Search Results for author: Laurent Schmalen

Found 35 papers, 8 papers with code

Loss Design for Single-carrier Joint Communication and Neural Network-based Sensing

no code implementations5 Mar 2024 Charlotte Muth, Benedikt Geiger, Daniel Gil Gaviria, Laurent Schmalen

To enhance the training behavior, we decouple the loss functions from the respective SNR values and the number of sensing snapshots, using bounds of the sensing performance.

object-detection Object Detection

Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communications

no code implementations23 Feb 2024 Jonas Ney, Patrick Matalla, Vincent Lauinger, Laurent Schmalen, Sebastian Randel, Norbert Wehn

In this work, we present a high-throughput field programmable gate array (FPGA) demonstrator of an artificial neural network (ANN)-based equalizer.

Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs

no code implementations23 Jan 2024 Luca Schmid, Tomer Raviv, Nir Shlezinger, Laurent Schmalen

We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels.

Fully-blind Neural Network Based Equalization for Severe Nonlinear Distortions in 112 Gbit/s Passive Optical Networks

no code implementations17 Jan 2024 Vincent Lauinger, Patrick Matalla, Jonas Ney, Norbert Wehn, Sebastian Randel, Laurent Schmalen

We demonstrate and evaluate a fully-blind digital signal processing (DSP) chain for 100G passive optical networks (PONs), and analyze different equalizer topologies based on neural networks with low hardware complexity.

Fractional Chirp-Slope-Shift-Keying for SDR-based Search and Rescue Applications

no code implementations8 Dec 2023 Daniel Gil Gaviria, Marcus Müller, Felix Artmann, Laurent Schmalen

The use of modern software-defined radio (SDR) devices enables the implementation of efficient communication systems in numerous scenarios.

Local Message Passing on Frustrated Systems

no code implementations2 Jun 2023 Luca Schmid, Joshua Brenk, Laurent Schmalen

Message passing on factor graphs is a powerful framework for probabilistic inference, which finds important applications in various scientific domains.

Single Particle Analysis

Spiking Neural Network Decision Feedback Equalization for IM/DD Systems

1 code implementation27 Apr 2023 Alexander von Bank, Eike-Manuel Edelmann, Laurent Schmalen

A spiking neural network (SNN) equalizer with a decision feedback structure is applied to an IM/DD link with various parameters.

Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation

no code implementations14 Apr 2023 Jonas Ney, Vincent Lauinger, Laurent Schmalen, Norbert Wehn

In recent years, communication engineers put strong emphasis on artificial neural network (ANN)-based algorithms with the aim of increasing the flexibility and autonomy of the system and its components.

Blind Channel Equalization Using Vector-Quantized Variational Autoencoders

no code implementations22 Feb 2023 Jinxiang Song, Vincent Lauinger, Yibo Wu, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, Laurent Schmalen, Henk Wymeersch

Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.

Autoencoder-based Joint Communication and Sensing of Multiple Targets

1 code implementation23 Jan 2023 Charlotte Muth, Laurent Schmalen

Foremost, we develop a suitable encoding scheme for the training of the AE and for targeting a fixed false alarm rate of the target detection during training.

Improving the Bootstrap of Blind Equalizers with Variational Autoencoders

no code implementations16 Jan 2023 Vincent Lauinger, Fred Buchali, Laurent Schmalen

We evaluate the start-up of blind equalizers at critical working points, analyze the advantages and obstacles of commonly-used algorithms, and demonstrate how the recently-proposed variational autoencoder (VAE) based equalizers can improve bootstrapping.

End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search

1 code implementation7 Dec 2022 Andrej Rode, Benedikt Geiger, Shrinivas Chimmalgi, Laurent Schmalen

As the demand for higher data throughput in coherent optical communication systems increases, we need to find ways to increase capacity in existing and future optical communication links.

Geometric Constellation Shaping with Low-complexity Demappers for Wiener Phase-noise Channels

no code implementations5 Dec 2022 Andrej Rode, Laurent Schmalen

We show that separating the in-phase and quadrature component in optimized, machine-learning based demappers of optical communications systems with geometric constellation shaping reduces the required computational complexity whilst retaining their good performance.

Spiking Neural Network Decision Feedback Equalization

no code implementations9 Nov 2022 Eike-Manuel Bansbach, Alexander von Bank, Laurent Schmalen

In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods.

Blind and Channel-agnostic Equalization Using Adversarial Networks

no code implementations15 Sep 2022 Vincent Lauinger, Manuel Hoffmann, Jonas Ney, Norbert Wehn, Laurent Schmalen

The proposed approach is independent of the equalizer topology and enables the application of powerful neural network based equalizers.

Autonomous Driving

Deep Reinforcement Learning for Uplink Multi-Carrier Non-Orthogonal Multiple Access Resource Allocation Using Buffer State Information

no code implementations31 Aug 2022 Eike-Manuel Bansbach, Yigit Kiyak, Laurent Schmalen

For orthogonal multiple access (OMA) systems, the number of served user equipments (UEs) is limited to the number of available orthogonal resources.

Scheduling

Optimization of Geometric Constellation Shaping for Wiener Phase Noise Channels with Varying Channel Parameters

no code implementations24 Jun 2022 Andrej Rode, Laurent Schmalen

We present a novel method to investigate the effects of varying channel parameters on geometrically shaped constellations for communication systems employing the blind phase search algorithm.

Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders

1 code implementation25 Apr 2022 Vincent Lauinger, Fred Buchali, Laurent Schmalen

We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications.

Variational Inference

Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs

1 code implementation30 Mar 2022 Luca Schmid, Laurent Schmalen

In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement.

Single Particle Analysis

Neural Enhancement of Factor Graph-based Symbol Detection

no code implementations7 Mar 2022 Luca Schmid, Laurent Schmalen

We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels.

Geometric Constellation Shaping for Phase-noise Channels Using a Differentiable Blind Phase Search

no code implementations6 Dec 2021 Andrej Rode, Benedikt Geiger, Laurent Schmalen

We perform geometric constellation shaping with optimized bit labeling using a binary autoencoder including a differential blind phase search (BPS).

Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information

no code implementations27 Aug 2021 Eike-Manuel Bansbach, Victor Eliachevitch, Laurent Schmalen

In particular, varying requirements lead to a non-convex optimization problem when maximizing the systems data rate while preserving fairness between UEs.

Fairness Feature Compression +3

Optical Fiber Communication Systems Based on End-to-End Deep Learning

no code implementations18 May 2020 Boris Karanov, Mathieu Chagnon, Vahid Aref, Domanic Lavery, Polina Bayvel, Laurent Schmalen

We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning.

Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

no code implementations18 May 2020 Boris Karanov, Mathieu Chagnon, Vahid Aref, Filipe Ferreira, Domanic Lavery, Polina Bayvel, Laurent Schmalen

The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization.

Pruning Neural Belief Propagation Decoders

no code implementations21 Jan 2020 Andreas Buchberger, Christian Häger, Henry D. Pfister, Laurent Schmalen, Alexandre Graell i Amat

In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning.

Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model

no code implementations11 Dec 2019 Boris Karanov, Mathieu Chagnon, Vahid Aref, Domaniç Lavery, Polina Bayvel, Laurent Schmalen

We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel.

Generative Adversarial Network

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

no code implementations2 Oct 2019 Boris Karanov, Gabriele Liga, Vahid Aref, Domaniç Lavery, Polina Bayvel, Laurent Schmalen

In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/DD) links.

Information Theory Signal Processing Information Theory

Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design

1 code implementation7 Mar 2019 Ahmed Elkelesh, Moustafa Ebada, Sebastian Cammerer, Laurent Schmalen, Stephan ten Brink

Moreover, GenAlg can be used to design LDPC codes with the aim of reducing decoding latency and complexity, leading to coding gains of up to $0. 325$ dB and $0. 8$ dB at BLER of $10^{-5}$ for both AWGN and Rayleigh fading channels, respectively, when compared to state-of-the-art short LDPC codes.

Information Theory Information Theory

End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks

no code implementations24 Jan 2019 Boris Karanov, Domaniç Lavery, Polina Bayvel, Laurent Schmalen

Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel.

End-to-end Deep Learning of Optical Fiber Communications

no code implementations11 Apr 2018 Boris Karanov, Mathieu Chagnon, Félix Thouin, Tobias A. Eriksson, Henning Bülow, Domaniç Lavery, Polina Bayvel, Laurent Schmalen

In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver.

A Compressed Sensing Approach for Distribution Matching

no code implementations2 Apr 2018 Mohamad Dia, Vahid Aref, Laurent Schmalen

In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem.

Bayesian Inference Quantization

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