TY - CHAP
T1 - Optimal Pilot Sequence Design for Machine Learning Based Channel Estimation in FDD Massive Mimo Systems
AU - Al-Salihi, Hayder
AU - Al-Gharbawi, Mohammed
AU - Said, Fatin
N1 - Publisher Copyright:
© 2021 ITU.
PY - 2021
Y1 - 2021
N2 - In this paper, we consider the problem of channel estimation for large scale Multiple-Input Multiple-Output (MIMO) systems, in which the main challenge that limits the functionality ofmassive MIMO is the acquisition of precise Channel State Information (CSI). We introduce an efficient channel estimation approach based on a block Sparse Bayesian Learning (SBL) that exploits the temporal common sparsity of channel coefficients. Furthermore, an optimal pilot approach to reduce the pilot overhead is derived. The optimal pilot is obtained by minimizing the Mean Square Error (MSE) of the proposed SBL estimator using Semi-Definite Programming (SDP). Simulation results demonstrate that the SBL-based approach is more robust than conventional methods when fewer training pilots are used.
AB - In this paper, we consider the problem of channel estimation for large scale Multiple-Input Multiple-Output (MIMO) systems, in which the main challenge that limits the functionality ofmassive MIMO is the acquisition of precise Channel State Information (CSI). We introduce an efficient channel estimation approach based on a block Sparse Bayesian Learning (SBL) that exploits the temporal common sparsity of channel coefficients. Furthermore, an optimal pilot approach to reduce the pilot overhead is derived. The optimal pilot is obtained by minimizing the Mean Square Error (MSE) of the proposed SBL estimator using Semi-Definite Programming (SDP). Simulation results demonstrate that the SBL-based approach is more robust than conventional methods when fewer training pilots are used.
KW - Channel estimation
KW - massive MIMO
KW - semidefinite programming
KW - sparse Bayesian learning
UR - http://www.scopus.com/inward/record.url?scp=85124667270&partnerID=8YFLogxK
U2 - 10.23919/ITUK53220.2021.9662117
DO - 10.23919/ITUK53220.2021.9662117
M3 - Conference paper
AN - SCOPUS:85124667270
T3 - 2021 ITU Kaleidoscope: Connecting Physical and Virtual Worlds, ITU K 2021
BT - 2021 ITU Kaleidoscope
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Telecommunication Union Kaleidoscope Academic Conference: Connecting Physical and Virtual Worlds, ITU K 2021
Y2 - 6 December 2021 through 10 December 2021
ER -