Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Benedikt Fesl"'
In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i.e., the training data are solely comprised of noisy and sparsely allocated pilot observations. In a practical application
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d10a2e79f6bd4d6375fa22f0cf8c4c3
We propose a precoder codebook construction and feedback encoding scheme which is based on Gaussian mixture models (GMMs). In an offline phase, the base station (BS) first fits a GMM to uplink (UL) training samples. Thereafter, it designs a codebook
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b7b1446abc3aec430c4fd81787d5dcf
This paper investigates a channel estimator based on Gaussian mixture models (GMMs) in the context of linear inverse problems with additive Gaussian noise. We fit a GMM to given channel samples to obtain an analytic probability density function (PDF)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f14fd78fc8b1f39f077855699553c92
http://arxiv.org/abs/2112.12499
http://arxiv.org/abs/2112.12499
Publikováno v:
SPAWC
A convolutional neural network estimator which learns the minimum mean squared error channel estimator for single-antenna users was recently proposed. We generalize the architecture to the estimation of MIMO channels with multiple-antenna users and i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f432449718b4e2e3ca1929cfd66bf3b8
This paper investigates a channel estimator based on Gaussian mixture models (GMMs). We fit a GMM to given channel samples to obtain an analytic probability density function (PDF) which approximates the true channel PDF. Then, a conditional mean chan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c73593bf41144cd6dd28de1e76d9dfbd
Publikováno v:
ACSSC
A low-complexity convolutional neural network (CNN) channel estimator has been proposed recently, which was designed based on assumptions on the underlying channel model. In this work, we investigate how one-bit quantized observations affect this CNN
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94a407aa0808a51ad5aef0f68c1ed5d7
https://doi.org/10.1109/ieeeconf51394.2020.9443563
https://doi.org/10.1109/ieeeconf51394.2020.9443563
