TY - GEN
T1 - Learning-Based Channel Estimation for Various Antenna Array Configurations
AU - Koller, Michael
AU - Hellings, Christoph
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Recently, a neural-network-based method for massive MIMO uplink channel estimation was introduced. The derivations assumed a uniform linear array (ULA) with half-wavelength antenna spacing at the base station. In this work, we show that the estimator can also be used in case of ULAs and uniform rectangular arrays (URAs) with antenna spacings given by integer multiples of half the wavelength. We then investigate how the antenna spacing and certain parameters of the channel model influence the estimation performance.
AB - Recently, a neural-network-based method for massive MIMO uplink channel estimation was introduced. The derivations assumed a uniform linear array (ULA) with half-wavelength antenna spacing at the base station. In this work, we show that the estimator can also be used in case of ULAs and uniform rectangular arrays (URAs) with antenna spacings given by integer multiples of half the wavelength. We then investigate how the antenna spacing and certain parameters of the channel model influence the estimation performance.
KW - MMSE channel estimation
KW - machine learning
KW - uniform array
UR - http://www.scopus.com/inward/record.url?scp=85072345573&partnerID=8YFLogxK
U2 - 10.1109/SPAWC.2019.8815482
DO - 10.1109/SPAWC.2019.8815482
M3 - Conference contribution
AN - SCOPUS:85072345573
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019
Y2 - 2 July 2019 through 5 July 2019
ER -