Inter-RAT Mobility Robustness Optimization in Self-Organizing Networks

The massive growth in mobile data communication requires new more efficient Radio Access Technology (RAT) such as Long Term Evolution (LTE) being deployed on top of legacy mobile communication systems. Inter-RAT handovers are triggered either when the signal level of the serving RAT becomes weak while a sufficiently high signal level is measured from another RAT, or by traffic steering policies for balancing the load among different RATs, for example. Trouble-free operation of inter-RAT handovers requires an optimal setting of the handover parameters which is typically different for each cell and even location. Without knowing the detailed radio propagation conditions, directions and speeds of User Equipments (UEs), network planning can only provide a default setting which needs to be manually optimized during network operation with the aid of drive tests and expert knowledge. This manual optimization requires extensive human intervention which increases Operational Expenses (OPEX) of mobile operators and yields sub-optimal mobility performance due to limited means for more detailed root cause analysis. Therefore, automatic mechanisms have been requested by mobile operators to optimize the inter-RAT handover parameters. This optimization is known as inter-RAT Mobility Robustness Optimization (MRO) which is one of the use cases in Self-Organizing Network (SON). The technical complexities and requirements on MRO are too difficult to be tackled efficiently and properly by existing manual optimization methods. Considering that mobile networks consist of a high number of cells, the number of handover thresholds to be optimized in a network is significant. Moreover, the intricate dependencies and interactions among the handover thresholds of different neighboring cells make MRO problems even more challenging and complicated. Current optimization methods such as the local search method Simulated Annealing, for example, can be used offline in the network planning phase, however, they cannot be applied online in real-time networks to dynamically react on the changes in the environment and traffic. From that perspective, new optimization methods are needed to address the challenges and limitations imposed by MRO. In this thesis, several novel and feasible inter-RAT MRO methods have been proposed and analyzed. New key performance indicators which capture the different types of mobility failure events are proposed by the author of this thesis for the inter-RAT scenario. An inter-RAT handover is triggered by a dual-threshold measurement event where the first threshold corresponds to the serving cell and the second to the neighboring target cell of another RAT. This dual-threshold measurement event requires a more precise analysis of Too Late Handovers (TLHs). A TLH which is caused by the misconfigured serving cell threshold is distinguished from that which can be resolved by the target cell threshold. Thus, there are two types of TLHs in contrast to the intra-RAT case where a single type of TLH handover exists. Inter-RAT handover thresholds of currently standardized RATs are configured and optimized cell-specifically. That is, the same handover thresholds are applied by the UEs irrespective of the neighboring handover target cell. The limitations of a cell-specific optimization approach are analyzed and a new cell-group specific optimization approach where the handover thresholds are differentiated with respect to a group of neighboring target cells is proposed. For both cell-specific and cell-group specific optimization approaches, an automatic algorithm is developed to optimize the inter- RAT handover thresholds. In order to analyze the impact of Time-to-Trigger (TTT), which is a time interval affecting the triggering of handovers, the MRO algorithm is extended to allow a joint optimization of handover thresholds and TTT. Based on findings that even cell-group specific parameters cannot resolve all mobility failure events in some cells where radio conditions are not stationary along the cell border, a more advanced location-specific approach is proposed. Unlike cell-based optimization approaches, the handover thresholds are configured and optimized per cell-area and they can be differentiated with respect to neighboring target cells. Simulative investigations are carried out to evaluate the performance of the different optimization approaches. It has been shown that mobility failure events are rather located in specific cells. Accordingly, the same UEs are probably affected all the time by these mobility failures which leads to high user dissatisfaction. This clearly indicates the need of cell-specific handover thresholds to resolve the mobility problems in some cells. Moreover, it is shown that the optimization of target cell threshold in a cell-group specific manner yields an additional performance improvement compared to cell-specific optimization approach. The joint optimization approach of handover thresholds and TTT has shown advantages only when the handover thresholds are configured cell-specifically rather than cell-group specifically. The mobility failure events that are not resolved by cell-based optimization approaches are mitigated by cell-area based optimization approach. The investigations and concepts in this thesis have directly impacted 3rd Generation Partnership Project (3GPP) standard. Several contributions related to cell-specific and cell-group specific optimization approaches have been submitted and adopted by LTE Release (Rel.) 11 standard

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

LTE-advanced self-organizing network conflicts and coordination algorithms

Self-organizing network (SON) functions have been introduced in the LTE and LTEAdvanced standards by the Third Generation Partnership Project as an excellent solution that promises enormous improvements in network performance. However, the most challenging issue in implementing SON functions in reality is the identification of the best possible interactions among simultaneously operating and even conflicting SON functions in order to guarantee robust, stable, and desired network operation. In this direction, the first step is the comprehensive modeling of various types of conflicts among SON functions, not only to acquire a detailed view of the problem, but also to pave the way for designing appropriate Self-Coordination mechanisms among SON functions. In this article we present a comprehensive classification of SON function conflicts, which leads the way for designing suitable conflict resolution solutions among SON functions and implementing SON in reality. Identifying conflicting and interfering relations among autonomous network management functionalities is a tremendously complex task. We demonstrate how analysis of fundamental trade-offs among performance metrics can us to the identification of potential conflicts. Moreover, we present analytical models of these conflicts using reference signal received power plots in multi-cell environments, which help to dig into the complex relations among SON functions. We identify potential chain reactions among SON function conflicts that can affect the concurrent operation of multiple SON functions in reality. Finally, we propose a selfcoordination framework for conflict resolution among multiple SON functions in LTE/LTEAdvanced networks, while highlighting a number of future research challenges for conflict-free operation of SON

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing

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