46 citations · 183 across the 29 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
On the Mean Square Error Optimal Estimator in One-Bit Quantized Systems
Benedikt Fesl, Michael Koller, Wolfgang Utschick
This paper investigates the mean square error (MSE)-optimal conditional mean estimator (CME) in one-bit quantized systems in the context of channel estimation with jointly Gaussian…
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 Optimal Approximation of the Conditional Mean Channel 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). We fit a GMM to given channel samples to obtain an analytic probability density function (PDF)…
Centralized Learning of the Distributed Downlink Channel Estimators in FDD Systems using Uplink Data
B. Fesl, N. Turan, M. Koller +2
In this work, we propose a convolutional neural network (CNN) based low-complexity approach for downlink (DL) channel estimation (CE) in frequency division duplex (FDD) systems. In…
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural Network
B. Fesl, N. Turan, M. Koller +1
A low-complexity convolutional neural network estimator which learns the minimum mean squared error channel estimator for single-antenna users was recently proposed. We generalize…