TY - GEN
T1 - Weighted fast iterative shrinkage thresholding for 3D massive MIMO channel estimation
AU - Nasser, Ahmed
AU - Elsabrouty, Maha
AU - Muta, Osamu
N1 - Funding Information:
This work has been supported by the Egyptian Ministry of Higher Education (MOHE), and Egypt Japan University of Science and Technology (E-JUST).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/14
Y1 - 2018/2/14
N2 - Fitting the huge number of pilots needed for massive multiple inputs multiple outputs antennas (MIMO) channel estimation within the time and frequency resources is a challenging problem. Generally, compressed sensing (CS) channel estimation algorithms face the dilemma of trading off the estimation accuracy and the computational complexity. In this paper, we propose a weighted fast iterative shrinkage thresholding algorithm (W-FISTA). The proposed algorithm provides higher estimation efficiency with the same complexity as the original FISTA. With low computational complexity, multiple measurement vectors (MMV) version of the W-FISTA is proposed to estimate the 3D massive MIMO channel. The proposed MMV-WFISTA estimate the channel coefficients by exploiting its joint sparsity structure in the angle-delay sparse domain. The complexity analysis and the simulation results indicate a clear improvement in the performance of the proposed MMV-WFISTA over joint estimation algorithms.
AB - Fitting the huge number of pilots needed for massive multiple inputs multiple outputs antennas (MIMO) channel estimation within the time and frequency resources is a challenging problem. Generally, compressed sensing (CS) channel estimation algorithms face the dilemma of trading off the estimation accuracy and the computational complexity. In this paper, we propose a weighted fast iterative shrinkage thresholding algorithm (W-FISTA). The proposed algorithm provides higher estimation efficiency with the same complexity as the original FISTA. With low computational complexity, multiple measurement vectors (MMV) version of the W-FISTA is proposed to estimate the 3D massive MIMO channel. The proposed MMV-WFISTA estimate the channel coefficients by exploiting its joint sparsity structure in the angle-delay sparse domain. The complexity analysis and the simulation results indicate a clear improvement in the performance of the proposed MMV-WFISTA over joint estimation algorithms.
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U2 - 10.1109/PIMRC.2017.8292556
DO - 10.1109/PIMRC.2017.8292556
M3 - Conference contribution
AN - SCOPUS:85045277490
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 1
EP - 5
BT - 2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017
Y2 - 8 October 2017 through 13 October 2017
ER -