TY - GEN
T1 - AN ASYMPTOTICALLY OPTIMAL APPROXIMATION OF THE CONDITIONAL MEAN CHANNEL ESTIMATOR BASED ON GAUSSIAN MIXTURE MODELS
AU - Koller, Michael
AU - Fesl, Benedikt
AU - Turan, Nurettin
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - 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 estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF. This optimal estimator cannot be calculated analytically because the true channel PDF is generally not available. To motivate the GMM-based estimator, we show that it converges to the optimal CME as the number of GMM components is increased. In numerical experiments, a reasonable number of GMM components already shows promising estimation results.
AB - 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 estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF. This optimal estimator cannot be calculated analytically because the true channel PDF is generally not available. To motivate the GMM-based estimator, we show that it converges to the optimal CME as the number of GMM components is increased. In numerical experiments, a reasonable number of GMM components already shows promising estimation results.
KW - Gaussian mixture models
KW - conditional mean channel estimation
KW - expectation-maximization
KW - machine learning
KW - spatial channel model
UR - http://www.scopus.com/inward/record.url?scp=85130490157&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9747226
DO - 10.1109/ICASSP43922.2022.9747226
M3 - Conference contribution
AN - SCOPUS:85130490157
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5268
EP - 5272
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Y2 - 22 May 2022 through 27 May 2022
ER -