TY - JOUR
T1 - Directional training for FDD massive MIMO
AU - Zhang, Xing
AU - Zhong, Lin
AU - Sabharwal, Ashutosh
N1 - Funding Information:
Manuscript received September 19, 2017; revised February 11, 2018 and March 28, 2018; accepted May 5, 2018. Date of publication May 28, 2018; date of current version August 10, 2018. This work was supported by NSF under Grants CNS-1518916 and CNS-1314822. The associate editor coordinating the review of this paper and approving it for publication was D. Lopez-Perez. (Corresponding author: Xing Zhang.) The authors are with the Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005 USA (e-mail: xz32@rice.edu; lzhong@rice.edu; ashu@rice.edu).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - A key challenge for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) is the large overhead in acquiring channel state information (CSI) for transmits beamforming. In this paper, we propose a scalable method called directional training to obtain downlink CSI. Directional training is motivated by two empirical results derived from massive MIMO channel measurements. First, the number of dominant angle-of-arrivals (departures) is much smaller than and nearly independent of the number of base-station antennas. Second, there is a strong correlation between uplink arrival and downlink departure angles even in FDD systems, which leads to the idea of directional training, where a small number of training symbols can be sent to estimate the dominant components of the downlink channel. Therefore, directional training measures much fewer complex coefficients than full-training-based methods, and as a result, compared with full-training, the overall channel acquisition overhead for directional training scales much slower with the number of base-station antennas. We evaluate directional training with extensive experiments with a 64-antenna base-station at two bands separated by approximately 72 MHz. Our results show that directional training-based downlink beamforming outperforms full-training systems by 150% in terms of average spectral efficiency, and loses only 5.3% average spectral efficiency from genie-aided systems.
AB - A key challenge for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) is the large overhead in acquiring channel state information (CSI) for transmits beamforming. In this paper, we propose a scalable method called directional training to obtain downlink CSI. Directional training is motivated by two empirical results derived from massive MIMO channel measurements. First, the number of dominant angle-of-arrivals (departures) is much smaller than and nearly independent of the number of base-station antennas. Second, there is a strong correlation between uplink arrival and downlink departure angles even in FDD systems, which leads to the idea of directional training, where a small number of training symbols can be sent to estimate the dominant components of the downlink channel. Therefore, directional training measures much fewer complex coefficients than full-training-based methods, and as a result, compared with full-training, the overall channel acquisition overhead for directional training scales much slower with the number of base-station antennas. We evaluate directional training with extensive experiments with a 64-antenna base-station at two bands separated by approximately 72 MHz. Our results show that directional training-based downlink beamforming outperforms full-training systems by 150% in terms of average spectral efficiency, and loses only 5.3% average spectral efficiency from genie-aided systems.
KW - Angle-of-arrival (departure)
KW - Directional training
KW - FDD
KW - Massive MIMO
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U2 - 10.1109/TWC.2018.2838600
DO - 10.1109/TWC.2018.2838600
M3 - Article
AN - SCOPUS:85047645722
VL - 17
SP - 5183
EP - 5197
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
SN - 1536-1276
IS - 8
M1 - 8368089
ER -