TY - JOUR
T1 - Randomized Approximate Channel Estimator in Massive-MIMO Communication
AU - Li, Bin
AU - Wang, Shuseng
AU - Zhang, Jun
AU - Cao, Xianbin
AU - Zhang, Jun
AU - Zhao, Chenglin
N1 - Publisher Copyright:
© 1997-2012 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Massive MIMO is considered as one key enabling technology in 5G communications. Although the high-quality channel state information (CSI) estimation is essential to improve the energy/spectrum efficiency of massive MIMO systems, the optimal MMSE estimator unfortunately creates one major challenge, due to its formidable computation complexity. In this letter, we propose a rank-restrained low-complexity MMSE channel estimator for massive MIMO communications, leveraging a novel concept of randomized low-rank approximation. To accomplish this, we first design a two-stage pilot training scheme. Rather than directly estimating the large channel matrix as usual, we then acquire two low-dimensional subsets of it, which respectively gives the row and column sampling version of unknown channel matrix. Finally, we reconstruct the complete channel matrix via such two estimated low-dimensional sketches, by exploiting the low-rank structure of channel. As demonstrated, our scheme significantly reduces the time complexity as well as the energy consumption in CSI acquisition, which yet attains the near-optimal estimation accuracy. It thus provides great promises to the practical deployment of massive MIMO communications.
AB - Massive MIMO is considered as one key enabling technology in 5G communications. Although the high-quality channel state information (CSI) estimation is essential to improve the energy/spectrum efficiency of massive MIMO systems, the optimal MMSE estimator unfortunately creates one major challenge, due to its formidable computation complexity. In this letter, we propose a rank-restrained low-complexity MMSE channel estimator for massive MIMO communications, leveraging a novel concept of randomized low-rank approximation. To accomplish this, we first design a two-stage pilot training scheme. Rather than directly estimating the large channel matrix as usual, we then acquire two low-dimensional subsets of it, which respectively gives the row and column sampling version of unknown channel matrix. Finally, we reconstruct the complete channel matrix via such two estimated low-dimensional sketches, by exploiting the low-rank structure of channel. As demonstrated, our scheme significantly reduces the time complexity as well as the energy consumption in CSI acquisition, which yet attains the near-optimal estimation accuracy. It thus provides great promises to the practical deployment of massive MIMO communications.
KW - Massive MIMO
KW - channel estimation
KW - low complexity/low power
KW - low-rank
KW - randomized matrix approximation
UR - http://www.scopus.com/inward/record.url?scp=85092696946&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2020.3001931
DO - 10.1109/LCOMM.2020.3001931
M3 - Article
AN - SCOPUS:85092696946
SN - 1089-7798
VL - 24
SP - 2314
EP - 2318
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 10
M1 - 9115654
ER -