MIMO-GAN: Generative MIMO Channel Modeling
Abstract
We propose generative channel modeling to learn statistical channel models from channel input-output measurements. Generative channel models can learn more complicated distributions and represent the field data more faithfully. They are tractable and easy to sample from, which can potentially speed up the simulation rounds. To achieve this, we leverage advances in GAN, which helps us learn an implicit distribution over stochastic MIMO channels from observed measurements. In particular, our approach MIMO-GAN implicitly models the wireless channel as a distribution of time-domain band-limited impulse responses. We evaluate MIMO-GAN on 3GPP TDL MIMO channels and observe high-consistency in capturing power, delay and spatial correlation statistics of the underlying channel. In particular, we observe MIMO-GAN achieve errors of under 3.57 ns average delay and -18.7 dB power.
Cite
@article{arxiv.2203.08588,
title = {MIMO-GAN: Generative MIMO Channel Modeling},
author = {Tribhuvanesh Orekondy and Arash Behboodi and Joseph B. Soriaga},
journal= {arXiv preprint arXiv:2203.08588},
year = {2022}
}
Comments
Accepted at IEEE ICC 2022. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works