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20162024
most citedEnd-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks

100 citations · 133 across the 18 of their papers we have counts for

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6 papers · 1 filter

eess.SP20232 cited

Bistatic OFDM-based Joint Radar-Communication: Synchronization, Data Communication and Sensing

Lucas Giroto de Oliveira, David Brunner, Axel Diewald +4

This article introduces a bistatic joint radar-communication (RadCom) system based on orthogonal frequency-division multiplexing (OFDM). In this context, the adopted OFDM frame str…

eess.SP2023

Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation

Jonas Ney, Vincent Lauinger, Laurent Schmalen +1

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 syst…

eess.SP2022

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

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…

eess.SP2022

Blind and Channel-agnostic Equalization Using Adversarial Networks

Vincent Lauinger, Manuel Hoffmann, Jonas Ney +2

Due to the rapid development of autonomous driving, the Internet of Things and streaming services, modern communication systems have to cope with varying channel conditions and a s…

eess.SP20201 cited

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

Boris Karanov, Mathieu Chagnon, Vahid Aref +4

We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In…

eess.SP20208 cited

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

Boris Karanov, Mathieu Chagnon, Vahid Aref +3

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