1,233 research outputs found
Angular-Distance Based Channel Estimation for Holographic MIMO
This paper investigates the channel estimation for holographic MIMO systems
by unmasking their distinctions from the conventional one. Specifically, we
elucidate that the channel estimation, subject to holographic MIMO's
electromagnetically large antenna arrays, has to discriminate not only the
angles of a user/scatterer but also its distance information, namely the
three-dimensional (3D) azimuth and elevation angles plus the distance (AED)
parameters. As the angular-domain representation fails to characterize the
sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED
parameters are firstly decomposed for independently constructing their own
covariance matrices. Then, the recovery of each individual parameter can be
structured as a compressive sensing (CS) problem by harnessing the covariance
matrix constructed. This pair of techniques contribute to a parametric
decomposition and compressed deconstruction (DeRe) framework, along with a
formulation of the maximum likelihood estimation for each parameter. Then, an
efficient algorithm, namely DeRe-based variational Bayesian inference and
message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED
parameters and the robust recovery of sparse channels. Finally, the proposed
channel estimation regime is confirmed to be of great robustness in
accommodating different channel conditions, regardless of the near-field and
far-field contexts of a holographic MIMO system, as well as an improved
performance in comparison to the state-of-the-art benchmarks.Comment: This paper has been accepted for publication in IEEE JSA
Parametric channel estimation for massive MIMO
Channel state information is crucial to achieving the capacity of
multi-antenna (MIMO) wireless communication systems. It requires estimating the
channel matrix. This estimation task is studied, considering a sparse channel
model particularly suited to millimeter wave propagation, as well as a general
measurement model taking into account hybrid architectures. The contribution is
twofold. First, the Cram{\'e}r-Rao bound in this context is derived. Second,
interpretation of the Fisher Information Matrix structure allows to assess the
role of system parameters, as well as to propose asymptotically optimal and
computationally efficient estimation algorithms
Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems
Millimeter wave signals and large antenna arrays are considered enabling
technologies for future 5G networks. While their benefits for achieving
high-data rate communications are well-known, their potential advantages for
accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao
bound (CRB) on position and rotation angle estimation uncertainty from
millimeter wave signals from a single transmitter, in the presence of
scatterers. We also present a novel two-stage algorithm for position and
rotation angle estimation that attains the CRB for average to high
signal-to-noise ratio. The algorithm is based on multiple measurement vectors
matching pursuit for coarse estimation, followed by a refinement stage based on
the space-alternating generalized expectation maximization algorithm. We find
that accurate position and rotation angle estimation is possible using signals
from a single transmitter, in either line-of- sight, non-line-of-sight, or
obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages.
Also, Fig.2, Fig. 10 and Table I are adde
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