6 research outputs found
Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs
One of the key benefits of virtualized radio access networks (vRANs) is
network management flexibility. However, this versatility raises
previously-unseen network management challenges. In this paper, a
learning-based zero-touch vRAN orchestration framework (LOFV) is proposed to
jointly select the functional splits and allocate the virtualized resources to
minimize the long-term management cost. First, testbed measurements of the
behaviour between the users' demand and the virtualized resource utilization
are collected using a centralized RAN system. The collected data reveals that
there are non-linear and non-monotonic relationships between demand and
resource utilization. Then, a comprehensive cost model is proposed that takes
resource overprovisioning, declined demand, instantiation and reconfiguration
into account. Moreover, the proposed cost model also captures different routing
and computing costs for each split. Motivated by our measurement insights and
cost model, LOFV is developed using a model-free reinforcement learning
paradigm. The proposed solution is constructed from a combination of deep
Q-learning and a regression-based neural network that maps the network state
and users' demand into split and resource control decisions. Our numerical
evaluations show that LOFV can offer cost savings by up to 69\% of the optimal
static policy and 45\% of the optimal fully dynamic policy.Comment: This paper has been accepted in Proc. of The 2022 Joint European
Conference on Networks and Communications (EuCNC) & 6G Summi
On the Optimization of Multi-Cloud Virtualized Radio Access Networks
We study the important and challenging problem of virtualized radio access
network (vRAN) design in its most general form. We develop an optimization
framework that decides the number and deployment locations of central/cloud
units (CUs); which distributed units (DUs) each of them will serve; the
functional split that each BS will implement; and the network paths for routing
the traffic to CUs and the network core. Our design criterion is to minimize
the operator's expenditures while serving the expected traffic. To this end, we
combine a linearization technique with a cutting-planes method in order to
expedite the exact solution of the formulated problem. We evaluate our
framework using real operational networks and system measurements, and follow
an exhaustive parameter-sensitivity analysis. We find that the benefits when
departing from single-CU deployments can be as high as 30% for our networks,
but these gains diminish with the further addition of CUs. Our work sheds light
on the vRAN design from a new angle, highlights the importance of deploying
multiple CUs, and offers a rigorous framework for optimizing the costs of
Multi-CUs vRAN.Comment: This preprint is to be published in Proc. of IEEE International
Conference on Communications (ICC) 202
A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond
The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft
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
On the analysis of joint scheduling and functional split selection over C-RAN architectures
ABSTRACT: The incessant growth of the traffic that current mobile networks must carry brings a change of the radio access network architecture really necessary. Cloud-RAN is one of the proposals to achieve higher capacities, better latencies, energy improvements, and greater coordination capacity between base stations, due to the benefits of virtualizing and centralizing baseband processing in data centers. However, it has one main drawback, the need for very high-performance links in the fronthaul network. In order to reduce the requirements imposed by these links, recent works propose to divide the tasks between the controller and the base stations, so that not all the processing is carried out in the centralized entity, in what is known as a functional split. In this way, it is possible to maintain some advantages of C-RAN and reduce the cost of the fronthaul network. There is also the possibility of choosing the functional split level based on the particular network status, which adds greater flexibility to the system and improvements when adapting to different traffic patterns. In this project we implement a scheduler capable of exploiting these advantages.RESUMEN: El incesante crecimiento del tráfico que deben cursar las redes móviles actuales hace cada vez más necesaria un cambio en la arquitectura de la red de acceso radio. La Cloud-RAN es una de las propuestas para lograr mayores capacidades, mejores latencias, mejoras energéticas y mayor capacidad de coordinación entre estaciones base, gracias a la premisa de virtualizar y centralizar el procesado en banda base en centros de datos. Sin embargo, presenta un principal inconveniente, la necesidad de enlaces de muy altas prestaciones en la red fronthaul. Para lograr reducir los requisitos impuestos sobre estos enlaces se propone dividir las tareas entre el controlador y las estaciones base, de forma que no todo el procesado se realice en la entidad centralizada, en lo que se conoce como división funcional. De este modo, se logra mantener parte de las ventajas de C-RAN, y reducir el coste de la red fronthaul. Existe también la posibilidad de elegir el nivel de división funcional en base al estado de la red, lo que añade una mayor flexibilidad al sistema, y mejoras en el momento de adaptarse a los diferentes patrones de tráfico. En este trabajo se implementará un scheduler capaz de explotar dichas ventajas.Máster en IngenierÃa de Telecomunicació
ADVANCED RADIO ACCESS NETWORK FEATURING FLEXIBLE PER-UE SERVICE PROVISIONING AND COLLABORATIVE MOBILE EDGE COMPUTING
Enriched by numerous technological advances, radio access networks (RANs) in the fifth mobile networks generation (5G)-and-beyond strive to meet the goals of both mobile network operators (MNOs) and end-users. While MNOs seek efficiency, resiliency, reliability and flexibility of their networks, end-users are more concerned with the variety and quality of the provided, state-of-the-art, reasonably priced services. This has resulted in a complex, multi-tier, and heterogeneous RAN architecture that is severely challenged to achieve and maintain a strict reliability requirement of seven-nines (i.e., 99.99999% network up-time) and to meet ultra-reliable, low latency communications (URLLC) requirements with a latency upper bound of 1 ms end-to-end roundtrip time. Based on the flexible function split concept and data-plane programmability, this dissertation makes several key contributions to the body of knowledge on advanced, service-oriented RANs in two key core components. The first core component pertains to improving fronthaul efficiency, resiliency, flexibility, and latency performance with a cross-layer integration of Analog-Option-9 function split in the flexible fronthaul paradigm. Within the folds of that, the novel cross-layer digital-analog integration is experimentally investigated to pave the way for promising analog technologies to find their niche in 5G-and-beyond. The second core component is related to the design of lightweight, fronthaul-positioned multi-access edge computing (MEC) units to host Cooperative-URLLC applications at the edge of the fronthaul. Hence, from the vertical perspective, the dissertation provides solutions to support general URLLC applications and the Cooperative-URLLC variation by shrinking and eliminating latency sources at the Top-of-RAN and Low-RAN segments of advanced RANs.Ph.D