activity
20192026
most citedModel Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques

18 citations · 19 across the 15 of their papers we have counts for

collaborators

18 papers

eess.SP2026

Supervised Device Charting with CSI Measurements from Commercial 5G NR User Equipments

Mischa Vasylyev, Frederik Zumegen, Reinhard Wiesmayr +1

Radio frequency fingerprint identification (RFFI) is a promising approach to distinguish physical wireless devices using hardware-induced signal imperfections. Conventional RFFI me…

cs.IT2026

On the Impact of Site-Specific Training for a Real-World 5G NR System

Reinhard Wiesmayr, Nuri Berke Baytekin, Chris Dick +1

Site-specific training can improve wireless receiver performance without increasing computational complexity. However, real-world results have so far focused on fully trainable neu…

eess.SP2026

Channel Charting for Position and Orientation

Daniel Richner, Reinhard Wiesmayr, Frederik Zumegen +1

Channel charting (CC) in real-world coordinates is a recently proposed self-supervised machine learning method that maps high-dimensional channel state information (CSI) to user eq…

eess.SP2026

Spatial and Temporal Generalization of CSI-based Neural Positioning

Till-Yannic Müller, Frederik Zumegen, Reinhard Wiesmayr +1

Channel state information (CSI)-based neural positioning learns a mapping from CSI measurements to user equipment (UE) positions using neural networks. However, most existing perfo…

cs.IT2026

SINR Estimation under Limited Feedback via Online Convex Optimization

Lorenzo Maggi, Boris Bonev, Reinhard Wiesmayr +2

We introduce a novel online convex optimization (OCO) framework to estimate the user's signal-to-interference-plus-noise ratio (SINR) from ACK/NACK feedback, channel quality indica…

eess.SP2026

Site-Specific Finetuning of Neural Receivers with Real-World 5G NR Measurements

Nuri Berke Baytekin, Reinhard Wiesmayr, Sebastian Cammerer +2

Finetuning wireless receivers to a specific deployment scenario can yield significant error-rate performance improvements without increasing processing complexity. However, site-sp…

cs.AR2025

A 14ns-Latency 9Gb/s 0.44mm 62pJ/b Short-Blocklength LDPC Decoder ASIC in 22FDX

Darja Nonaca, Jérémy Guichemerre, Reinhard Wiesmayr +2

Ultra-reliable low latency communication (URLLC) is a key part of 5G wireless systems. Achieving low latency necessitates codes with short blocklengths for which polar codes with s…

eess.SP2025

CSI-Based User Positioning, Channel Charting, and Device Classification with an NVIDIA 5G Testbed

Reinhard Wiesmayr, Frederik Zumegen, Sueda Taner +2

Channel-state information (CSI)-based sensing will play a key role in future cellular systems. However, no CSI dataset has been published from a real-world 5G NR system that facili…

eess.SP2025

Neural Positioning Without External Reference

Till-Yannic Müller, Frederik Zumegen, Reinhard Wiesmayr +2

Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device U…

cs.IT2025

SALAD: Self-Adaptive Link Adaptation

Reinhard Wiesmayr, Lorenzo Maggi, Sebastian Cammerer +3

Adapting the modulation and coding scheme (MCS) to the wireless link quality is critical for maximizing spectral efficiency while ensuring reliability. We propose SALAD (self-adapt…

cs.IT2024

Optimizing Puncturing Patterns of 5G NR LDPC Codes for Few-Iteration Decoding

Reinhard Wiesmayr, Darja Nonaca, Chris Dick +1

Rate-matching of low-density parity-check (LDPC) codes enables a single code description to support a wide range of code lengths and rates. In 5G NR, rate matching is accomplished…

cs.IT2024★ 1 cited

Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR

Reinhard Wiesmayr, Sebastian Cammerer, Fayçal Aït Aoudia +3

We detail the steps required to deploy a multi-user multiple-input multiple-output (MU-MIMO) neural receiver (NRX) in an actual cellular communication system. This raises several e…

eess.SP2024

PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna

Fabian Ulbricht, Gian Marti, Reinhard Wiesmayr +1

Despite extensive research on jamming attacks on wireless communication systems, the potential of machine learning for amplifying the threat of such attacks, or our ability to miti…

cs.IT2024

LoFi User Scheduling for Multiuser MIMO Wireless Systems

Alexandra Gallyas-Sanhueza, Gian Marti, Victoria Palhares +2

We propose new low-fidelity (LoFi) user equipment (UE) scheduling algorithms for multiuser multiple-input multiple-output (MIMO) wireless communication systems. The proposed method…

cs.IT2022

DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

Reinhard Wiesmayr, Chris Dick, Jakob Hoydis +1

Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding…

cs.IT2022

Bit Error and Block Error Rate Training for ML-Assisted Communication

Reinhard Wiesmayr, Gian Marti, Chris Dick +2

Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention.…

cs.IT2021

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…

eess.SP2019★ 18 cited

Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques

Andreas Barthelme, Reinhard Wiesmayr, Wolfgang Utschick

In this paper, we present a machine learning approach for estimating the number of incident wavefronts in a direction of arrival scenario. In contrast to previous works, a multilay…