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

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eess.SP2024

Decoupling Networks and Super-Quadratic Gains for RIS Systems with Mutual Coupling

Dominik Semmler, Josef A. Nossek, Michael Joham +2

We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particul…

eess.SP2024

Robust Precoding for FDD MISO Systems via Minorization Maximization

Donia Ben Amor, Michael Joham, Wolfgang Utschick

In this work, we propose an approach to robust precoder design based on a minorization maximization technique that optimizes a surrogate function of the achievable spectral efficie…

eess.SP2024

Bilinear Precoder Based Efficient Rate Splitting Method in FDD Systems

Sadaf Syed, Donia Ben Amor, Michael Joham +1

In this work, we propose a low-cost rate splitting (RS) technique for a multi-user multiple-input single-output (MISO) system operating in frequency division duplex (FDD) mode. The…

eess.SP2024

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…

eess.SP2024

An Efficient Rate Splitting Precoding Approach in Multi-User MISO FDD Systems

Donia Ben Amor, Michael Joham, Wolfgang Utschick

In this work, we develop an efficient precoding strategy for a multi-user multiple-input-single output (MU MISO) system operating in frequency-division-duplex (FDD) mode, where rat…

eess.SP2024

Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation

Benedikt Fesl, Michael Baur, Florian Strasser +2

This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models. Contrary to related works utilizing generative prior…