus Karlsson, Erik Agrell +1
We propose physics-informed digital twin (PIDT): a fiber parameter estimation approach that combines a parameterized split-step method with a physics-informed loss. PIDT improves a…
Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features
José Miguel Mateos-Ramos, Frederik Zumegen, Henk Wymeersch +2
Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned wit…
Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul
Alireza Bordbar, Lise Aabel, Christian Häger +2
We consider the problem of pilot-aided, uplink channel estimation in a distributed massive multiple-input multiple-output (MIMO) architecture, in which the access points are connec…
Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Mohammad Farsi, Christian Häger, Magnus Karlsson +1
We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resi…
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Jinxiang Song, Vincent Lauinger, Christian Häger +4
We propose a novel frequency-domain blind equalization scheme for coherent optical communications. The method is shown to achieve similar performance to its recently proposed time-…
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
José Miguel Mateos-Ramos, Baptiste Chatelier, Christian Häger +3
Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficien…
Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication under Hardware Impairments
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin +2
We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. Hardware impairments are usually addressed by mea…
Model-Driven End-to-End Learning for Integrated Sensing and Communication
José Miguel Mateos-Ramos, Christian Häger, Musa Furkan Keskin +2
Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such i…
FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
Keren Liu, Erik Börjeson, Christian Häger +1
We design and implement an adaptive machine learning equalizer that alternates multiple linear and nonlinear computational layers on an FPGA. On-chip training via gradient backprop…
Spatial Signal Design for Positioning via End-to-End Learning
Steven Rivetti, Josè Miguel Mateos-Ramos, Yibo Wu +5
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a mult…
Polarization Tracking in the Presence of PDL and Fast Temporal Drift
Mohammad Farsi, Christian Häger, Magnus Karlsson +1
In this paper, we analyze the effectiveness of polarization tracking algorithms in optical transmission systems suffering from fast state of polarization (SOP) rotations and polari…
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Jinxiang Song, Christian Häger, Jochen Schröder +2
We propose an AE-based transceiver for a WDM system impaired by hardware imperfections. We design our AE following the architecture of conventional communication systems. This enab…
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
Yibo Wu, Jinxiang Song, Christian Häger +3
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between…
End-to-End Learning for Integrated Sensing and Communication
José Miguel Mateos-Ramos, Jinxiang Song, Yibo Wu +4
Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint s…
Over-the-fiber Digital Predistortion Using Reinforcement Learning
Jinxiang Song, Zonglong He, Christian Häger +4
We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning. Optical back-to-back training of a novel…
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment
Jinxiang Song, Christian Häger, Jochen Schröder +2
We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component. Our system achieves up to 60% SER reduction and up to 50% guard…
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Christian Häger, Henry D. Pfister
We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main observa…
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Rick M. Bütler, Christian Häger, Henry D. Pfister +2
In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The re…
Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
Vinícius Oliari, Sebastiaan Goossens, Christian Häger +7
Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch''. One guiding principle for previous work on…
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Jinxiang Song, Christian Häger, Jochen Schröder +2
End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO t…
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Christian Häger, Henry D. Pfister, Rick M. Bütler +2
We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs…
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
Kadir Gümüs, Alex Alvarado, Bin Chen +2
GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates…