activity
20082026
most citedCovariance Matrix Estimation in Massive MIMO

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

collaborators

51 papers

eess.SP2026

Efficient Channel Prediction based on Gram-Square-Root Factorization using GMMs

Kathrin Klein, Amar Kasibovic, Michael Joham +2

Accurate channel state information (CSI) is critical for downlink (DL)-multi-user (MU)-multiple-input multiple-output (MIMO) systems, where feedback delays and mobility can degrade…

eess.SP2026

On the Optimality of Rate Balancing for Max-Min Fair Multicasting

Sadaf Syed, Wolfgang Utschick, Michael Joham

The max-min fair (MMF) multicasting problem is known to be NP-hard. In this work, we analytically derive the optimal solution to this NP-hard problem and establish the equivalence…

cs.IT2025

Precoder Design in Multi-User FDD Systems with VQ-VAE and GNN

Srikar Allaparapu, Michael Baur, Benedikt Böck +2

Robust precoding is efficiently feasible in frequency division duplex (FDD) systems by incorporating the learnt statistics of the propagation environment through a generative model…

cs.IT2025

Is Lattice Reduction Necessary for Vector Perturbation Precoding?

Dominik Semmler, Wolfgang Utschick, Michael Joham

Vector perturbation (VP) precoding is an effective nonlinear precoding technique in the downlink (DL) with modulo channels, providing an approximation of dirty paper coding (DPC) w…

eess.SP2025

Sum-Rate Optimisation of a Multi-User STAR-RIS-Aided System with Low Complexity

Sadaf Syed, Wolfgang Utschick, Michael Joham

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. However, the conventional RIS can only reflect the incident signal. He…

cs.IT2024

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,…