TY - JOUR
T1 - Generalized Bussgang LMMSE Channel Estimation for One-Bit Massive MIMO Systems
AU - Wan, Qian
AU - Fang, Jun
AU - Duan, Huiping
AU - Chen, Zhi
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - In this paper, we consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. We first extend the conventional Bussgang linear minimum mean square error (BLMMSE) estimator to the general nonzero threshold case. We then study the problem of one-bit quantization design, aiming at minimizing the mean squared error of the generalized BLMMSE estimator. A set partition scheme is proposed to devise the quantization thresholds. The rationale behind the proposed scheme is to divide each antenna's received samples into a number of disjoint subsets according to their pairwise correlation and assign diverse thresholds to those highly correlated data samples. In addition to the set partition scheme, a gradient descent scheme is developed to search for optimal quantization thresholds. The proposed schemes only require the statistical information of the received signals to devise the quantization thresholds, which can be calculated in advance before the training process begins. Simulation results show that the generalized BLMMSE estimator can achieve a significant performance improvement over the conventional Bussgang LMMSE estimator.
AB - In this paper, we consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. We first extend the conventional Bussgang linear minimum mean square error (BLMMSE) estimator to the general nonzero threshold case. We then study the problem of one-bit quantization design, aiming at minimizing the mean squared error of the generalized BLMMSE estimator. A set partition scheme is proposed to devise the quantization thresholds. The rationale behind the proposed scheme is to divide each antenna's received samples into a number of disjoint subsets according to their pairwise correlation and assign diverse thresholds to those highly correlated data samples. In addition to the set partition scheme, a gradient descent scheme is developed to search for optimal quantization thresholds. The proposed schemes only require the statistical information of the received signals to devise the quantization thresholds, which can be calculated in advance before the training process begins. Simulation results show that the generalized BLMMSE estimator can achieve a significant performance improvement over the conventional Bussgang LMMSE estimator.
KW - Massive MIMO systems
KW - channel estimation
KW - one-bit quantization design
UR - http://www.scopus.com/inward/record.url?scp=85087184784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087184784&partnerID=8YFLogxK
U2 - 10.1109/TWC.2020.2981599
DO - 10.1109/TWC.2020.2981599
M3 - Article
AN - SCOPUS:85087184784
SN - 1536-1276
VL - 19
SP - 4234
EP - 4246
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 6
M1 - 9046300
ER -