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document type:
congress contribution (original)
title:
Generative-AI Methods for Channel Impulse Response Generation
keywords:
Channel impulse responses, generative neural networks, normalizing flows, latent space disentanglement
authors:
Weißer, Franz; Mayer, Timo; Baccouche, Bessem; Utschick, Wolfgang
congress title:
25th International ITG Workshop on Smart Antennas (WSA 2021)
year:
2021
abstract:
In this work, we propose methods for generating and manipulating channel impulse responses using normalizing flows. Using standardised, simplified, analytic models, when no perfect description of the channel is known, can lead to performance losses. We are able to show using simulations that our machine learning methods generate channel impulse responses with forced features. In addition to that, we show how disentanglement in the latent space of a normalizing flow can be used for the cha...     »
WWW:
https://ieeexplore.ieee.org/document/9739136
TUM-institution:
Professur für Methoden der Signalverarbeitung
ingested:
27.01.2022
format:
text