Dynamic Capacity Enhancement using a Smart Antenna in Mobile Telecommunications Networks

This work describes an investigation into the performance of antennas for mobile base station applications and techniques for improving the coverage and capacity within a base station cell. The work starts by tracing the development of mobile systems, both in technical and commercial terms, from the earliest analogue systems to present day broadband systems and includes anticipated future developments. This is followed by an outline of how smart antenna systems can be utilised to improve cell coverage and capacity. A novel smart antenna system incorporating an array of slant ± 450 dual- polarised stacked patch elements four columns wide excited by a novel multi-beam forming and beam shaping network has been designed, simulated and implemented. It is found that for an ideal smart antenna array, four narrow overlapping beams, one wide “broadcast channel” beam and right and left shaped beams can be provided. Results are presented for the simulation of the smart antenna system using CST EM simulation software which inherently includes mutual coupling and the effects of a truncated ground plane on the element patterns. The results show some significant changes to the desired set of coverage patterns and various mutual coupling compensation techniques have been reviewed. An improved design technique has been developed for compensating the performance degrading effects of mutual coupling and finite ground plane dimensions in microstrip antenna arrays. The improved technique utilises combination of two previously known techniques: complex excitation weights compensation by inversion of the array mutual coupling scattering matrix and the incorporation of a WAIM (wide angle impedance matching) sheet. The technique has been applied to a novel multi-beam smart antenna array to demonstrate the efficacy of the technique by electromagnetic simulation. In addition, a demonstrator array has been constructed and tested which has yielded a positive conformation of the simulation results. For the developed demonstrator array which provides seven different beams, beams “footprints” have been predicted both for free space propagation and for urban propagation to evaluate the dynamic capacity performance of the smart antenna in a 3G mobile network. The results indicate that sector capacity can be dynamically tailored to user demand profiles by selection of the appropriate beam patterns provided by the novel smart antenna system

Studies on 6-sector-site deployment in downlink LTE

Mobile data traffic is expected to increase massively in the following years. Consequently, service operators are induced to increase the capacity of their networks continually to attract more subscribers and maximize their revenues. At the same time, they want to minimize operational costs and capital expenditures. Among the alternatives that aim to increase the network capacity, higher order sectorization, and in particular a six sectorized configuration, is nowadays attracting a lot of attention for LTE macro-cell deployments since a higher number of sectors per site results in improved site capacity and coverage. A six sectorized configuration is attractive for both roll-out phase and growth phase of the network. In the roll-out phase, the radio access network is planned with 6-sector sites instead of 3-sector sites with the advantage that less sites are needed for the same capacity and coverage requirements. In the growth phase, the six sectorized configuration can be used to upgrade existing 3-sector sites where the traffic grows beyond the current sites' capabilities. Therefore, no additional expensive and time consuming contracts need to be signed for the locations of the new sites, while the existing sites are used more efficiently. However, although potentially a 6-sector site can offer a double capacity than a 3-sector site, several factors prevent the capacity from growing proportionately to the number of sectors. Consequently, there is an uncertainty on whether the capacity gain is high enough to justify the extra costs of the additional equipment and, more specifically, whether the 6-sector-site deployment is more economically attractive than a 3-sector-site deployment. The aim of this report is to solve this uncertainty. First, we present the main factors that affect the capacity gain. Next, we quantify the impact of these factors on the capacity gain in downlink LTE with the use of a system level simulator. Finally, we use the results of the simulation study as inputs for an economic study to access the reasons for a possible deployment of 6-sector sites instead of 3-sector sites for LTE

Self-Organized Coverage and Capacity Optimization for Cellular Mobile Networks

Die zur Erfüllung der zu erwartenden Steigerungen übertragener Datenmengen notwendige größere Heterogenität und steigende Anzahl von Zellen werden in der Zukunft zu einer deutlich höheren Komplexität bei Planung und Optimierung von Funknetzen führen. Zusätzlich erfordern räumliche und zeitliche Änderungen der Lastverteilung eine dynamische Anpassung von Funkabdeckung und -kapazität (Coverage-Capacity-Optimization, CCO). Aktuelle Planungs- und Optimierungsverfahren sind hochgradig von menschlichem Einfluss abhängig, was sie zeitaufwändig und teuer macht. Aus diesen Grnden treffen Ansätze zur besseren Automatisierung des Netzwerkmanagements sowohl in der Industrie, als auch der Forschung auf groes Interesse.Selbstorganisationstechniken (SO) haben das Potential, viele der aktuell durch Menschen gesteuerten Abläufe zu automatisieren. Ihnen wird daher eine zentrale Rolle bei der Realisierung eines einfachen und effizienten Netzwerkmanagements zugeschrieben. Die vorliegende Arbeit befasst sich mit selbstorganisierter Optimierung von Abdeckung und Übertragungskapazität in Funkzellennetzwerken. Der Parameter der Wahl hierfür ist die Antennenneigung. Die zahlreichen vorhandenen Ansätze hierfür befassen sich mit dem Einsatz heuristischer Algorithmen in der Netzwerkplanung. Im Gegensatz dazu betrachtet diese Arbeit den verteilten Einsatz entsprechender Optimierungsverfahren in den betreffenden Netzwerkknoten. Durch diesen Ansatz können zentrale Fehlerquellen (Single Point of Failure) und Skalierbarkeitsprobleme in den kommenden heterogenen Netzwerken mit hoher Knotendichte vermieden werden.Diese Arbeit stellt einen "Fuzzy Q-Learning (FQL)"-basierten Ansatz vor, ein einfaches Maschinenlernverfahren mit einer effektiven Abstraktion kontinuierlicher Eingabeparameter. Das CCO-Problem wird als Multi-Agenten-Lernproblem modelliert, in dem jede Zelle versucht, ihre optimale Handlungsstrategie (d.h. die optimale Anpassung der Antennenneigung) zu lernen. Die entstehende Dynamik der Interaktion mehrerer Agenten macht die Fragestellung interessant. Die Arbeit betrachtet verschiedene Aspekte des Problems, wie beispielsweise den Unterschied zwischen egoistischen und kooperativen Lernverfahren, verteiltem und zentralisiertem Lernen, sowie die Auswirkungen einer gleichzeitigen Modifikation der Antennenneigung auf verschiedenen Knoten und deren Effekt auf die Lerneffizienz.Die Leistungsfähigkeit der betrachteten Verfahren wird mittels eine LTE-Systemsimulators evaluiert. Dabei werden sowohl gleichmäßig verteilte Zellen, als auch Zellen ungleicher Größe betrachtet. Die entwickelten Ansätze werden mit bekannten Lösungen aus der Literatur verglichen. Die Ergebnisse zeigen, dass die vorgeschlagenen Lösungen effektiv auf Änderungen im Netzwerk und der Umgebung reagieren können. Zellen stellen sich selbsttätig schnell auf Ausfälle und Inbetriebnahmen benachbarter Systeme ein und passen ihre Antennenneigung geeignet an um die Gesamtleistung des Netzes zu verbessern. Die vorgestellten Lernverfahren erreichen eine bis zu 30 Prozent verbesserte Leistung als bereits bekannte Ansätze. Die Verbesserungen steigen mit der Netzwerkgröße.The challenging task of cellular network planning and optimization will become more and more complex because of the expected heterogeneity and enormous number of cells required to meet the traffic demands of coming years. Moreover, the spatio-temporal variations in the traffic patterns of cellular networks require their coverage and capacity to be adapted dynamically. The current network planning and optimization procedures are highly manual, which makes them very time consuming and resource inefficient. For these reasons, there is a strong interest in industry and academics alike to enhance the degree of automation in network management. Especially, the idea of Self-Organization (SO) is seen as the key to simplified and efficient cellular network management by automating most of the current manual procedures. In this thesis, we study the self-organized coverage and capacity optimization of cellular mobile networks using antenna tilt adaptations. Although, this problem is widely studied in literature but most of the present work focuses on heuristic algorithms for network planning tool automation. In our study we want to minimize this reliance on these centralized tools and empower the network elements for their own optimization. This way we can avoid the single point of failure and scalability issues in the emerging heterogeneous and densely deployed networks.In this thesis, we focus on Fuzzy Q-Learning (FQL), a machine learning technique that provides a simple learning mechanism and an effective abstraction level for continuous domain variables. We model the coverage-capacity optimization as a multi-agent learning problem where each cell is trying to learn its optimal action policy i.e. the antenna tilt adjustments. The network dynamics and the behavior of multiple learning agents makes it a highly interesting problem. We look into different aspects of this problem like the effect of selfish learning vs. cooperative learning, distributed vs. centralized learning as well as the effect of simultaneous parallel antenna tilt adaptations by multiple agents and its effect on the learning efficiency.We evaluate the performance of the proposed learning schemes using a system level LTE simulator. We test our schemes in regular hexagonal cell deployment as well as in irregular cell deployment. We also compare our results to a relevant learning scheme from literature. The results show that the proposed learning schemes can effectively respond to the network and environmental dynamics in an autonomous way. The cells can quickly respond to the cell outages and deployments and can re-adjust their antenna tilts to improve the overall network performance. Additionally the proposed learning schemes can achieve up to 30 percent better performance than the available scheme from literature and these gains increases with the increasing network size

Dynamic capacity enhancement using a smart antenna in mobile telecommunications networks

This work describes an investigation into the performance of antennas for mobile base station applications and techniques for improving the coverage and capacity within a base station cell. The work starts by tracing the development of mobile systems, both in technical and commercial terms, from the earliest analogue systems to present day broadband systems and includes anticipated future developments. This is followed by an outline of how smart antenna systems can be utilised to improve cell coverage and capacity. A novel smart antenna system incorporating an array of slant ± 450 dual- polarised stacked patch elements four columns wide excited by a novel multi-beam forming and beam shaping network has been designed, simulated and implemented. It is found that for an ideal smart antenna array, four narrow overlapping beams, one wide “broadcast channel” beam and right and left shaped beams can be provided. Results are presented for the simulation of the smart antenna system using CST EM simulation software which inherently includes mutual coupling and the effects of a truncated ground plane on the element patterns. The results show some significant changes to the desired set of coverage patterns and various mutual coupling compensation techniques have been reviewed. An improved design technique has been developed for compensating the performance degrading effects of mutual coupling and finite ground plane dimensions in microstrip antenna arrays. The improved technique utilises combination of two previously known techniques: complex excitation weights compensation by inversion of the array mutual coupling scattering matrix and the incorporation of a WAIM (wide angle impedance matching) sheet. The technique has been applied to a novel multi-beam smart antenna array to demonstrate the efficacy of the technique by electromagnetic simulation. In addition, a demonstrator array has been constructed and tested which has yielded a positive conformation of the simulation results. For the developed demonstrator array which provides seven different beams, beams “footprints” have been predicted both for free space propagation and for urban propagation to evaluate the dynamic capacity performance of the smart antenna in a 3G mobile network. The results indicate that sector capacity can be dynamically tailored to user demand profiles by selection of the appropriate beam patterns provided by the novel smart antenna system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Cooperative control of relay based cellular networks

PhDThe increasing popularity of wireless communications and the higher data requirements of new types of service lead to higher demands on wireless networks. Relay based cellular networks have been seen as an effective way to meet users’ increased data rate requirements while still retaining the benefits of a cellular structure. However, maximizing the probability of providing service and spectrum efficiency are still major challenges for network operators and engineers because of the heterogeneous traffic demands, hard-to-predict user movements and complex traffic models. In a mobile network, load balancing is recognised as an efficient way to increase the utilization of limited frequency spectrum at reasonable costs. Cooperative control based on geographic load balancing is employed to provide flexibility for relay based cellular networks and to respond to changes in the environment. According to the potential capability of existing antenna systems, adaptive radio frequency domain control in the physical layer is explored to provide coverage at the right place at the right time. This thesis proposes several effective and efficient approaches to improve spectrum efficiency using network wide optimization to coordinate the coverage offered by different network components according to the antenna models and relay station capability. The approaches include tilting of antenna sectors, changing the power of omni-directional antennas, and changing the assignment of relay stations to different base stations. Experiments show that the proposed approaches offer significant improvements and robustness in heterogeneous traffic scenarios and when the propagation environment changes. The issue of predicting the consequence of cooperative decisions regarding antenna configurations when applied in a realistic environment is described, and a coverage prediction model is proposed. The consequences of applying changes to the antenna configuration on handovers are analysed in detail. The performance evaluations are based on a system level simulator in the context of Mobile WiMAX technology, but the concepts apply more generally

Area spectral efficiency of soft-decision space–time–frequency shift-keying-aided slow-frequency-hopping multiple access

Slow-frequency-hopping multiple access (SFHMA) can provide inherent frequency diversity and beneficially randomize the effects of cochannel interference. It may also be advantageously combined with our novel space-time–frequency shift keying (STFSK) scheme. The proposed system’s area spectral efficiency is investigated in various cellular frequency reuse structures. Furthermore, it is compared to both classic Gaussian minimum shift keying (GMSK)-aided SFHMA and GMSK-assisted time- division/frequency-division multiple access (TD/FDMA). The more sophisticated third-generation wideband code-division multiple access (WCDMA) and the fourth-generation Long Term Evolution (LTE) systems were also included in our comparisons. We demonstrate that the area spectral efficiency of the STFSK-aided SFHMA system is higher than the GMSK-aided SFHMA and TD/FDMA systems, as well as WCDMA, but it is only 60% of the LTE system

Traffic pattern prediction in cellular networks.

PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion. One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance. Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning. The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance