53 research outputs found
A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO
Employing low resolution analog-to-digital converters in massive
multiple-input multiple-output (MIMO) has many advantages in terms of total
power consumption, cost and feasibility of such systems. However, such
advantages come together with significant challenges in channel estimation and
data detection due to the severe quantization noise present. In this study, we
propose a novel iterative receiver for quantized uplink single carrier MIMO
(SC-MIMO) utilizing an efficient message passing algorithm based on the
Bussgang decomposition and Ungerboeck factorization, which avoids the use of a
complex whitening filter. A reduced state sequence estimator with bidirectional
decision feedback is also derived, achieving remarkable complexity reduction
compared to the existing receivers for quantized SC-MIMO in the literature,
without any requirement on the sparsity of the transmission channel. Moreover,
the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO
under frequency-selective channel, which do not require any cyclic-prefix
overhead, is also derived. We observe that the proposed receiver has
significant performance gains with respect to the existing receivers in the
literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication.
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FCFGS-CV-Based Channel Estimation for Wideband MmWave Massive MIMO Systems with Low-Resolution ADCs
In this paper, the fully corrective forward greedy selection-cross
validation-based (FCFGS-CV-based) channel estimator is proposed for wideband
millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems
with low-resolution analog-to-digital converters (ADCs). The sparse nature of
the mmWave virtual channel in the angular and delay domains is exploited to
convert the maximum a posteriori (MAP) channel estimation problem to an
optimization problem with a concave objective function and sparsity constraint.
The FCFGS algorithm, which is the generalized orthogonal matching pursuit (OMP)
algorithm, is used to solve the sparsity-constrained optimization problem.
Furthermore, the CV technique is adopted to determine the proper termination
condition by detecting overfitting when the sparsity level is unknown.Comment: to appear in IEEE Wireless Communications Letter
Gradient Pursuit-Based Channel Estimation for MmWave Massive MIMO Systems with One-Bit ADCs
In this paper, channel estimation for millimeter wave (mmWave) massive
multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital
converters (ADCs) is considered. In the mmWave band, the number of propagation
paths is small, which results in sparse virtual channels. To estimate sparse
virtual channels based on the maximum a posteriori (MAP) criterion,
sparsity-constrained optimization comes into play. In general, optimizing
objective functions with sparsity constraints is NP-hard because of their
combinatorial complexity. Furthermore, the coarse quantization of one-bit ADCs
makes channel estimation a challenging task. In the field of compressed sensing
(CS), the gradient support pursuit (GraSP) and gradient hard thresholding
pursuit (GraHTP) algorithms were proposed to approximately solve
sparsity-constrained optimization problems iteratively by pursuing the gradient
of the objective function via hard thresholding. The accuracy guarantee of
these algorithms, however, breaks down when the objective function is
ill-conditioned, which frequently occurs in the mmWave band. To prevent the
breakdown of gradient pursuit-based algorithms, the band maximum selecting
(BMS) technique, which is a hard thresholder selecting only the "band maxima,"
is applied to GraSP and GraHTP to propose the BMSGraSP and BMSGraHTP algorithms
in this paper.Comment: to appear in PIMRC 2019, Istanbul, Turke
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