72 citations · 410 across the 39 of their papers we have counts for
48 papers
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…
Decoding Quantum LDPC Codes Using Graph Neural Networks
Vukan Ninkovic, Ognjen Kundacina, Dejan Vukobratovic +2
In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs). Similar to the Belief Propagation (BP)-b…
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-…
Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation
Hanwen Yao, Waleed Abu Laban, Christian Häger +2
Quantum low-density parity-check (QLDPC) codes have emerged as a promising technique for quantum error correction. A variety of decoders have been proposed for QLDPC codes and many…
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…
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Vukan Ninkovic, Dejan Vukobratovic, Christian Häger +2
Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword). However, in many practical scenarios,…
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…
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski +8
Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-c…
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…
Data-Driven Estimation of Capacity Upper Bounds
Christian Häger, Erik Agrell
We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm…
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…
Autoencoder-Based Unequal Error Protection Codes
Vukan Ninkovic, Dejan Vukobratovic, Christian Häger +2
We present a novel autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities. The proposed design is based on a generalization of an a…
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…