46 citations · 112 across the 29 of their papers we have counts for
30 papers
Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals
Kathrin Klein, Benedikt Böck, Nurettin Turan +1
This work addresses the challenge of making generative models suitable for resource-constrained environments like mobile wireless communication systems. We propose a generative mod…
Semi-Blind Strategies for MMSE Channel Estimation Utilizing Generative Priors
Franz Weißer, Nurettin Turan, Dominik Semmler +2
This paper investigates semi-blind channel estimation for massive multiple-input multiple-output (MIMO) systems. To this end, we first estimate a subspace based on all received sym…
Statistical Precoder Design in Multi-User Systems via Graph Neural Networks and Generative Modeling
Nurettin Turan, Srikar Allaparapu, Donia Ben Amor +3
This letter proposes a graph neural network (GNN)-based framework for statistical precoder design that leverages model-based insights to compactly represent statistical knowledge,…
Feedback Design with VQ-VAE for Robust Precoding in Multi-User FDD Systems
Nurettin Turan, Michael Baur, Jianqing Li +1
In this letter, we propose a vector quantized-variational autoencoder (VQ-VAE)-based feedback scheme for robust precoder design in multi-user frequency division duplex (FDD) system…
A Versatile Pilot Design Scheme for FDD Systems Utilizing Gaussian Mixture Models
Nurettin Turan, Benedikt Böck, Benedikt Fesl +3
In this work, we propose a Gaussian mixture model (GMM)-based pilot design scheme for downlink (DL) channel estimation in single- and multi-user multiple-input multiple-output (MIM…
Evaluation Metrics and Methods for Generative Models in the Wireless PHY Layer
Michael Baur, Nurettin Turan, Simon Wallner +1
Generative models are typically evaluated by direct inspection of their generated samples, e.g., by visual inspection in the case of images. Further evaluation metrics like the Fré…
A Statistical Characterization of Wireless Channels Conditioned on Side Information
Benedikt Böck, Michael Baur, Nurettin Turan +2
Statistical prior channel knowledge, such as the wide-sense-stationary-uncorrelated-scattering (WSSUS) property, and additional side information both can be used to enhance physica…
Channel-Adaptive Pilot Design for FDD-MIMO Systems Utilizing Gaussian Mixture Models
Nurettin Turan, Benedikt Fesl, Benedikt Böck +2
In this work, we propose to utilize Gaussian mixture models (GMMs) to design pilots for downlink (DL) channel estimation in frequency division duplex (FDD) systems. The GMM capture…
Wireless Channel Prediction via Gaussian Mixture Models
Nurettin Turan, Benedikt Böck, Kai Jie Chan +5
In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) wi…
Limited Feedback on Measurements: Sharing a Codebook or a Generative Model?
Nurettin Turan, Benedikt Fesl, Michael Joham +5
Discrete Fourier transform (DFT) codebook-based solutions are well-established for limited feedback schemes in frequency division duplex (FDD) systems. In recent years, data-aided…
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…
Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems
Benedikt Fesl, Andreas Faika, Nurettin Turan +2
We consider channel estimation in systems equipped with a reconfigurable intelligent surface (RIS). In order to illuminate the additional cascaded channel as compared to systems wi…
Evaluation of a Gaussian Mixture Model-based Channel Estimator using Measurement Data
Nurettin Turan, Benedikt Fesl, Moritz Grundei +2
In this work, we use real-world data in order to evaluate and validate a machine learning (ML)-based algorithm for physical layer functionalities. Specifically, we apply a recently…
GMM-based Codebook Construction and Feedback Encoding in FDD Systems
Nurettin Turan, Michael Koller, Benedikt Fesl +3
We propose a precoder codebook construction and feedback encoding scheme which is based on Gaussian mixture models (GMMs). In an offline phase, the base station (BS) first fits a G…
Channel Estimation based on Gaussian Mixture Models with Structured Covariances
Benedikt Fesl, Michael Joham, Sha Hu +3
In this work, we propose variations of a Gaussian mixture model (GMM) based channel estimator that was recently proven to be asymptotically optimal in the minimum mean square error…
An Asymptotically MSE-Optimal Estimator based on Gaussian Mixture Models
Michael Koller, Benedikt Fesl, Nurettin Turan +1
This paper investigates a channel estimator based on Gaussian mixture models (GMMs) in the context of linear inverse problems with additive Gaussian noise. We fit a GMM to given ch…