2,298 research outputs found
Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks
The ever-increasing demand for intelligent, automated, and connected mobility
solutions pushes for the development of an innovative sixth Generation (6G) of
cellular networks. A radical transformation on the physical layer of vehicular
communications is planned, with a paradigm shift towards beam-based millimeter
Waves or sub-Terahertz communications, which require precise beam pointing for
guaranteeing the communication link, especially in high mobility. A key design
aspect is a fast and proactive Initial Access (IA) algorithm to select the
optimal beam to be used. In this work, we investigate alternative IA techniques
to fasten the current fifth-generation (5G) standard, targeting an efficient 6G
design. First, we discuss cooperative position-based schemes that rely on the
position information. Then, motivated by the intuition of a non-uniform
distribution of the communication directions due to road topology constraints,
we design two Probabilistic Codebook (PCB) techniques of prioritized beams. In
the first one, the PCBs are built leveraging past collected traffic
information, while in the second one, we use the Hough Transform over the
digital map to extract dominant road directions. We also show that the
information coming from the angular probability distribution allows designing
non-uniform codebook quantization, reducing the degradation of the performances
compared to uniform one. Numerical simulation on realistic scenarios shows that
PCBs-based beam selection outperforms the 5G standard in terms of the number of
IA trials, with a performance comparable to position-based methods, without
requiring the signaling of sensitive information
Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services
Current robotic systems handle a different range of applications such as video surveillance, delivery
of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems
have been embraced not only by the general population but also by the vertical industries to
help them in performing daily activities. Traditionally, the robotic systems have been deployed in
standalone robots that were exclusively dedicated to performing a specific task such as cleaning the
floor in indoor environments. In recent years, cloud providers started to offer their infrastructures
to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed
robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions
are appearing in this form. As a result, standalone robots became software-enhanced objects
with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading
the heavy processing from the robot to the cloud, it is easier to share services and information from
various robots or agents to achieve better cooperation and coordination.
Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities
(e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications
(e.g., remote control, motion planning, autonomous navigation) that can not be executed with
cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots.
While the cloud providers can ensure certain performance in their infrastructure, very little can be
ensured in the network between the robots and the cloud, especially in the last hop where wireless
radio access networks are involved. Over the last years advances in edge computing, fog computing,
5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating
the interest of the industrial sector to satisfy the stringent and real-time requirements of their
applications. Robotic systems are a key piece in the industrial digital transformation and their benefits
are very well studied in the literature. However, designing and implementing a robotic system
that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency,
reliability) is an ambitious task.
This thesis studies the integration of modern Information andCommunication Technologies (ICTs)
in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of
robotic services. To evaluate the performance of the proposed enhancements, this thesis departs
from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge
robotics system serves to represent two exemplary robotic applications. In particular, autonomous
navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic
system is distributed between the robots and the edge server. The open-source prototype implementation
of the designed edge native robotic system resulted in the creation of two real-world testbeds
that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in
robotic systems.
After detailing the design and prototype implementation of the end-to-end edge native robotic
system, this thesis proposes several enhancements that can be offered to robotic systems by adapting
the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it
proposes exemplary network context-aware enhancements in which the real-time information about
robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to
the actual status of the communication (e.g., radio channel). Three different exemplary context-aware
enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later,
the thesis studies the capability of the edge native robotic system to offer potential savings by means of
computation offloading for robot manipulators in different deployment configurations. Further, the
impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a
robot manipulator and its remote controller are assessed.
In the following part of the thesis, the focus is set on how orchestration solutions can support
mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm
and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile
robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated
solutions present high compatibility with the designed edge robotics system where the robot
driving range is extended without any interruption of the end-to-end edge robotics service. While the
DLT-based federation extends the robot driving range by deploying access point extension on top of
external domain infrastructure, OKpi selects the most suitable access point and computing resource
in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled
mobile robots.
To conclude the thesis the focus is set on how robotic systems can improve their performance by
leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions.
To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical
System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling
technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are
integrated with computing and network domains. The role of AI in each technology domain is identified
by analyzing a set of AI agents at the application and infrastructure level. In the last part of the
thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery
mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer
lost commands caused by interference in the wireless channel. The obtained results are showcasing
the its potential in simulation and real-world experimentation.Programa de Doctorado en IngenierÃa Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett
Business Case and Technology Analysis for 5G Low Latency Applications
A large number of new consumer and industrial applications are likely to
change the classic operator's business models and provide a wide range of new
markets to enter. This article analyses the most relevant 5G use cases that
require ultra-low latency, from both technical and business perspectives. Low
latency services pose challenging requirements to the network, and to fulfill
them operators need to invest in costly changes in their network. In this
sense, it is not clear whether such investments are going to be amortized with
these new business models. In light of this, specific applications and
requirements are described and the potential market benefits for operators are
analysed. Conclusions show that operators have clear opportunities to add value
and position themselves strongly with the increasing number of services to be
provided by 5G.Comment: 18 pages, 5 figure
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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