318 citations · 1k across the 84 of their papers we have counts for
33 papers · 1 filter
Performance Analysis of Systems with Coupled and Decoupled RISs
Dominik Semmler, Josef A. Nossek, Michael Joham +1
We analyze and compare different methods for handling the mutual coupling in RIS-aided communication systems. A new mutual coupling aware algorithm is derived where the reactance o…
Alternating Minimization for Wideband Multiuser IRS-aided MIMO Systems under Imperfect CSI
Darian Pérez-Adán, Michael Joham, Óscar Fresnedo +3
This work focuses on wideband intelligent reflecting surface (IRS)-aided multiuser MIMO systems. One of the major challenges of this scenario is the joint design of the frequency-d…
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…
A Zero-Forcing Approach for the RIS-Aided MIMO Broadcast Channel
Dominik Semmler, Michael Joham, Wolfgang Utschick
We present efficient algorithms for the sum-spectral efficiency (SE) maximization of the multi-user reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (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…
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…
High SNR Analysis of RIS-Aided MIMO Broadcast Channels
Dominik Semmler, Michael Joham, Wolfgang Utschick
We analyze the influence of a reconfigurable intelligent surface (RIS) on the Gram channel eigenvalues in a high signal-to-noise ratio (SNR) scenario. This allows to connect specif…
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…
Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO
Matteo Nerini, Valentina Rizzello, Michael Joham +2
To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division…
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)…
Distributed Joint Multi-cell Optimization of IRS Parameters with Linear Precoders
Reinhard Wiesmayr, Michael Honig, Michael Joham +1
We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledg…
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…
Unsupervised Learning of Adaptive Codebooks for Deep Feedback Encoding in FDD Systems
Nurettin Turan, Michael Koller, Samer Bazzi +2
In this work, we propose a joint adaptive codebook construction and feedback generation scheme in frequency division duplex (FDD) systems. Both unsupervised and supervised deep lea…
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…
Learning the CSI Denoising and Feedback Without Supervision
Valentina Rizzello, Wolfgang Utschick
In this work, we develop a joint denoising and feedback strategy for channel state information in frequency division duplex systems. In such systems, the biggest challenge is the o…
Learning the CSI Recovery in FDD Systems
Wolfgang Utschick, Valentina Rizzello, Michael Joham +2
We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, t…
Two-User SIMO Interference Channel with TIN: Improper Signaling versus Time-Sharing
Christoph Hellings, Ferhad Askerbeyli, Wolfgang Utschick
In the two-user Gaussian interference channel with Gaussian inputs and treating interference as noise (TIN), improper complex signals can be beneficial if time-sharing is not allow…
Reproducible Evaluation of Neural Network Based Channel Estimators And Predictors Using A Generic Dataset
Nurettin Turan, Wolfgang Utschick
A low-complexity neural network based approach for channel estimation was proposed recently, where assumptions on the channel model were incorporated into the design procedure of t…
Learning The MMSE Channel Predictor
Nurettin Turan, Wolfgang Utschick
We present a neural network based predictor which is derived by starting from the linear minimum mean squared error (LMMSE) predictor and by further making two key assumptions. Wit…
Maximizing the Partial Decode-and-Forward Rate in the Gaussian MIMO Relay Channel
Christoph Hellings, Patrick Gest, Thomas Wiegart +1
It is known that circularly symmetric Gaussian signals are the optimal input signals for the partial decode-and-forward (PDF) coding scheme in the Gaussian multiple-input multiple-…
Mixed Monotonic Programming for Fast Global Optimization
Bho Matthiesen, Christoph Hellings, Eduard A. Jorswieck +1
While globally optimal solutions to many convex programs can be computed efficiently in polynomial time, this is, in general, not possible for nonconvex optimization problems. Ther…