Semi-blind channel estimation and hybrid receiver beamforming in the Tera-Hertz multi-user massive MIMO uplink

Abstract

We develop a pragmatic multi-user (MU) massive
multiple-input multiple-output (MIMO) channel model tailored
to the THz band, encompassing factors such as molecular
absorption, reflection losses and multipath diffused ray com
ponents. Next, we propose a novel semi-blind based channel
state information (CSI) acquisition technique i.e. MU whitening
decorrelation semi-blind (MU-WD-SB) that exploits the second
order statistics corresponding to the unknown data symbols
along with pilot vectors. A constrained Cramér-Rao Lower
Bound (C-CRLB) is derived to bound the normalized mean
square error (NMSE) performance of the proposed semi-blind
learning technique. Our proposed scheme efficiently reduces the
training overheads while enhancing the overall accuracy of the
channel learning process. Furthermore, a novel hybrid receiver
combiner framework is devised for MU THz massive MIMO
systems, leveraging multiple measurement vector based sparse
Bayesian learning (MMV-SBL) that relies on the estimated CSI
acquired through our proposed semi-blind technique relying on
low resolution analog-to-digital converters (ADCs). Finally, we
propose an optimal hybrid combiner based on MMV-SBL, which
directly reduces the MU interference. Extensive simulations are
conducted to evaluate the performance gain of the proposed MU
WD-SB scheme over conventional training-based and other semi
blind learning techniques for a practical THz channel obtained
from the high-resolution transmission (HITRAN) database. The
metrics considered for quantifying the improvements include the
NMSE, bit error rate (BER) and spectral-efficiency (SE).

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Identifiers

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

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