Page 214 - Kaleidoscope Academic Conference Proceedings 2021

S1.4 Optimal pilot sequence design for machine learning based channel estimation in FDD massive
MIMO systems*
Hayder AL-Salihi, Mohammed Al-Gharbawi (Iraqi Communications & Media Commission,
Iraq); Fatin Said (King's College London, United Kingdom (Great Britain))

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 of
massive 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.

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