10 citations · 17 across the 11 of their papers we have counts for
9 papers · 1 filter
A Tensor-Train Framework for Bayesian Inference in High-Dimensional Systems: Applications to MIMO Detection and Channel Decoding
Luca Schmid, Dominik Sulz, Shrinivas Chimmalgi +1
Bayesian inference in high-dimensional discrete-input additive noise models is a fundamental challenge in communication systems, as the support of the required joint a posteriori p…
Signal Space-Transformed Expectation Propagation for Symbol Detection in ISI Channels
Jannis Clausius, Luca Schmid, Laurent Schmalen +1
Iterative message passing detection based on expectation propagation (EP) has demonstrated near-optimum performance in many signal processing and communication scenarios. The metho…
Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation
Luca Schmid, Tomer Raviv, Nir Shlezinger +1
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and…
Fast and Robust Expectation Propagation MIMO Detection via Preconditioned Conjugated Gradient
Luca Schmid, Dominik Sulz, Laurent Schmalen
We study the expectation propagation (EP) algorithm for symbol detection in massive multiple-input multiple-output (MIMO) systems. The EP detector shows excellent performance but s…
Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs
Luca Schmid, Tomer Raviv, Nir Shlezinger +1
We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In p…
Approximate Maximum a Posteriori Carrier Phase Estimator for Wiener Phase Noise Channels using Belief Propagation
Shrinivas Chimmalgi, Andrej Rode, Luca Schmid +1
The blind phase search (BPS) algorithm for carrier phase estimation is known to have sub-optimal performance for probabilistically shaped constellations. We present a belief propag…
Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning
Lukas Rapp, Luca Schmid, Andrej Rode +1
We propose a novel method to optimize the structure of factor graphs for graph-based inference. As an example inference task, we consider symbol detection on linear inter-symbol in…
Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs
Luca Schmid, Laurent Schmalen
We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection…
Neural Enhancement of Factor Graph-based Symbol Detection
Luca Schmid, Laurent Schmalen
We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Cyclic factor graphs have the potential to yield low-compl…