Multichannel power line communication

Power line communication (PLC) is the technology in which the data signals of a communication system are transmitted through the conductors of a power delivery infrastructure. The unique environment of the PLC channels create specific challenges and requirements, which need to be modeled and analyzed properly in order to obtain a clear understanding of the communication system as well as attaining the ability to further improve the performance and reliability of the transmission. Moreover, the demand for increased data throughput as well as increased reliability and robustness of the transmission is of fundamental importance in any communication system as it is in PLC systems. In order to address these challenges and demands, the concept of multichannel PLC is studied and developed in this thesis. Multichannel PLC in this context is referred to the transmission of multiple information-carrying signals though the power line channel from one source to one destination. We study multiple scenarios of multichannel data transmission in order to cover the diverse situations and requirements of a PLC transmission. One of the multichannel scenarios discussed in this thesis is the multiple-input multiple-output (MIMO) transmission, in which multiple data signals are transmitted via spatially separated PLC channels. Another scenario discussed in this thesis is the cooperative transmission between the source and destination of a PLC system by means of intermediate relay nodes in the network. Finally, the multiband transmission by utilizing different parts of the available PLC spectrum is studied. The core objective of this thesis is to develop and study novel algorithms and models to address the challenges and problems introduced in different scenarios of the multichannel PLC. These problems can be categorized as the channel selection problem for MIMO transmission, the relay selection problem for the cooperative communication, and the spectrum assignment problem for the multiband transmission. The basis of all these problems is a decision making problem, which can greatly influence the performance of the system. To address these decision making problems, a powerful mathematical tool, namely the multi-armed bandit model, is used to model the different problems emerging in different scenarios of the multichannel PLC. This modeling approach is then used as a building block for developing machine learning algorithms in order to solve the aforementioned selection problems. Finally, novel machine learning algorithms are developed and their performances are analyzed and assessed. It is shown that the machine learning approach can considerably improve the performance of the multichannel PLC systems compared to the existing state of the art approaches, by enabling the selecting agent, i.e. the PLC transmitter, to perform intelligent decisions which improves the overall performance.Die Power-Line-Communication (PLC) ist die Technologie, bei der die Datensignale eines Kommunikationssystems über die Leiter einer Energieversorgungsinfrastruktur übertragen werden. Die einzigartige Umgebung der PLC-Kanäle stellt konkrete Herausforderungen und Anforderungen dar, die modelliert und analysiert werden müssen, um ein klares Verständnis des Kommunikationssystems zu erhalten und die Fähigkeit zur Verbesserung der Leistung und Zuverlässigkeit der Übertragung zu erreichen. Darüber hinaus ist in Kommunikationssystem die Nachfrage nach erhöhtem Datendurchsatz, sowie erhöhter Zuverlässigkeit und Robustheit der Übertragung von grundlegender Bedeutung. Um diesen Herausforderungen und Anforderungen gerecht zu werden, wird in dieser Arbeit das Konzept der Mehrkanal-PLC untersucht und weiterentwickelt. Die Mehrkanal-PLC wird in diesem Zusammenhang auf die Übertragung mehrerer informationstragenden Signale über den PLC-Kanal von einer Quelle zu einem Ziel bezogen. Wir untersuchen mehrere Szenarien der Mehrkanal-Datenübertragung, um die vielfältigen Anforderungen einer PLC-Übertragung zu behandeln. Eines der in dieser Arbeit besprochenen Mehrkanal-Szenarien ist die Multiple-Input-Multiple-Output-Übertragung (MIMO), bei der mehrere Datensignale über räumlich getrennte PLC-Kanäle übertragen werden. Ein weiteres Szenario, das in dieser Arbeit diskutiert wird, ist die kooperative Übertragung zwischen der Quelle und dem Ziel eines PLC-Systems mittels Zwischenrelais als Knoten im Netzwerk. Schließlich wird die Multiband-Übertragung unter Verwendung unterschiedlicher Teile des verfügbaren PLC-Spektrums untersucht. Das Kernziel dieser Arbeit ist es, neuartige Algorithmen und Modelle zu entwickeln und zu untersuchen, um die Herausforderungen und Probleme zu lösen, die in verschiedenen Szenarien der Mehrkanal-PLC existieren. Diese Probleme sind als das Kanalauswahlproblem für die MIMO-Übertragung, das Relaiauswahlproblem für die kooperative Kommunikation und das Spektrum-Zuweisungsproblem für die Multibandübertragung kategorisiert werden. Die Basis all dieser Probleme ist ein Entscheidungsproblem, das die Leistungsfähigkeit des Systems stark beeinflussen kann. Um diese Probleme lösen zu können, wird ein mathematisches Werkzeug, nämlich das mehrarmige Bandit-Modell, verwendet, um die verschiedenen Probleme zu modellieren, die sich in verschiedenen Szenarien der Mehrkanal-PLC ergeben. Dieser Modellierungsansatz wird als Baustein für die Entwicklung von maschinellen Lernalgorithmen verwendet, um die zuvor beschriebenen Auswahlprobleme zu lösen. Schließlich werden neuartige maschinelle Lernalgorithmen entwickelt und ihre Leistungen analysiert sowie bewertet. Es zeigt sich, dass der maschinelle Lernansatz die Leistungsfähigkeit der Mehrkanal-PLC-Systeme im Vergleich zu den bestehenden Ans\"atzen des Standes der Technik erheblich verbessern kann, indem es dem Auswahlagenten, d.h. dem PLC-Sender, ermöglicht, intelligente Entscheidungen durchzuführen, die die Gesamtleistung verbessern

Machine Learning Tips and Tricks for Power Line Communications

4openopenTonello A.M.; Letizia N.A.; Righini D.; Marcuzzi F.Tonello, A. M.; Letizia, N. A.; Righini, D.; Marcuzzi, F

Machine Learning and Data Mining Applications in Power Systems

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Design and optimisation of a low cost Cognitive Mesh Network

Wireless Mesh Networks (WMNs) have been touted as the most promising wireless technology in providing high-bandwidth Internet access to rural, remote and under-served areas, with relatively lower investment cost as compared to traditional access networks. WMNs structurally comprise of mesh routers and mesh clients. Furthermore, WMNs have an envisaged ability to provide a heterogeneous network system that integrates wireless technologies such as IEEE 802.22 WRAN, IEEE 802.16 WiMAX, IEEE 802.11 Wi-Fi, Blue-tooth etc. The recent proliferation of new devices on the market such as smart phones and, tablets, and the growing number of resource hungry applications has placed a serious strain on spectrum availability which gives rise to the spectrum scarcity problem. The spectrum scarcity problem essentially results in increased spectrum prices that hamper the growth and efficient performance of WMNs as well as subsequent transformation of WMN into the envisaged next generation networks. Recent developments in TV white space communications technology and the emergence of Cognitive radio devices that facilitate Dynamic Spectrum Access (DSA) have provided an opportunity to mitigate the spectrum scarcity problem. To solve the scarcity problem, this thesis reconsiders the classical Network Engineering (NE) and Traffic Engineering (TE) problems to objectively design a low cost Cognitive Mesh network that promotes efficient resources utilization and thereby achieve better Quality of Service (QoS) levels

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Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields

In dieser Arbeit werden spektral kodierte multispektrale Lichtfelder untersucht, wie sie von einer Lichtfeldkamera mit einem spektral kodierten Mikrolinsenarray aufgenommen werden. Für die Rekonstruktion der kodierten Lichtfelder werden zwei Methoden entwickelt, eine basierend auf den Prinzipien des Compressed Sensing sowie eine Deep Learning Methode. Anhand neuartiger synthetischer und realer Datensätze werden die vorgeschlagenen Rekonstruktionsansätze im Detail evaluiert