46 citations · 112 across the 29 of their papers we have counts for
9 papers · 1 filter
DoA-Aided MMSE Channel Estimation for Wireless Communication Systems
Franz Weißer, Nurettin Turan, Wolfgang Utschick
This paper investigates the combination of parametric channel estimation with minimum mean square error (MMSE) estimation. We propose a direction-of-arrival (DoA)-aided two-stage c…
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
Michael Baur, Benedikt Böck, Nurettin Turan +1
This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and p…
Data-Aided Channel Estimation Utilizing Gaussian Mixture Models
Franz Weißer, Nurettin Turan, Dominik Semmler +1
In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind cha…
Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders
Michael Baur, Nurettin Turan, Benedikt Fesl +1
In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed c…
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models
Benedikt Fesl, Nurettin Turan, Benedikt Böck +1
This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative m…
Enhanced Low-Complexity FDD System Feedback with Variable Bit Lengths via Generative Modeling
Nurettin Turan, Benedikt Fesl, Wolfgang Utschick
Recently, a versatile limited feedback scheme based on a Gaussian mixture model (GMM) was proposed for frequency division duplex (FDD) systems. This scheme provides high flexibilit…
Low-Rank Structured MMSE Channel Estimation with Mixtures of Factor Analyzers
Benedikt Fesl, Nurettin Turan, Wolfgang Utschick
This work proposes a generative modeling-aided channel estimator based on mixtures of factor analyzers (MFA). In an offline step, the parameters of the generative model are inferre…
A Versatile Low-Complexity Feedback Scheme for FDD Systems via Generative Modeling
Nurettin Turan, Benedikt Fesl, Michael Koller +2
We propose a versatile feedback scheme for both single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. Particularly, we propose utili…
Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation
Benedikt Fesl, Nurettin Turan, Michael Joham +1
In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i.e., the training data are solely comprised of noisy…