SON management based on weighted objectives and combined SON Function models

This demonstrator visualises a new paradigm for the management of Self-Organising Networks (SON), and the impact of SON management decisions on a heterogeneous mobile radio network. The main aspects shown thereby cover the operability of the system from a network operator perspective, and the effects of different SON implementation options on the Key Performance Indicators (KPIs) of the network. In terms of operability, the demonstrator shows that even a complex definition of target values for KPIs through the operator, which may be weighted against each other and have different values depending on the operational and network context, can be deployed to the implemented SON functions in a fully automated way. Changes to the target values, the context or the implemented SON functions lead to an automated adaptation of the SON system such that the KPI targets are achieved in the best possible way.

A Self-Organizing Network (SON) performs automated network management through the coordinated execution of autonomous functions, each aiming to achieve a specific objective like the optimization of a network Key Performance Indicator (KPI) value. However, there are situations in which a SON function cannot achieve its objective which can lead to disturbed SON operation and inferior performance. We present a SON Operational Troubleshooting (SONOT) SON function that is able to detect such problematic situations and trigger respective countermeasures. Thereby, it can exploit regular SON functions as probes in order to improve problem detection. Simulations show that the tight integration of the SONOT function with SON coordination provides means to automatically overcome the problems and improve overall network performance.KeywordsSelf-Organizing NetworkSON operationSON coordinationTroubleshooting

The management and operation of a Self-Organizing Network (SON)-enabled mobile network still requires considerable human effort. On the one hand, SON Functions need to be configured through low-level parameters in order to control the optimization of the network. On the other hand, an operator wants to steer the system with solely technical objectives, and the underlying network should be adapted accordingly. This opens up a gap in network management that is currently closed manually. This paper presents an approach that overcomes the manual gap between technical objectives and SON Functions by choosing the best values for the SON Functions' configurations automatically. Main advantage of this approach is that it allows to manage a system at a high level of abstraction and, at the same time, reduces manual effort. The approach is explained by applying it in a case study in the field of mobile networks with four SON Functions, namely Mobility Load Balancing (MLB), Coverage and Capacity Optimization (CCO), Energy Savings Management (ESM) and Mobility Robustness Optimization (MRO).

The Self-Organizing Network (SON) paradigm enables automated management of next generation mobile communication networks by introducing a set of autonomous functions that jointly achieve self-configuration, self-optimization, and self-healing. Each SON function is driven by a specific objective to optimize one or more Key Performance Indicators (KPIs). If a function encounters situations in which the given targets cannot be achieved, it can produce undesired behavior, resulting in an impaired SON. This paper presents the idea of SON Operational Troubleshooting function and outlines a concept for the automatic detection, analysis, and mitigation of operational problems.

In mobile networks, handover (HO) is one of the most important and complex KPIs (Key Performance Indicators), which directly affect to Quality of Service (QoS), Quality of Experience (QoE), and mobility performance. Moreover, its configuration parameters such as handover thresholds and handover neighbor lists are the key factors for implementing network optimization such as load balancing and energy saving. In a study before, the authors proposed clustering and forecasting models using ML algorithms and Time Series models to cluster, forecast, and manage the HO behavior of a huge number of cells. In this study, on the other hand, the authors firstly investigated more network KPIs to analyze their relationship with HO KPIs, and then, proposed new clustering, forecasting, and abnormal detection models that are expected to make them much more comprehensive. Finally, the performances of the proposed models were evaluated by applying them to a real dataset collected from the HO KPIs and other KPIs of more than 6000 cells of a real network during the years, 2016 and 2017. The results showed that the study was successful in identifying the relationship among network KPIs and significantly improving the performance of the HO clustering, forecasting, abnormal detection models. Moreover, the study also introduced the integration of emerging technologies such as machine learning (ML), big data, software-defined network (SDN), and network functions virtualization (NFV) to establish a practical and powerful computing platform for future self-organizing networks (SON).

It has been observed that in overlaid 3G/2G networks certain user equipments (UE) may stay camped on 2G for long periods despite the fact there is available 3G coverage. In this paper we present a framework to enable network operators to identify these occurences, not easily recognized using counter-based observability. The proposed methodology relies on the post processing of performance monitoring (PM) events from the radio access and core networks, feature provided by most major network vendors. Based on that, new key performance indicators (KPI) are defined e.g. the time UEs camp on 2G. Then, we show how these event-based KPIs may be used as inputs to self-optimization algorithms, functioning as a self-organizing networks (SON) enabler. Results are shown using data from live 3G/2G networks.

Policy Based SON Management (PBSM) is the process of orchestrating the deployed Self-Organized Network (SON) functions, so that the network responds as a whole to the operator objectives. This process is based on the configuration of the SON functions in order to steer their actions in the network towards certain operator objectives. The PBSM ensures an automated translation of these objectives into configurations of the SON functions. An approach has been recently proposed to empower the PBSM with cognitive capabilities (C-PBSM), using a Multi-Armed Bandit algorithm, namely the UCB1. The C-PBSM learns the optimal SON configurations based on network feedback. In this paper we propose an alternative approach, based on the LinUCB algorithm, that is able to learn the optimal SON configuration much faster than the previous approach. The speed of convergence is a critical factor that has to be thoroughly considered in the deployment of online learning processes. Results are shown using an LTE-A simulator that considers real-like network topology and parameters, and accurate ray tracing based propagation.

With deploying LTE (Long-Term Evolution) network operators are faced with managing up to three partly overlaying network deployments of different radio access technologies (RATs), for instance, GSM, UMTS and LTE. A concerted management of all three RATs requires self-organizing networks (SON) functionalities which have become an essential part of LTE. One prominent use case is mobility robustness optimization (MRO) and, recently, inter-RAT MRO has been discussed and specified in 3GPP as part of the Rel.11 SON framework. Hereto, a network deployment scenario was assumed where the existing legacy cellular network (for instance UMTS) provides full coverage, while LTE network deployment is limited to areas with high user penetration. Consequently, 3GPP focused on two inter-RAT failure cases, namely too late handover from LTE to 3G and too early handover from 3G to LTE, where the latter case refers to a handover triggered to LTE while the quality offered there is too poor to maintain the call. All other inter-RAT handover failure cases are not considered in Rel.11. In this paper, we investigate the performance of an inter-RAT MRO algorithm based on the Rel.11 capabilities with the reduced set of key performance indicators (KPIs) being applied to deployment scenario where 3G is not ubiquitous and LTE provides better coverage in some areas. The simulation results show that the algorithm with reduced KPI set performs very well for the considered limited LTE coverage scenario, but show expected deficiencies if deployment scenario changes.

Self-organizing systems are capable of executing tasks in complex and unknown domains that are unsuitable for humans. However, designing of self-organizing systems is challenging due to balancing global and local decision-making and in enabling the system to adaptively generate desired behaviors in different environments. To address these challenges, in this paper we propose a self-organizing system design framework based on stigmergy and bi-level planning. We use the multi-agent box-pushing problem as an example to verify the framework. Within this framework, the upper-level planning (which is used for box position prediction) and the lower-level planning (which is used for agent position allocation) are designed to connect and coordinate between the global and local decisions. The influence of states based on stigmergy creates a closed-loop control mechanism with “pheromones” as input, allowing the system to adapt to various environments. In this paper we use Webots as the simulation platform to conduct multi-agent box-pushing experiments and compare the results with an ant colony handling method, to demonstrate the effectiveness and advantage of our approach.

<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks. In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management. Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

Future wireless networks (LTE and beyond) will experience a continuous growth regarding the number of network elements with increasingly complex interrelations between the configuration of multiple network elements (NEs). A related trend is the seamless integration of multiple radio technologies into a single heterogeneous wireless network. Both developments increase network management complexity and require new management concepts with a very high degree of automation such as Self-Organizing Network (SON) concepts, which are currently discussed in the network operator (NGMN), research, and standardization (3GPP) communities. SON functions have to be coordinated and supervised in an automated way in order to enable a stable system operation with tight control over the system behavior by the network operator together with a high degree of automation. Based on a detailed analysis of the requirements for the coordination, a policy-based approach to realize the coordination-related decision making based on the network configuration and SON function context is presented. Results for two use cases (fully automatic hardware to site mapping and coverage & capacity optimization) are presented to show the applicability of the developed approach to diverse SON use cases.

The goal of our research and experiments is to find the definitions and values of key performance indicators (KPIs) in unstructured text. The direct access to opinions of customers served as a motivating factor for us to choose Twitter data for our experiments. For our case study, we have chosen the restaurant business domain. As in the other business domains, KPIs often serve as a solution for identification of current problems. Therefore, it is essential to learn which criteria are important to restaurant guests. The mission of our Proof-of-Concept KPI discovery tool presented in this paper is to facilitate the explorative analysis taking Twitter user posts as a data source. After processing tweets with Stanford CoreNLP toolkit, aggregated values are computed and presented as visual graphs. We see our tool as an instrument for data discovery applicable, for example, to define new qualitative and quantitative KPIs based on the values found in the graph. The graph represents a complete view of aggregated data that corresponds to the search results according to the user-defined keywords, and gives easy access to detailed data (tweets) that, in its turn, leads to better understanding of the post context and its emotional coloring.

Baggage handling at airports is among the most complex tasks for material handling: Baggage handling systems form a large-scale conveying network connecting multiple sources and sinks. Also, they are used in a very dynamic environment: Many airports grow rapidly on average but have to absorb significant variance in passenger numbers at the same time. Therefore, baggage-handling systems that are flexible regarding expansions or modifications are desirable. This can be realized by using a highly modular approach with each module containing its own control. This results in self-organized systems, which are also known as the Internet of Things in facility logistics. However, it is necessary to quantify resulting advantages in monetary terms to compare them with additional costs like necessary RFID tags. In this paper, we analyze a conventional and two possible implementations of self-organized baggage handling systems for a reference airport on the base of all relevant life-cycle costs. To account for the dynamic nature of expansion flexibility, we use a dynamic programming approach. The data for the reference scenario was carefully collected by evaluating public and non-public sources and is provided in this paper. Results suggest that self-organized systems can decrease life-cycle costs of typical baggage handling systems, and that savings exceed the costs of necessary RFID tags.

The article is devoted to the formation of key performance indicators (KPI) and customer satisfaction (CPI) for evaluating the implementation of strategies for the transformation of industrial complexes in the digital economy. The content of the KPI and CPI of the strategy for the transformation of the industrial complex as an integrated system of organizational, economic and sociological indicators is determined, allowing you to establish target quantitative values for the transformation and development of the most important elements, structures, processes and spheres of the enterprise, as well as to assess the degree of their achievement in operational and longterm periods. A methodology for the formation of KPI and CPI is proposed, a system and a scheme of mutual influence of these indicators are developed. The calculation procedure, data sources, and responsible performers for KPI and CPI are defined. Target values of KPI and CPI for industrial complexes from various regions of Russia have been developed. A general description of the mechanism for implementing KPI and CPI at enterprises is presented.

Tracing the Interrelationship between Key Performance Indicators and Production Cost using Bayesian Networks