TY - GEN
T1 - Beamspace channel estimation for 3D lens-based millimeter-wave massive MIMO systems
AU - Gao, Xinyu
AU - Dai, Linglong
AU - Han, Shuangfeng
AU - Chih-Lin, I.
AU - Adachi, Fumiyuki
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/21
Y1 - 2016/11/21
N2 - Lens-based mmWave massive MIMO can significantly reduce the number of required RF chains without obvious performance loss, where the accurate information of beamspace channel is required. However, existing beamspace channel estimation schemes are based on the 2D beamspace channel model. In this paper, we consider the more general 3D beamspace channel model, and propose an adaptive support detection (ASD)-based channel estimation scheme. The basic idea is to decompose the 3D beamspace channel estimation problem into several sub-problems, and each one only deals with a sparse channel component. For each channel component, we first adaptively detect its support with high accuracy by exploiting the horizontal and vertical sparsity of 3D beamspace channel. Then, we remove the influence of this channel component to detect the support of the next channel component. After the support detections of all channel components, we can estimate the nonzero elements of the beamspace channel with low pilot overhead. Simulation results verify that the proposed scheme enjoys satisfying accuracy, even with low SNR.
AB - Lens-based mmWave massive MIMO can significantly reduce the number of required RF chains without obvious performance loss, where the accurate information of beamspace channel is required. However, existing beamspace channel estimation schemes are based on the 2D beamspace channel model. In this paper, we consider the more general 3D beamspace channel model, and propose an adaptive support detection (ASD)-based channel estimation scheme. The basic idea is to decompose the 3D beamspace channel estimation problem into several sub-problems, and each one only deals with a sparse channel component. For each channel component, we first adaptively detect its support with high accuracy by exploiting the horizontal and vertical sparsity of 3D beamspace channel. Then, we remove the influence of this channel component to detect the support of the next channel component. After the support detections of all channel components, we can estimate the nonzero elements of the beamspace channel with low pilot overhead. Simulation results verify that the proposed scheme enjoys satisfying accuracy, even with low SNR.
UR - http://www.scopus.com/inward/record.url?scp=85006728639&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006728639&partnerID=8YFLogxK
U2 - 10.1109/WCSP.2016.7752487
DO - 10.1109/WCSP.2016.7752487
M3 - Conference contribution
AN - SCOPUS:85006728639
T3 - 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
BT - 2016 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Wireless Communications and Signal Processing, WCSP 2016
Y2 - 13 October 2016 through 15 October 2016
ER -