887 citations · 1.2k across the 50 of their papers we have counts for
14 papers · 1 filter
Row-Merged Polar Codes: Analysis, Design and Decoder Implementation
Andreas Zunker, Marvin Geiselhart, Lucas Johannsen +4
Row-merged polar codes are a family of pre-transformed polar codes (PTPCs) with little precoding overhead. Providing an improved distance spectrum over plain polar codes, they are…
Augmenting Channel Charting with Classical Wireless Source Localization Techniques
Florian Euchner, Phillip Stephan, Stephan ten Brink
Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting cha…
Learning Radio Environments by Differentiable Ray Tracing
Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer +4
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene…
Enumeration of Minimum Weight Codewords of Pre-Transformed Polar Codes by Tree Intersection
Andreas Zunker, Marvin Geiselhart, Stephan ten Brink
Pre-transformed polar codes (PTPCs) form a class of codes that perform close to the finite-length capacity bounds. The minimum distance and the number of minimum weight codewords a…
Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting
Phillip Stephan, Florian Euchner, Stephan ten Brink
Channel charting is a self-supervised learning technique whose objective is to reconstruct a map of the radio environment, called channel chart, by taking advantage of similarity r…
Successive Cancellation Automorphism List Decoding of Polar Codes
Lucas Johannsen, Claus Kestel, Marvin Geiselhart +3
The discovery of suitable automorphisms of polar codes gained a lot of attention by applying them in Automorphism Ensemble Decoding (AED) to improve the error-correction performanc…
Multi-Target Localization in Multi-Static Integrated Sensing and Communication Deployments
Maximilian Bauhofer, Silvio Mandelli, Marcus Henninger +2
In future wireless communication networks, existing active localization will gradually evolve into more sophisticated (passive) sensing functionalities. One main enabler for this p…
CRAP: Clutter Removal with Acquisitions Under Phase Noise
Marcus Henninger, Silvio Mandelli, Artjom Grudnitsky +2
The emergence of Integrated Sensing and Communication (ISAC) in future 6G networks comes with a variety of challenges to be solved. One of those is clutter removal, which should be…
Component Training of Turbo Autoencoders
Jannis Clausius, Marvin Geiselhart, Stephan ten Brink
Isolated training with Gaussian priors (TGP) of the component autoencoders of turbo-autoencoder architectures enables faster, more consistent training and better generalization to…
Phase-Equivariant Polar Coded Modulation
Marvin Geiselhart, Marc Gauger, Felix Krieg +2
For short-packet, low-latency communications over random access channels, piloting overhead significantly reduces spectral efficiency. Therefore, pilotless systems recently gained…
Rate-Compatible Polar Codes for Automorphism Ensemble Decoding
Marvin Geiselhart, Jannis Clausius, Stephan ten Brink
Recently, automorphism ensemble decoding (AED) has drawn research interest as a more computationally efficient alternative to successive cancellation list (SCL) decoding of polar c…
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication
Sebastian Jung, Tim Uhlemann, Alexander Span +2
Higher-order solitons inherently possess a spatial periodicity along the propagation axis. The pulse expands and compresses in both, frequency and time domain. This property is exp…
Automorphism Ensemble Polar Code Decoders for 6G URLLC
Claus Kestel, Marvin Geiselhart, Lucas Johannsen +2
The URLLC scenario in the upcoming 6G standard requires low latency and ultra reliable transmission, i.e., error correction towards ML performance. Achieving near-ML performance is…
Deep Reinforcement Learning for mmWave Initial Beam Alignment
Daniel Tandler, Sebastian Dörner, Marc Gauger +1
We investigate the applicability of deep reinforcement learning algorithms to the adaptive initial access beam alignment problem for mmWave communications using the state-of-the-ar…