43 citations · 72 across the 9 of their papers we have counts for
4 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…
End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Shrinivas Chimmalgi, Laurent Schmalen, Vahid Aref
Probabilistic constellation shaping enables easy rate adaption and has been proven to reduce the gap to Shannon capacity. Constellation point probabilities are optimized to maximiz…
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…
Optimized Geometric Constellation Shaping for Wiener Phase Noise Channels with Viterbi-Viterbi Carrier Phase Estimation
Andrej Rode, Wintana Araya Gebrehiwot, Shrinivas Chimmalgi +1
The Viterbi & Viterbi (V&V) algorithm is well understood for QPSK and 16-QAM, but modifications are required for higher-order modulation formats. We present an approach to extend t…