Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Dörner, Sebastian"'
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 low latency and high achievable beamforming gain. Much of the recent
Externí odkaz:
http://arxiv.org/abs/2401.13587
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-art proximal policy optimization algorithm as an example. In comparison
Externí odkaz:
http://arxiv.org/abs/2302.08969
When operating massive multiple-input multiple-output (MIMO) systems with uplink (UL) and downlink (DL) channels at different frequencies (frequency division duplex (FDD) operation), acquisition of channel state information (CSI) for downlink precodi
Externí odkaz:
http://arxiv.org/abs/2209.10977
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 that was originally intended for linear channels. The best known clas
Externí odkaz:
http://arxiv.org/abs/2207.06089
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 message before actually decoding it. For this, we extend the recently pr
Externí odkaz:
http://arxiv.org/abs/2207.05699
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 machine learning algorithms for user localization, JCAS, channel charting
Externí odkaz:
http://arxiv.org/abs/2206.15302
The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional CSI that is acquired by a multi-antenna wireless system. Since, in static environments, CSI is a function of the transmitter location, a mappin
Externí odkaz:
http://arxiv.org/abs/2206.09774
Autor:
Fischer, Moritz Benedikt, Dörner, Sebastian, Cammerer, Sebastian, Shimizu, Takayuki, Lu, Hongsheng, Brink, Stephan ten
We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel conditions. This online adaptation via retraining is mainly motivated by t
Externí odkaz:
http://arxiv.org/abs/2203.13571
Attracted by its scalability towards practical codeword lengths, we revisit the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer communications. For this, we study the existing concepts of Turbo-autoencoders from the literature and com
Externí odkaz:
http://arxiv.org/abs/2104.14234
Autor:
Fischer, Moritz Benedikt, Dörner, Sebastian, Cammerer, Sebastian, Shimizu, Takayuki, Cheng, Bin, Lu, Hongsheng, Brink, Stephan ten
We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. For this, we propose a low-complexity recurrent neural network (RNN)-ba
Externí odkaz:
http://arxiv.org/abs/2102.03163