Novel Approach for Modeling Wireless Fading Channels using a Finite State Markov Chain

yesEmpirical modeling of wireless fading channels using common schemes such as autoregression and thefinitestate Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that afirst-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed andproven to be less demanding compared to the conventional FSMC approach based on the levelcrossing rate. Simulations confirm the analytical results and promising performance of the new channel modelbased on the Tauchen approach without extracomplexity costs

Spectrum cartography techniques, challenges, opportunities, and applications: A survey

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio

Learning Parameters of Stochastic Radio Channel Models from Summaries

Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

The impact of inter-vehicle communication on vehicular traffic

The work addresses communication networks established over radio equipped vehicles in our everyday road traffic, so called Vehicular Ad Hoc Networks (VANETs), and discusses their impact on two major goals, namely traffic safety and traffic efficiency. For both objectives, the thesis proposes an appropriate modeling of the essential building blocks Traffic, Communication and Application and enables impact assessment studies by means of implemented simulation tools

Modeling and formal verification of probabilistic reconfigurable systems

In this thesis, we propose a new approach for formal modeling and verification of adaptive probabilistic systems. Dynamic reconfigurable systems are the trend of all future technological systems, such as flight control systems, vehicle electronic systems, and manufacturing systems. In order to meet user and environmental requirements, such a dynamic reconfigurable system has to actively adjust its configuration at run-time by modifying its components and connections, while changes are detected in the internal/external execution environment. On the other hand, these changes may violate the memory usage, the required energy and the concerned real-time constraints since the behavior of the system is unpredictable. It might also make the system's functions unavailable for some time and make potential harm to human life or large financial investments. Thus, updating a system with any new configuration requires that the post reconfigurable system fully satisfies the related constraints. We introduce GR-TNCES formalism for the optimal functional and temporal specification of probabilistic reconfigurable systems under resource constraints. It enables the optimal specification of a probabilistic, energetic and memory constraints of such a system. To formally verify the correctness and the safety of such a probabilistic system specification, and the non-violation of its properties, an automatic transformation from GR-TNCES models into PRISM models is introduced. Moreover, a new approach XCTL is also proposed to formally verify reconfigurable systems. It enables the formal certification of uncompleted and reconfigurable systems. A new version of the software ZIZO is also proposed to model, simulate and verify such GR-TNCES model. To prove its relevance, the latter was applied to case studies; it was used to model and simulate the behavior of an IPV4 protocol to prevent the energy and memory resources violation. It was also used to optimize energy consumption of an automotive skid conveyor.In dieser Arbeit wird ein neuer Ansatz zur formalen Modellierung und Verifikation dynamisch rekonfigurierbarer Systeme vorgestellt. Dynamische rekonfigurierbare Systeme sind in vielen aktuellen und zukünftigen Anwendungen, wie beispielsweise Flugsteuerungssystemen, Fahrzeugelektronik und Fertigungssysteme zu finden. Diese Systeme weisen ein probabilistisches, adaptives Verhalten auf. Um die Benutzer- und Umgebungsbedingungen kontinuierlich zu erfüllen, muss ein solches System seine Konfiguration zur Laufzeit aktiv anpassen, indem es seine Komponenten, Verbindungen zwischen Komponenten und seine Daten modifiziert (adaptiv), sobald Änderungen in der internen oder externen Ausführungsumgebung erkannt werden (probabilistisch). Diese Anpassungen dürfen Beschränkungen bei der Speichernutzung, der erforderlichen Energie und bestehende Echtzeitbedingungen nicht verletzen. Eine nicht geprüfte Rekonfiguration könnte dazu führen, dass die Funktionen des Systems für einige Zeit nicht verfügbar wären und potenziell menschliches Leben gefährdet würde oder großer finanzieller Schaden entstünde. Somit erfordert das Aktualisieren eines Systems mit einer neuen Konfiguration, dass das rekonfigurierte System die zugehörigen Beschränkungen vollständig einhält. Um dies zu überprüfen, wird in dieser Arbeit der GR-TNCES-Formalismus, eine Erweiterung von Petrinetzen, für die optimale funktionale und zeitliche Spezifikation probabilistischer rekonfigurierbarer Systeme unter Ressourcenbeschränkungen vorgeschlagen. Die entstehenden Modelle sollen über probabilistische model checking verifiziert werden. Dazu eignet sich die etablierte Software PRISM. Um die Verifikation zu ermöglichen wird in dieser Arbeit ein Verfahren zur Transformation von GR-TNCES-Modellen in PRISM-Modelle beschrieben. Eine neu eingeführte Logik (XCTL) erlaubt zudem die einfache Beschreibung der zu prüfenden Eigenschaften. Die genannten Schritte wurden in einer Softwareumgebung für den automatisierten Entwurf, die Simulation und die formale Verifikation (durch eine automatische Transformation nach PRISM) umgesetzt. Eine Fallstudie zeigt die Anwendung des Verfahren

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out