428 research outputs found
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Multi-Array 5G V2V Relative Positioning: Performance Bounds
We study the performance bounds of vehicle-to-vehicle (V2V) relative
positioning for vehicles with multiple antenna arrays. The Cram\'{e}r-Rao bound
for the estimation of the relative position and the orientation of the Tx
vehicle is derived, when angle of arrival (AOA) measurements with or without
time-difference of arrival (TDOA) measurements are used. In addition,
geometrically intuitive expressions for the corresponding Fisher information
are provided. The derived bounds are numerically evaluated for different
carrier frequencies, bandwidths and array configurations under different V2V
scenarios, i.e. overtaking and platooning. The significance of the AOA and TDOA
measurements for position estimation is investigated. The achievable
positioning accuracy is then compared with the present requirements of the 3rd
Generation Partnership Project (3GPP) 5G New Radio (NR) vehicle-to-everything
(V2X) standardization
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
Wireless Localization for mmWave Networks in Urban Environments
Millimeter wave (mmWave) technology is expected to be a major component of 5G
wireless networks. Ultra-wide bandwidths of mmWave signals and the possibility
of utilizing large number of antennas at the transmitter and the receiver allow
accurate identification of multipath components in temporal and angular
domains, making mmWave systems advantageous for localization applications. In
this paper, we analyze the performance of a two-step mmWave localization
approach that can utilize time-of-arrival, angle-of-arrival, and
angle-of-departure from multiple nodes in an urban environment with both
line-of-sight (LOS) and non-LOS (NLOS) links. Networks with/without
radio-environmental mapping (REM) are considered, where a network with REM is
able to localize nearby scatterers. Estimation of a UE location is challenging
due to large numbers of local optima in the likelihood function. To address
this problem, a gradient-assisted particle filter (GAPF) estimator is proposed
to accurately estimate a user equipment (UE) location as well as the locations
of nearby scatterers. Monte Carlo simulations show that the GAPF estimator
performance matches the Cramer-Rao bound (CRB). The estimator is also used to
create an REM. It is seen that significant localization gains can be achieved
by increasing beam directionality or by utilizing REM
5G mmwave positioning for vehicular networks
5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented
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