Kaiserslautern - Fachbereich Elektrotechnik und Informationstechnik
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The fifth-generation (5G) of wireless networks promises to bring new advances, such as a huge increase in mobile data rates, a plunge in communications latency, and an increase in the quality of experience perceived by users that can cope with the ever-increasing demand in Internet traffic. However, the high cost of capital and operational expenditure (CAPEX/OPEX) of the new 5G network and the lack of a killer application hinder its rapid adoption. In this context, Mobile Network Operators (MNOs) have turned their attention to the following idea: opening up their infrastructure so that vertical businesses can leverage the new 5G network to improve their primary businesses and develop new ones. However, deploying multiple isolated vertical applications on top of the same infrastructure poses unique challenges that must be addressed. In this thesis, we provide critical contributions to developing 5G networks to accommodate different vertical applications in an isolated, flexible, and automated manner. This thesis contributions spawn on three main areas: (i) the development of an integrated fronthaul and backhaul network, (ii) the development of a network slicing overbooking algorithm, and (iii) the development of a method to mitigate the noisy neighbors' problem in a vRAN deployment.
This thesis focuses on the development and analysis of Stochastic Model Predictive Control (SMPC) strategies for both distributed stochastic systems and centralized stochastic systems with partially known distributional information. The first part deals with the development of distributed SMPC schemes that can be synthesized and operated in a fully distributed manner, establishing rigorous theoretical guarantees such as recursive feasibility, stability and closed-loop chance constraint satisfaction. We study several control problems of practical interest, such as the output-feedback regulation problem or the state-feedback tracking problem under additive stochastic noise, and the regulation problem under multiplicative noise. In the second part of this thesis, a novel research topic known as distributionally robust MPC (DR-MPC) is explored, which enhances the applicability of SMPC to real-world problems. DR-MPC is advantageous as it solely necessitates partial knowledge in the form of samples of the uncertainty, which is usually available in practical scenarios, while SMPC mandates exact knowledge of the (unknown) distributional information. We investigate different so-called ambiguity sets to immunize the DR-MPC optimization problem against sampling inaccuracies, leading to tractable optimization problems with strong theoretical guarantees. Altogether, both parts provide rigorous theoretical guarantees with practical design procedures demonstrated by numerical examples, which are the main contributions of this thesis.
Model Identification of Power Electronic Systems for Interaction Studies and Small-Signal Analysis
(2023)
The rapid growth in offshore wind brings various challenges to power system research
and industry, such as the development of multi-terminal multi-vendor HVDC grids.
To ensure interoperability in those power converter dominated systems, suitable
models are needed to efficiently perform stability and interaction studies. With
state-space based small-signal methods stability and interaction phenomena can be
assessed globally for a complex system. Yet detailed models are needed. However,
in multi-vendor projects most likely only black-boxed models will be available to
protect the intellectual property, so that identification techniques are necessary to
obtain suitable models. This thesis contributes to the research activities on statespace
model identification of black-boxed power electronic systems.
In the first part of the thesis, a method was developed and tested, where the matrix
elements of linearized state-space models were fitted in dependency of the operating
point, based on input sweeps performed on the model of a grid forming power converter
controlled as a virtual synchronous machine. It was discussed how changes in
multiple inputs can be approximated by the superposition of the individual input
dependencies and a fully operating point dependent state-space model approximation
was created. The results were validated in time and frequency domain analyses.
It was found that the method can provide a good approximation, especially for the
operating range around the default operating point.
In the second part, identification of a power electronic system was performed based
on measurement data which was generated experimentally from a low voltage laboratory
system. A sequence of input perturbations was applied to the laboratory
system and frequency response data was calculated from the corresponding output
perturbations. The data served as basis for model identification with N4SID and a
soon to be published vector fitting method. The identified models were validated by
a visual inspection of the transfer function and by comparison of the calculated step
responses to the step responses measured in the laboratory. It was found that the
treatment of incomplete data sets, the generation of substitute data and the impact
of time delays on the identification might be worth further investigation.
This work provides a valuable contribution to the research of state-space model
identification of black-boxed power electronic systems. It points out challenges and
presents promising approaches to enable state-space based methods for stability
analysis and interaction studies in future multi-terminal multi-vendor HVDC grids.
Machine Learning (ML) is expected to become an integrated part of future mobile networks due to its capacity for solving complex problems. During inference, ML algorithms extract the hidden knowledge of their input data which is delivered to them through wireless links in many scenarios. Transmission of a massive amount of such input data can impose a huge burden on the mobile network. On the other hand, it is known that ML algorithms can tolerate different levels of distortion on their input components, while the quality of their predictions remains unaffected. Therefore, utilization of the conventional approaches
implies a waste of radio resources, since they target an exact reconstruction of transmitted data, i.e., the input of ML algorithms. In this thesis, we propose a novel relevance based framework that focuses on the quality of final ML outputs instead of such syntax based reconstruction of transmitted inputs. To this end, we quantify the semantics or relevancy of input components in terms of the bit allocation aspect of data compression, where a higher tolerance for distortion implies less relevancy. A lower relevance level is translated into the allocation of less radio resources, e.g., bandwidth. The introduced formulation provides the foundations for the efficient support of ML models with their required data in the inference phase, while wireless resources are employed efficiently.
In this dissertation, a generic relevance based framework utilizing the Kullback-Leibler Divergence (KLD) is developed that is applicable to many realistic scenarios. The system model under study contains multiple sources transmitting correlated multivariate input components of a ML algorithm. The ML model is seen as a black box, which is trained and has fixed parameters while operating in the inference phase. Our proposed bit allocation accounts for the rate-distortion tradeoff. Hence, it is simply adjustable for application to
other problems. Here, an extended version of the proposed bit allocation strategy is introduced for signaling overhead reduction, in which the relevancy level of each input attribute changes instantaneously. In another expansion, to take the effect of dynamic channel states into account, a resource allocation approach for ML based centralized control systems is proposed. The novel quality of service metric takes outputs of ML algorithms into consideration,
and in combination with the designed greedy algorithm, provides significantly
improved end-to-end performance for a network of cart inverted pendulums.
The introduced relevance based framework is comprehensively investigated by considering various case studies, real and synthetic data, regression and classification, different estimators for the KLD, various ML models and codebook designs. Furthermore, the reliability of this proposed solution is explored in presence of packet drops, indicating robustness of the relevance based compression. In all of the simulations, the relevance based solutions deliver the best outcome in terms of the carefully chosen key performance indicators. In most of them, significantly high gains are also achieved compared to the conventional techniques, motivating further research on the subject.
Hardware devices fabricated with recent process technology are intrinsically
more susceptible to faults than before. Resilience against hardware faults is,
therefore, a major concern for safety-critical embedded systems and has been
addressed in several standards. These standards demand a systematic and
thorough safety evaluation, especially for the highest safety levels. However,
any attempt to cover all faults for all theoretically possible scenarios that a sys-
tem might be used in can easily lead to excessive costs. Instead, an application-
dependent approach should be taken: strategies for test and fault resilience
must target only those faults that can actually have an effect in the situations
in which the hardware is being used.
In order to provide the data for such safety evaluations, we propose scalable
and formal methods to analyse the effects of hardware faults on hardware/soft-
ware systems across three abstraction levels where we:
(1) perform a fault effect analysis at instruction set architecture level by em-
ploying fault injection into a hardware-dependent software model called
program netlist,
(2) use the results from the program netlist analysis to perform a deductive
analysis to determine “application-redundant” faults at the gate level by
exploiting standard combinational test pattern generation,
(3) use the results from the program netlist analysis to perform an inductive
analysis to identify all faults of a given fault list that can have an effect
on selected objects of the high-level software, such as specified safety
functions, by employing Abstract Interpretation.
These methods aid in the certification process for the higher safety levels
by (a) providing formal guarantees that certain faults can be ignored and (b)
pointing to those faults which need to be detected in order to ensure product
safety.
We consider transient and permanent faults corrupting data in program-
visible hardware registers and model them using the single-event upset and
stuck-at fault models, respectively.
Scalability of our approaches results from combining an analysis at the ma-
chine and hardware level with separate analyses on gate level and C level
source code, as well as, exploiting certain properties that are characteristic for
embedded systems software. We demonstrate the effectiveness and scalability
of each method on industry-oriented software, including a software system
with about 138 k lines of C code.
Augmented (AR), Virtual (VR) and Mixed Reality (MR) are on their way into everyday life. The recent emergence of consumer-friendly hardware to access this technology has greatly benefited the community. Research and application examples for AR, VR and MR can be found in many fields, such as medicine, sports, the area of cultural heritage, teleworking, entertainment and gaming. Although this technology has been around for decades, immersive applications using this technology are still in their infancy. As manufacturers increase accessibility to these technologies by introducing consumer grade hardware with natural input modalities such as eye gaze or hand tracking, new opportunities but also problems and challenges arise. Researchers strive to develop and investigate new techniques for dynamic content creation or novel interaction techniques. It has yet to be found out which interactions can be made intuitively by users. A major issue is that the possibilities for easy prototyping and rapid testing of new interaction techniques are limited and largely unexplored.
In this thesis, different solutions are proposed to improve gesture-based interaction in immersive environments by introducing gesture authoring tools and developing novel applications. Specifically, hand gestures should be made more accessible to people outside this specialised domain. First, a survey which explores one of the largest and most promising application scenario for AR, VR and MR, namely remote collaboration is introduced. Based on the results of this survey, the thesis focuses on several important issues to consider when developing and creating applications. At its core, the thesis is about rapid prototyping based on panorama images and the use of hand gestures for interactions. Therefore, a technique to create immersive applications with panorama based virtual environments including hand gestures is introduced. A framework to rapidly design, prototype, implement, and create arbitrary one-handed gestures is presented. Based on a user study, the potential of the framework as well as efficacy and usability of hand gestures is investigated. Next, the potential of hand gestures for locomotion tasks in VR is investigated. Additionally, it is analysed how lay people can adapt to the use of hand tracking technology in this context. Lastly, the use of hand gestures for grasping virtual objects is explored and compared to state of the art techniques. Within this thesis, different input modalities and techniques are compared in terms of usability, effort, accuracy, task completion time, user rating, and naturalness.
Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor positioning.. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and 5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and Wi-Fi positioning techniques for indoor localization.
Users privacy is more and more relevant in today digital world. In this paper, we study how mobile network operators (MNOs) practices can lead to loss of privacy for mobile phone subscribers. This article focuses on the mobile phone service providers' implication in privacy violation. Network attacks from other agents, such as cyber-criminals, are not covered in this work.
We review the impact of the location tracking improvement from 2G to 5G networks on police investigations and users' privacy rights.
We also study the role of MNOs in users' sensitive data monetization and the legality behind this practice.
There are few existing publications aiming to enhance mobile phone users' privacy protection against mobile broadband internet providers. We have tried to list all of them in this article.
With the growing support for features such as hardware virtualization tied to the boost of hardware capacity, embedded systems are now able to regroup many software components on a same hardware platform to save costs. This evolution has raised system complexity, motivating the introduction of Mixed-Criticality Systems (MCS) to consolidate applications from different criticality levels on a hardware target: in critical environments such as an aircraft or a factory floor, high-critical functions are now regrouped with other non-critical functions. A key requirement of such system is to guarantee that the execution of a critical function cannot be compromised by other functions, especially by ones with a lower-criticality level. In this context, runtime intrusion detection contributes to secure system execution to avoid an intentional misbehavior in critical applications.
Host Intrusion Detection Systems (HIDS) has been an active field of research for computer security for more than two decades. The goal of HIDS is to detect traces of malicious activity in the execution of a monitored software at runtime. While this topic has been extensively investigated for general-purpose computers, its application in the specific context of embedded MCS is comparatively more recent.
We extend the domain of HIDS research towards HIDS deployment into industrial embedded MCS. For this, we provide a review of state-of-the-art HIDS solutions and evaluate the main problems towards a deployment into an industrial embedded MCS.
We present several HIDS approaches based on solutions for general-purpose computers, which we apply to protect the execution of an application running into an embedded MCS. We introduce two main HIDS methods to protect the execution of a given user-level application. Because of possible criticality constraints of the monitored application, such as industrial certification aspects, our solutions support transparent monitoring; i.e. they do not require application instrumentation. On one hand, we propose a machine-learning (ML) based framework to monitor low-level system events transparently. On the other hand, we introduce a hardware-assisted control-flow monitoring framework to deploy control-flow integrity monitoring without instrumentation of the monitored application.
We provide a methodology to integrate and evaluate HIDS mechanisms into an embedded MCS. We evaluate and implement our monitoring solutions on a practical industrial platform, using generic hardware system and SYSGO’s industrial real-time hypervisor.
The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: (i) to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; (ii) to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization–management and orchestration (NFV–MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and (iii) to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.