2 citations · 3 across the 6 of their papers we have counts for
6 papers
Deep Learning Based Adaptive Joint mmWave Beam Alignment
Daniel Tandler, Marc Gauger, Ahmet Serdar Tan +2
The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with…
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…
Deep Learning for Uplink CSI-based Downlink Precoding in FDD massive MIMO Evaluated on Indoor Measurements
Florian Euchner, Niklas Süppel, Marc Gauger +2
When operating massive multiple-input multiple-output (MIMO) systems with uplink (UL) and downlink (DL) channels at different frequencies (frequency division duplex (FDD) operation…
Introducing -lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication
Tim Uhlemann, Alexander Span, Sebastian Dörner +1
Pulse shaping for coherent optical fiber communication has been an active area of research for the past decade. Most of the early schemes are based on classic Nyquist pulse shaping…
Learning Joint Detection, Equalization and Decoding for Short-Packet Communications
Sebastian Dörner, Jannis Clausius, Sebastian Cammerer +1
We propose and practically demonstrate a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a me…
A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning
Florian Euchner, Marc Gauger, Sebastian Dörner +1
A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of various mac…