7 research outputs found
Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
In this paper, we present a robust multipath-based localization and mapping
framework that exploits the phases of specular multipath components (MPCs)
using a massive multiple-input multiple-output (MIMO) array at the base
station. Utilizing the phase information related to the propagation distances
of the MPCs enables the possibility of localization with extraordinary accuracy
even with limited bandwidth. The specular MPC parameters along with the
parameters of the noise and the dense multipath component (DMC) are tracked
using an extended Kalman filter (EKF), which enables to preserve the
distance-related phase changes of the MPC complex amplitudes. The DMC comprises
all non-resolvable MPCs, which occur due to finite measurement aperture. The
estimation of the DMC parameters enhances the estimation quality of the
specular MPCs and therefore also the quality of localization and mapping. The
estimated MPC propagation distances are subsequently used as input to a
distance-based localization and mapping algorithm. This algorithm does not need
prior knowledge about the surrounding environment and base station position.
The performance is demonstrated with real radio-channel measurements using an
antenna array with 128 ports at the base station side and a standard cellular
signal bandwidth of 40 MHz. The results show that high accuracy localization is
possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to
the IEEE Transaction on Wireless Communications for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation
To enable realistic studies of massive multiple-input multiple-output
systems, the COST 2100 channel model is extended based on measurements. First,
the concept of a base station-side visibility region (BS-VR) is proposed to
model the appearance and disappearance of clusters when using a
physically-large array. We find that BS-VR lifetimes are exponentially
distributed, and that the number of BS-VRs is Poisson distributed with
intensity proportional to the sum of the array length and the mean lifetime.
Simulations suggest that under certain conditions longer lifetimes can help
decorrelating closely-located users. Second, the concept of a multipath
component visibility region (MPC-VR) is proposed to model birth-death processes
of individual MPCs at the mobile station side. We find that both MPC lifetimes
and MPC-VR radii are lognormally distributed. Simulations suggest that unless
MPC-VRs are applied the channel condition number is overestimated. Key
statistical properties of the proposed extensions, e.g., autocorrelation
functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived
and analyzed.Comment: Submitted to IEEE Transactions of Wireless Communication
Downlink Single-Snapshot Localization and Mapping with a Single-Antenna Receiver
5G mmWave MIMO systems enable accurate estimation of the user position and
mapping of the radio environment using a single snapshot when both the base
station (BS) and user are equipped with large antenna arrays. However, massive
arrays are initially expected only at the BS side, likely leaving users with
one or very few antennas. In this paper, we propose a novel method for
single-snapshot localization and mapping in the more challenging case of a user
equipped with a single-antenna receiver. The joint maximum likelihood (ML)
estimation problem is formulated and its solution formally derived. To avoid
the burden of a full-dimensional search over the space of the unknown
parameters, we present a novel practical approach that exploits the sparsity of
mmWave channels to compute an approximate joint ML estimate. A thorough
analysis, including the derivation of the Cram\'er-Rao lower bounds, reveals
that accurate localization and mapping can be achieved also in a MISO setup
even when the direct line-of-sight path between the BS and the user is severely
attenuated
Power Allocation and Parameter Estimation for Multipath-based 5G Positioning
We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal
frequency-division multiplexing (OFDM) system with imperfectly synchronized
transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position
based on the received reference signals. The Tx, having (imperfect) prior
knowledge about the Rx location and the surrounding geometry, transmits the
reference signals based on a set of fixed beams. In this work, we develop
strategies for the power allocation among the beams aiming to minimize the
expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional
constraints on the design are included to ensure that the line-of-sight (LOS)
path is detected with high probability. Furthermore, the effect of clock
asynchronism on the resulting allocation strategies is also studied. We also
propose a gridless compressed sensing-based position estimation algorithm,
which exploits the information on the clock offset provided by
non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multipleoutput (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth