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20082026
most citedCovariance Matrix Estimation in Massive MIMO

106 citations · 324 across the 49 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

eess.SP2023★ 1 cited

Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI

Donia Ben Amor, Michael Joham, Wolfgang Utschick

In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency…

cs.IT2023

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…

eess.SP2023

Design of a Single-User RIS-Aided MISO System Based on Statistical Channel Knowledge

Sadaf Syed, Dominik Semmler, Donia Ben Amor +2

Reconfigurable intelligent surface (RIS) is considered a prospective technology for beyond fifth-generation (5G) networks to improve the spectral and energy efficiency at a low cos…

eess.SP2023★ 2 cited

Physically Consistent Modelling of Wireless Links with Reconfigurable Intelligent Surfaces Using Multiport Network Analysis

Josef A. Nossek, Dominik Semmler, Michael Joham +1

RISs are an emerging technology for engineering the channels of future wireless communication systems. The vast majority of research publications on RIS are focussing on system-lev…

cs.IT2023★ 20 cited

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…

eess.SP2023

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…