TY - GEN
T1 - Generative-AI Methods for Channel Impulse Response Generation
AU - Weisser, Franz
AU - Mayer, Timo
AU - Baccouche, Bessem
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© VDE VERLAG GMBH ∙ Berlin ∙ Offenbach
PY - 2021
Y1 - 2021
N2 - 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 changing of certain features. We evaluate our methods using the maximum mean discrepancy.
AB - 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 changing of certain features. We evaluate our methods using the maximum mean discrepancy.
KW - Channel impulse responses
KW - Generative neural networks
KW - Latent space disentanglement
KW - Normalizing flows
UR - http://www.scopus.com/inward/record.url?scp=85124327107&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85124327107
T3 - WSA 2021 - 25th International ITG Workshop on Smart Antennas
SP - 65
EP - 70
BT - WSA 2021 - 25th International ITG Workshop on Smart Antennas
PB - VDE VERLAG GMBH
T2 - 25th International ITG Workshop on Smart Antennas, WSA 2021
Y2 - 10 November 2021 through 12 November 2021
ER -