Traffic Hotspot localization in 3G and 4G wireless networks using OMC metrics

In recent years, there has been an increasing awareness to traffic localization techniques driven by the emergence of heterogeneous networks (HetNet) with small cells deployment and the green networks. The localization of hotspot data traffic with a very high accuracy is indeed of great interest to know where the small cells should be deployed and how can be managed for sleep mode concept. In this paper, we propose a new traffic localization technique based on the combination of different key performance indicators (KPI) extracted from the operation and maintenance center (OMC). The proposed localization algorithm is composed with five main steps; each one corresponds to the determination of traffic weight per area using only one KPI. These KPIs are Timing Advance (TA), Angle of Arrival (AoA), Neighbor cell level, the load of each cell and the Harmonic mean throughput (HMT) versus the Arithmetic mean throughput (AMT). The five KPIs are finally combined by a function taking as variables the values computed from the five steps. By mixing such KPIs, we show that it is possible to lessen significantly the errors of localization in a high precision attaining small cell dimensions.Comment: 7 pages, 7 figures, published in Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2014 (PIMRC); IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2014 (PIMRC

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

Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks

Nutzer der Mobilfunknetze wünschen und fordern eine Steigerung des Datendurchsatzes, die zur Erhöhung der Netzlast führt. Besonders seit der Einführung von LTE erhöht sich daher die Anzahl und Dichte der Zellen in Mobilfunknetzen. Dies führt zusätzlich zur Zunahme der Investitions- und Betriebskosten, sowie einer höheren Komplexität des Nerzbetriebs. Der Einsatz selbstorganisierter Netze (SONs) wird vorgeschlagen, um diese drei Herausforderungen zu bewältigen. Einige SON-Funktionen (SF) wurden sowohl von Seiten der Netzbetreiber als auch von den Standardisierungsgremien vorgeschlagen. Eine SF repräsentiert hierbei eine Netzfunktion, die automatisiert werden kann. Ein Beispiel ist die Optimierung der Robustheit des Netzes (Mobility Robustness Optimization, MRO) oder der Lastausgleich zwischen Funkzellen (Mobility Load Balancing, MLB). Die unterschiedlichen SON-Funktionen werden innerhalb eines Mobilfunknetzes eingesetzt, wobei sie dabei häufig gleiche oder voneinander abhängige Parameter optimieren. Zwangsläufig treten daher beim Einsatz paralleler SON-Funktionen Konflikte auf, die Mechanismen erfordern, um diese Konflikte aufzulösen oder zu minimieren. In dieser Dissertation werden Lösungen aufgezeigt und untersucht, um die Koordination der SON-Funktionen zu automatisieren und, soweit möglich, gleichmä{\ss}ig zu verteilen. Im ersten Teil werden grundsätzliche Entwürfe für SFs evaluiert, um die SON-Koordination zu vereinfachen. Basierend auf der Beobachtung, dass die Steurung der SON-Funktion sich ähnlich dem generischen Q-Learning Problem verhält, werden die SFs als Q-Learning-Agenten entworfen. Dieser Ansatz wurde mit sehr positiven Ergebnissen auf zwei SFs (MRO und MLB) angewandt. Die als Q-Learning-Agenten entworfenen SFs werden für zwei unterschiedliche Ansätze der SON-Koordination evaluiert. Beide Koordinierungsansätze betrachten dabei die SON-Umgebung als ein Multi-Agenten-System. Der erste Ansatz basierend auf einer räumlich-zeitlichen Entkoppelung separiert die Ausführung von SF-Instanzen sowohl räumlich als auch zeitlich, um die Konflikte zwischen den SF-Instanzen zu minimieren. Der zweite Ansatz wendet kooperatives Lernen in Multi-Agenten-Systemen als automatisierten Lösungsansatz zur SON-Koordination an. Die einzelnen SF-Instanzen lernen anhand von Utility-Werten, die sowohl die eigenen Metriken als auch die Metriken der Peer-SF-Instanzen auswerten. Die Intention dabei ist, durch die erlernte Zustands-Aktions-Strategie Aktionen auszuführen, die das beste Resultat für die aktive SF, aber auch die geringste Auswirkung auf Peer-SFs gewährleisten. In der Evaluation des MRO-MLB-Konflikts zeigten beide Koordinierungsansätze sehr gute Resultate.Owing to increase in desired user throughput and to the subsequent increase in network traffic, the number and density of cells in cellular networks have increased, especially starting with LTE. This directly translates into higher capital and operational expenses as well as increased complexity of network operation. To counter all three challenges, Self-Organized Networks (SON) have been proposed. A number of SON Functions (SFs) have been defined both from the network operator community as well as from the standardization bodies. In this respect, a SF represents a network function that can be automated e.g. Mobility Robustness Optimization (MRO) or Mobility Load balancing (MLB). The different SFs operate on the same radio network, in many cases adjusting the same or related parameters. Conflicts are as such bound to occur during the parallel operation of such SFs and mechanisms are required to resolve or minimize the conflicts. This thesis studies the solutions through which SON functions can be coordinated in an automated and preferably distributed manner. In the first part we evaluate the design principles of SFs that aim at easing the coordination. With the observation that the SON control loop is similar to a generic Q-learning problem, we propose designing SFs as Q-learning agents. This framework is applied to two SFs (MRO and MLB) with very positive results. Given the designed QL based SFs, we then evaluate two SON coordination approaches that consider the SON environment as a Multi-Agent System (MAS). The first approach based on Spatial-Temporal Decoupling (STD) separates the execution of SF instances in space and time so as to minimize the conflicts among instances. The second approach applies multi-agent cooperative learning for an automated solution towards SON coordination. In this case individual SF instances learn based on utilities that aggregate their own metrics as well as the metrics of peer SF instances. The intention in this case is to ensure that the learned state-action policy functions apply actions that guarantee the best result for the active SF but also have the least effect on the peer SFs. Both coordination approaches have been evaluated with very positive results in simulations that consider the MRO - MLB conflict

Self organization in 3GPP long term evolution networks

Mobiele en breedbandige internettoegang is realiteit. De internetgeneratie vindt het immers normaal om overal breedbandige internettoegang te hebben. Vandaag zijn er al 5,9 miljard mobiele abonnees ( 87% van de wereldbevolking) en 20% daarvan hebben toegang tot een mobiele breedbandige internetverbinding. Dit wordt aangeboden door 3G (derde generatie) technologieën zoals HSPA (High Speed Packet Access) en 4G (vierde generatie) technologieën zoals LTE (Long Term Evolution). De vraag naar hoogkwalitatieve diensten stelt de mobiele netwerkoperatoren en de verkopers van telecommunicatieapparatuur voor nieuwe uitdagingen: zij moeten nieuwe oplossingen vinden om hun diensten steeds sneller en met een hogere kwaliteit aan te bieden. De nieuwe LTE-standaard brengt niet alleen hogere pieksnelheden en kleinere vertragingen. Het heeft daarnaast ook nieuwe functionaliteiten in petto die zeer aantrekkelijk zijn voor de mobiele netwerkoperator: de integratie van zelfregelende functies die kunnen ingezet worden bij de planning van het netwerk, het uitrollen van een netwerk en het controleren van allerhande netwerkmechanismen (o.a. handover, spreiding van de belasting over de cellen). Dit proefschrift optimaliseert enkele van deze zelfregelende functies waardoor de optimalisatie van een mobiel netwerk snel en automatisch kan gebeuren. Hierdoor verwacht men lagere kosten voor de mobiele operator en een hogere kwaliteit van de aangeboden diensten

Self-Organizing Networks use cases in commercial deployments

These measurements can be obtained from different sources, but these sources are either expensive or not applicable to any network. To solve this problem, this thesis proposes a method that uses information available in any network so that the calibration of predictive maps is converted into universal without losing accuracy with respect to current methods. Furthermore, the complexity of today's networks makes them prone to failure. To save costs, operators employ network self-healing techniques so that networks are able to self-diagnose and even self-fix when possible. Among the various failures that can occur in mobile communication networks, a common case is the existence of sectors whose radiated signal has been exchanged. This issue appears during the network roll-out when engineers accidentally cross feeders of several antennas. Currently, manual methodology is used to identify this problem. Therefore, this thesis presents an automatic system to detect these cases. Finally, special attention has been paid to the computational efficiency of the algorithms developed in this thesis since they have finally been integrated into commercial tools.Ince their origins, mobile communication networks have undergone major changes imposed by the need for networks to adapt to user demand. To do this, networks have had to increase in complexity. In turn, complexity has made networks increasingly difficult to design and maintain. To mitigate the impact of network complexity, the concept of self-organizing networks (SON) emerged. Self-organized networks aim at reducing the complexity in the design and maintenance of mobile communication networks by automating processes. Thus, three major blocks in the automation of networks are identified: self-configuration, self-optimization and self-healing. This thesis contributes to the state of the art of self-organized networks through the identification and subsequent resolution of a problem in each of the three blocks into which they are divided. With the advent of 5G networks and the speeds they promise to deliver to users, new use cases have emerged. One of these use cases is known as Fixed Wireless Access. In this type of network, the last mile of fiber is replaced by broadband radio access of mobile technologies. Until now, regarding self-configuration, greenfield design methodologies for wireless networks based on mobile communication technologies are based on the premise that users have mobility characteristics. However, in fixed wireless access networks, the antennas of the users are in fixed locations. Therefore, this thesis proposes a novel methodology for finding the optimal locations were to deploy network equipment as well as the configuration of their radio parameters in Fixed Wireless Access networks. Regarding self-optimization of networks, current algorithms make use of signal maps of the cells in the network so that the changes that these maps would experience after modifying any network parameter can be estimated. In order to obtain these maps, operators use predictive models calibrated through real network measurements

Mobility Analysis and Management for Heterogeneous Networks

The global mobile data traffic has increased tremendously in the last decade due to the technological advancement in smartphones. Their endless usage and bandwidth-intensive applications will saturate current 4G technologies and has motivated the need for concrete research in order to sustain the mounting data traffic demand. In this regard, the network densification has shown to be a promising direction to cope with the capacity demands in future 5G wireless networks. The basic idea is to deploy several low power radio access nodes called small cells closer to the users on the existing large radio foot print of macrocells, and this constitutes a heterogeneous network (HetNet). However, there are many challenges that operators face with the dense HetNet deployment. The mobility management becomes a challenging task due to triggering of frequent handovers when a user moves across the network coverage areas. When there are fewer users associated in certain small cells, this can lead to significant increase in the energy consumption. Intelligently switching them to low energy consumption modes or turning them off without seriously degrading user performance is desirable in order to improve the energy savings in HetNets. This dynamic power level switching in the small cells, however, may cause unnecessary handovers, and it becomes important to ensure energy savings without compromising handover performance. Finally, it is important to evaluate mobility management schemes in real network deployments, in order to find any problems affecting the quality of service (QoS) of the users. The research presented in this dissertation aims to address these challenges. First, to tackle the mobility management issue, we develop a closed form, analytical model to study the handover and ping-pong performance as a function of network parameters in the small cells, and verify its performance using simulations. Secondly, we incorporate fuzzy logic based game-theoretic framework to address and examine the energy efficiency improvements in HetNets. In addition, we design fuzzy inference rules for handover decisions and target base station selection is performed through a fuzzy ranking technique in order to enhance the mobility robustness, while also considering energy/spectral efficiency. Finally, we evaluate the mobility performance by carrying out drive test in an existing 4G long term evolution (LTE) network deployment using software defined radios (SDR). This helps to obtain network quality information in order to find any problems affecting the QoS of the users

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods