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Optimization Methods for Heterogeneous Wireless Communication Networks: Planning, Configuration and Operation

Bahlke, Florian (2019):
Optimization Methods for Heterogeneous Wireless Communication Networks: Planning, Configuration and Operation.
Darmstadt, Technische Universität, [Online-Edition: https://tuprints.ulb.tu-darmstadt.de/8675],
[Ph.D. Thesis]

Abstract

With the fourth generation of wireless radio communication networks reaching maturity, the upcoming fifth generation (5G) is a major subject of current research. 5G networks are designed to achieve a multitude of performance gains and the ability to provide services dedicated to various application scenarios. These applications include those that require increased network throughput, low latency, high reliability and support for a very high number of connected devices. Since the achieved throughput on a single point-to-point transmission is already close to the theoretical optimum, more efforts need to be invested to enable further performance gains in 5G. Technology candidates for future wireless networks include using very large antenna arrays with hundreds of antenna elements or expanding the bandwidth used for transmission to the millimeter-wave spectrum. Both these and other envisioned approaches require significant changes to the network architecture and a high economic commitment from the network operator. An already well established technology for expanding the throughput of a wireless communication network is a densification of the cellular layout. This is achieved by supplementing the existing, usually high-power, macro cells with a larger number of low-power small cells, resulting in a so-called heterogeneous network (HetNet). This approach builds upon the existing network infrastructure and has been shown to support the aforementioned technologies requiring more sophisticated hardware. Network densification using small cells can therefore be considered a suitable bridging technology to path the way for 5G and subsequent generations of mobile communication networks. The most significant challenge associated with HetNets is that the densification is only beneficial for the overall network performance up to a certain density, and can be harmful beyond that point. The network throughput is limited by the additional interferences caused by the close proximity of cells, and the economic operability of the network is limited by the vastly increased energy consumption and hardware cost associated with dense cell deployment. This dissertation addresses the challenge of enabling reliable performance gains through network densification while guaranteeing quality-of-service conditions and economic operability. The proposed approach is to address the underlying problem vertically over multiple layers, which differ in the time horizon on which network optimization measures are initiated, necessary information is gathered, and an optimized solutions are found. These time horizons are classified as network planning phase, network configuration phase, and network operation phase. Optimization schemes are developed for optimizing the resource- and energy consumption that operate mostly in the network configuration phase. Since these approaches require a load-balanced network, schemes to achieve and maintain load balancing between cells are introduced for the network planning phase and operation phase, respectively. For the network planning phase, an approach is proposed for optimizing the locations of additional small cells in an existing wireless network architecture, and to schedule their activity phases in advance according to data demand forecasts. Optimizing the locations of multiple cells jointly is shown to be superior to deploying them one-by-one based on greedy heuristic approaches. Furthermore, the cell activity scheduling obtains the highest load balancing performance if the time-schedule and the durations of activity periods is jointly optimized, which is an approach originating from process engineering. Simulation results show that the load levels of overloaded cells can be effectively decreased in the network planning phase by choosing optimized deployment locations and cell activity periods. Operating the network with a high resource efficiency while ensuring quality-of-service constraints is addressed using resource optimization in the network configuration phase. An optimization problem to minimize the resource consumption of the network by operating multiple separated resource slices is designed. The originally problem, which is computationally intractable for large networks, is reformulated with a linear inner approximation, that is shown to achieve close to optimal performance. The interference is approximated with a dynamic model that achieves a closer approximation of the actual cell load than the static worst-case model established in comparable state-ot-the art approaches. In order to mitigate the increase in energy consumption associated with the increase in cell density, an energy minimization problem is proposed that jointly optimizes the transmit power and activity status of all cells in the network. An original problem formulation is designed and an inner approximation with better computational tractability is proposed. Energy consumption levels of a HetNet are simulated for multiple energy minimization approaches. The proposed method achieves lower energy consumption levels than approaches based on an exhaustive search over all cell activity configurations or heuristic power scaling. Additionally, in simulations, the likelihood of finding an energy minimized solution that satisfies quality-of-service constraints is shown to be significantly higher for the proposed approach. Finally, the problem of maintaining load balancing while the network is in operation is addressed with a decentralized scheme based on a learning system using multi-class support vector machines. Established methods often require significant information exchange between network entities and a centralized optimization of the network to achieve load balancing. In this dissertation, a decentralized learning system is proposed that globally balance the load levels close to the optimal solution while only requiring limited local information exchange.

Item Type: Ph.D. Thesis
Erschienen: 2019
Creators: Bahlke, Florian
Title: Optimization Methods for Heterogeneous Wireless Communication Networks: Planning, Configuration and Operation
Language: English
Abstract:

With the fourth generation of wireless radio communication networks reaching maturity, the upcoming fifth generation (5G) is a major subject of current research. 5G networks are designed to achieve a multitude of performance gains and the ability to provide services dedicated to various application scenarios. These applications include those that require increased network throughput, low latency, high reliability and support for a very high number of connected devices. Since the achieved throughput on a single point-to-point transmission is already close to the theoretical optimum, more efforts need to be invested to enable further performance gains in 5G. Technology candidates for future wireless networks include using very large antenna arrays with hundreds of antenna elements or expanding the bandwidth used for transmission to the millimeter-wave spectrum. Both these and other envisioned approaches require significant changes to the network architecture and a high economic commitment from the network operator. An already well established technology for expanding the throughput of a wireless communication network is a densification of the cellular layout. This is achieved by supplementing the existing, usually high-power, macro cells with a larger number of low-power small cells, resulting in a so-called heterogeneous network (HetNet). This approach builds upon the existing network infrastructure and has been shown to support the aforementioned technologies requiring more sophisticated hardware. Network densification using small cells can therefore be considered a suitable bridging technology to path the way for 5G and subsequent generations of mobile communication networks. The most significant challenge associated with HetNets is that the densification is only beneficial for the overall network performance up to a certain density, and can be harmful beyond that point. The network throughput is limited by the additional interferences caused by the close proximity of cells, and the economic operability of the network is limited by the vastly increased energy consumption and hardware cost associated with dense cell deployment. This dissertation addresses the challenge of enabling reliable performance gains through network densification while guaranteeing quality-of-service conditions and economic operability. The proposed approach is to address the underlying problem vertically over multiple layers, which differ in the time horizon on which network optimization measures are initiated, necessary information is gathered, and an optimized solutions are found. These time horizons are classified as network planning phase, network configuration phase, and network operation phase. Optimization schemes are developed for optimizing the resource- and energy consumption that operate mostly in the network configuration phase. Since these approaches require a load-balanced network, schemes to achieve and maintain load balancing between cells are introduced for the network planning phase and operation phase, respectively. For the network planning phase, an approach is proposed for optimizing the locations of additional small cells in an existing wireless network architecture, and to schedule their activity phases in advance according to data demand forecasts. Optimizing the locations of multiple cells jointly is shown to be superior to deploying them one-by-one based on greedy heuristic approaches. Furthermore, the cell activity scheduling obtains the highest load balancing performance if the time-schedule and the durations of activity periods is jointly optimized, which is an approach originating from process engineering. Simulation results show that the load levels of overloaded cells can be effectively decreased in the network planning phase by choosing optimized deployment locations and cell activity periods. Operating the network with a high resource efficiency while ensuring quality-of-service constraints is addressed using resource optimization in the network configuration phase. An optimization problem to minimize the resource consumption of the network by operating multiple separated resource slices is designed. The originally problem, which is computationally intractable for large networks, is reformulated with a linear inner approximation, that is shown to achieve close to optimal performance. The interference is approximated with a dynamic model that achieves a closer approximation of the actual cell load than the static worst-case model established in comparable state-ot-the art approaches. In order to mitigate the increase in energy consumption associated with the increase in cell density, an energy minimization problem is proposed that jointly optimizes the transmit power and activity status of all cells in the network. An original problem formulation is designed and an inner approximation with better computational tractability is proposed. Energy consumption levels of a HetNet are simulated for multiple energy minimization approaches. The proposed method achieves lower energy consumption levels than approaches based on an exhaustive search over all cell activity configurations or heuristic power scaling. Additionally, in simulations, the likelihood of finding an energy minimized solution that satisfies quality-of-service constraints is shown to be significantly higher for the proposed approach. Finally, the problem of maintaining load balancing while the network is in operation is addressed with a decentralized scheme based on a learning system using multi-class support vector machines. Established methods often require significant information exchange between network entities and a centralized optimization of the network to achieve load balancing. In this dissertation, a decentralized learning system is proposed that globally balance the load levels close to the optimal solution while only requiring limited local information exchange.

Place of Publication: Darmstadt
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communication Systems
Date Deposited: 12 May 2019 19:55
Official URL: https://tuprints.ulb.tu-darmstadt.de/8675
URN: urn:nbn:de:tuda-tuprints-86753
Referees: Pesavento, Prof. Marius and Jorswieck, Prof. Eduard
Refereed / Verteidigung / mdl. Prüfung: 30 January 2019
Alternative Abstract:
Alternative abstract Language
Die vierte Generation der Mobilkommunikationsnetze hat flächendeckende Verbreitung erreicht, und die kommende fünfte Generation (5G) bildet einen signifikanten Anteil der aktuellen Forschung. 5G Netzwerke sind darauf ausgelegt, in mehreren Aspekten höhere Leistung zu erreichen, und neuartige Services zu unterstützen. Je nach Anwendungsgebiet sind hierbei eine hohe Datenrate, geringe Latenz, hohe Zuverlässigkeit oder die Unterstützung einer sehr hohen Anzahl verbundener Geräte nötig. Da die erreichte Datenrate einer einfachen Punkt-zu-Punkt Verbindung bereits nahe an ihrem theoretischen Optimum liegt, müssen in 5G mehr Ressourcen aufgewendet werden um eine weitere Leistungssteigerung des Netzwerks zu erreichen. Mögliche Technologien für zukünftige Mobilkommunikationsnetze sind unter anderem die Nutzung von sehr großen Antennenarrays mit hunderten Antennenelementen oder eine Erweiterung des verwendeten Frequenzbandes in den Millimeterwellenbereich. Diese und andere Technologien verlangen signifikante Modifikationen der Netzwerkarchitektur, und damit hohe Investitionen des Netzwerkbetreibers. Eine bereits etablierte Technologie um die Leistungsfähigkeit eines Mobilkommunikationsnetzes zu erhöhen ist eine räumliche Verdichtung der Mobilfunkzellen. Dies wird erreicht indem die existierenden Zellen mit hoher Sendeleistung durch eine größere Zahl kleiner Zellen unterstützt werden, was in einem sogenannten "Heterogenen Netzwerk" (HetNet) resultiert. Dieser Ansatz erweitert die bereits existierende Architektur des Netzes und unterstützt die beschriebenen weiterführenden Technologien, welche komplexere Hardware benötigen. Heterogene Netze sind daher eine gute Übergangstechnologie für 5G und zukünftige Generationen von Mobilkommunikationsnetzen. Die signifikanteste Herausforderung von HetNets ist dass die Verdichtung des Netzwerks für dessen Leistungsfähigkeit nur bis zu einem bestimmten Level förderlich ist. Die erreichten Datenraten sind begrenzt durch die räumlich sehr nahen benachbarten Zellen, und der ökonomische Betrieb des Netzwerks wird eingeschränkt durch den hohen Energieverbrauch und Hardwarekosten, die durch eine große Anzahl an Zellen entstehen. Diese Dissertation behandelt die Herausforderung, durch eine Verdichtung des Netzwerks zuverlässige Leistungssteigerung zu erzielen und gleichzeitig die Servicequalität und den ökonomischen Betrieb sicherzustellen. Dieses grundlegende Problem wird auf mehreren Ebenen adressiert, die sich unterscheiden im Bezug auf den Zeithorizont in dem Maßnahmen zur Netzwerkoptimierung eingeleitet, die nötigen Informationen gesammelt, und die Optimierungen durchgeführt werden. Diese Zeithorizonte werden unterschieden in die Phasen der Planung, Konfiguration und Operation. Optimierungsverfahren für die Energie- und Ressourceneffizienz des Netzwerks werden hauptsächlich entwickelt für die Konfigurationsphase. Da ein Netzwerk mit gleichmäßiger Lastverteilung als Basis für weitere Optimierungen dient, werden für die Planungs- und Operationsphase Verfahren entwickelt um diese zu erreichen und dauerhaft sicherzustellen. Für die Planungsphase werden die Standorte neuer Zellen in einem existierenden Netzwerk optimiert, und die Aktivitätsphasen der Zellen geplant anhand der zu erwartenden Auslastung. Es wird gezeigt, dass eine gemeinsame Optimierung der Standorte mehrerer Zellen einer konsekutiven Aufstellung im Bezug auf die Lastverteilung des HetNets überlegen ist. Der Zeitplan für die Zellaktivität und die Länge der jeweiligen Zeitphasen werden gemeinsam optimiert. Durch dieses, aus der Verfahrenstechnik übernommene Konzept, erreicht die Planung der Aktivitätsphasen der Zellen die beste Lastverteilung. Simulationsergebnisse zeigen dass die Auslastung von überladenen Zellen effektiv verringert werden kann durch eine Optimierung der Aufstellungsorte und der Aktivität von Zellen. Der Betrieb des Netzwerkes mit hoher Ressourceneffizienz und unter Sicherstellung der Servicequalität wird erreicht durch eine Optimierung in der Konfigurationsphase. Es wird ein Optimierungsproblem entwickelt um den Ressourcenverbrauchs des Netzwerks zu optimieren mittels mehrerer Subnetze, die orthogonal zueinander mit unterschiedlichen Ressourcen operieren. Für dieses Problem, welches für größere Netzwerke sehr hohe Komplexität aufweist, wird eine lineare innere Approximation gebildet, welche fast optimale Ressourceneffizienz erreicht. Die Interferenzen werden während der Optimierung dynamisch modelliert, wodurch im Vergleich zu gängigen Verfahren die Auslastung von Zellen genauer approximiert werden kann. Um den höheren Energieverbrauch, welcher durch ein dichteres Netzwerk entsteht, zu verringern, wird die Sendeleistung und die Aktivität der Zellen im Netzwerk gleichzeitig optimiert. Für das sich ergebende Optimierungsproblem wird eine vereinfachte innere Approximation gebildet. Mehrere Verfahren zur Optimierung des Energieverbrauchs werden in einem simulierten HetNet getestet. Die entwickelte Methode erreicht einen niedrigeren Energieverbrauch als gängige, heuristische Verfahren, und findet in schwierigen Szenarien mit höherer Wahrscheinlichkeit eine Konfiguration für das Netzwerk, die alle Bedingungen an die Servicequalität erfüllt. Zuletzt wird das Problem adressiert, eine ausgeglichene Lastverteilung im Netzwerk während der Operationsphase zu erhalten. Ein Verfahren basierend auf einer Mehrklassen-Stützvektormethode wird genutzt um das Lastverteilungsproblem dezentral zu lösen. Etablierte Methoden basieren häufig auf umfangreicher Kommunikation zwischen Zellen um Optimierungsprobleme zentral zu lösen. Das entwickelte dezentrale Verfahren erreicht eine fast optimale Lastverteilung obwohl die durchgeführten Optimierungen von den Mobilfunkzellen und Nutzern nur mit lokal verfügbaren Informationen durchgeführt werden.German
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