1,174 research outputs found
Performance Analysis of Small Cells' Deployment under Imperfect Traffic Hotspot Localization
Heterogeneous Networks (HetNets), long been considered in operators' roadmaps
for macrocells' network improvements, still continue to attract interest for 5G
network deployments. Understanding the efficiency of small cell deployment in
the presence of traffic hotspots can further draw operators' attention to this
feature. In this context, we evaluate the impact of imperfect small cell
positioning on the network performances. We show that the latter is mainly
impacted by the position of the hotspot within the cell: in case the hotspot is
near the macrocell, even a perfect positioning of the small cell will not yield
improved performance due to the interference coming from the macrocell. In the
case where the hotspot is located far enough from the macrocell, even a large
error in small cell positioning would still be beneficial in offloading traffic
from the congested macrocell.Comment: This article is already published in IEEE Global Communications
Conference (GLOBECOM) 201
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
Indoor localization systems-tracking objects and personnel with sensors, wireless networks and RFID
Advances in ubiquitous mobile computing and rapid spread of information
systems have fostered a growing interest in indoor location-aware or location-based
technologies. In this paper we will introduce the primary technologies used in indoor
localization systems by classifying them in three categories: Non-RF technologies,
Active-RF technologies and Passive-RF technologies. Both commercialized products and
research prototypes in all categories are involved in our discussion. The Passive-RF
technologies are further divided into “Mobile tag” and “Mobile reader” systems. We
expect such classification can cover most of the indoor localization systems. Features of
these systems are briefly compared at the end of this paper
Target Tracking in Confined Environments with Uncertain Sensor Positions
To ensure safety in confined environments such as mines or subway tunnels, a
(wireless) sensor network can be deployed to monitor various environmental
conditions. One of its most important applications is to track personnel,
mobile equipment and vehicles. However, the state-of-the-art algorithms assume
that the positions of the sensors are perfectly known, which is not necessarily
true due to imprecise placement and/or dropping of sensors. Therefore, we
propose an automatic approach for simultaneous refinement of sensors' positions
and target tracking. We divide the considered area in a finite number of cells,
define dynamic and measurement models, and apply a discrete variant of belief
propagation which can efficiently solve this high-dimensional problem, and
handle all non-Gaussian uncertainties expected in this kind of environments.
Finally, we use ray-tracing simulation to generate an artificial mine-like
environment and generate synthetic measurement data. According to our extensive
simulation study, the proposed approach performs significantly better than
standard Bayesian target tracking and localization algorithms, and provides
robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201
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