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

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach

Conflict resolution in mobile networks: a self-coordination framework based on non-dominated solutions and machine learning for data analytics [Application notes]

©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Self-organizing network (SON) is a well-known term used to describe an autonomous cellular network. SON functionalities aim at improving network operational tasks through the capability to configure, optimize and heal itself. However, as the deployment of independent SON functions increases, the number of dependencies between them also grows. This work proposes a tool for efficient conflict resolution based on network performance predictions. Unlike other state-of-theart solutions, the proposed self-coordination framework guarantees the right selection of network operation even if conflicting SON functions are running in parallel. This self-coordination is based on the history of network measurements, which helps to optimize conflicting objectives with low computational complexity. To do this, machine learning (ML) is used to build a predictive model, and then we solve the SON conflict by optimizing more than one objective function simultaneously. Without loss of generality, we present an analysis of how the proposed scheme provides a solution to deal with the potential conflicts between two of the most important SON functions in the context of mobility, namely mobility load balancing (MLB) and mobility robustness optimization (MRO), which require the updating of the same set of handover parameters. The proposed scheme allows fast performance evaluations when the optimization is running. This is done by shifting the complexity to the creation of a prediction model that uses historical data and that allows to anticipate the network performance. The simulation results demonstrate the ability of the proposed scheme to find a compromise among conflicting actions, and show it is possible to improve the overall system throughput.Peer ReviewedPostprint (author's final draft

Future Trends and Challenges for Mobile and Convergent Networks

Some traffic characteristics like real-time, location-based, and community-inspired, as well as the exponential increase on the data traffic in mobile networks, are challenging the academia and standardization communities to manage these networks in completely novel and intelligent ways, otherwise, current network infrastructures can not offer a connection service with an acceptable quality for both emergent traffic demand and application requisites. In this way, a very relevant research problem that needs to be addressed is how a heterogeneous wireless access infrastructure should be controlled to offer a network access with a proper level of quality for diverse flows ending at multi-mode devices in mobile scenarios. The current chapter reviews recent research and standardization work developed under the most used wireless access technologies and mobile access proposals. It comprehensively outlines the impact on the deployment of those technologies in future networking environments, not only on the network performance but also in how the most important requirements of several relevant players, such as, content providers, network operators, and users/terminals can be addressed. Finally, the chapter concludes referring the most notable aspects in how the environment of future networks are expected to evolve like technology convergence, service convergence, terminal convergence, market convergence, environmental awareness, energy-efficiency, self-organized and intelligent infrastructure, as well as the most important functional requisites to be addressed through that infrastructure such as flow mobility, data offloading, load balancing and vertical multihoming.Comment: In book 4G & Beyond: The Convergence of Networks, Devices and Services, Nova Science Publishers, 201

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-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