Policy-based coordination and management of SON functions

<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>

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 deployment, operation and maintenance of complex cellular networks are managed autonomously by multiple concurrently executing Self-Organizing Network (SON) functions with dedicated objectives, that can often negatively impact the functioning of each other. It is essential to avoid the blinkered view to their individual targets and consider a holistic approach towards identifying the best possible coordination between them, in order to achieve desired overall network gains while ensuring stable and robust network operation. The designing of appropriate SON-coordination mechanisms is quite challenging as it requires comprehensive modelling of all the complementing and conflicting interactions among them. This article discusses the application of Machine Learning based online Recommender Systems to model the dynamics between SON functions. To evaluate its applicability, in this work, the focus is to jointly implement two intertwined SON functions - Inter Cell Interference Coordination (ICIC) and Coverage and Capacity Optimization (CCO), to implicitly handle their conflicts and achieve the desired trade-off between coverage and capacity by optimizing a joint objective. The proposed cooperative learning and distributed configuration enforcement based ICIC-CCO coordinated SON solution has been evaluated on a system-level LTE network simulator with varied traffic distributions. It has been observed that the outage situations in the network are significantly reduced while still achieving high Signal-to-Interference Ratios (SIRs), even with reduced transmit power settings on several occasions.

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

Over the last years the complexity of mobile communication networks has significantly increased. Therefore, Self-Organizing Network (SON) features have been introduced to automate the process of fault-remedying, configuring Network Elements (NEs), and optimizing their operation. Such features are typically implemented by SON functions which actively perform changes to Configuration Management (CM) parameters in order to achieve certain objectives. However, having such features also requires a mechanism that allows us to verify their actions. In case certain NEs experience an undesired behavior, we have to be able to determine whether it is caused by CM changes and, if so, identify the responsible ones for that to happen. In this paper we propose a novel CM change performance assessment method with dynamic scope management for automated CM undo decision making. Our method includes two key properties: the ability to determine the minimal set of cells that are possibly influenced by a certain CM change, and generate a recommendation to accept or reject it based on the observed network performance. Results from our real data evaluation show our method is able to detect anomalous network behavior and identify the responsible changes.

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 this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

The instrumentation of Self-Organising Network (SON) Functions in such way that they contribute to the operational objectives defined by a wireless network operator is a complex task. In order to enable automation of this configuration, we previously described the concept of a SON Objective Manager (SOM) which determines the best configuration based on conditional, ranked objectives and SON Function models describing the Key Performance Indicators (KPIs) optimised by some configuration. However, the simplicity of the models and the complexity of the design-time inference may limit the applicability of the initial concept. In this paper, we present an extended and enhanced run-time SOM with more expressive input models, namely, context-dependent, weighted operator objectives allowing a better trade-off and SON Function models defining the possible values of KPIs for some configuration. These improvements together allow for a broader application of the concept.

Edge intelligence in radio access network (RAN) is an emerging concept wherein machine learning (ML) driven self-organizing network (SON) functions reside in edge nodes or base stations (BSs). This allows to fulfill ultra-low latency, scalability, ultra-high reliability and signaling needs and requirements for beyond-5G applications. However, the resource-intensive hyper-parameter search of edge-distributed ML workloads is a major challenge that undermines the gains of ML driven automated networks. In current edge-ML, resource-constraints force the ML driven SON functions to incline towards inference-only workloads at the edge, whereas their training is done in the central infrastructure in non-collaborative fashion. This requires a redundant transfer of big data for training from the edge to the central location and a hyper-parameter search with high space time complexity, eventually resulting in high signaling costs and delays. This paper aims to address these challenges by presenting the Coordinated HypEr-paramETer seArcH (CHEETAH) frame-work for edge intelligence. It is a resource-efficient, low time-complexity distributed framework that eliminates the transfer of large training data from the edge to the central location and reduces the time required to find optimal (or near-optimal) hyper-parameters. Using a traffic forecasting use-case based on real network data, we demonstrate that as compared to state-of-the-art, the CHEETAH framework offers an attractive pragmatic solution in terms of predicting the best hyper-parameter configuration while keeping the associated costs low.

The evolution of wireless communications and networking technologies has led significantly expansion of the dimensionality of network resources, which compels innovations in resource management. Graphs, a classic discrete mathematical tool, have long been widely used for resource management thanks to their capabilities to model complex relationships and interactions among elements in wireless networks. Recently, resource management over graphs embraces various advanced approaches of graph optimization and graph learning, aligned with evolving demands in future wireless networks. To better learn recent research landscape and explore important trends, this two-part survey provides a comprehensive overview for resource management via graph optimization and learning. Part I presents the fundamentals of graph optimization and provides a recent literature review of graph optimization for resource management in various wireless communication scenarios, including cellular networks, device-to-device communications, multi-hop networks, multi-antenna systems, edge caching and computing, and non-terrestrial networks. Part II gives the basics of graph learning and provides a state-of-the-art literature review of graph learning in wireless networks for addressing various resource management issues, covering power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. A discussion of technical challenges and future research directions is covered in Part II.

Vertical handover supporting pervasive computing in future wireless networks

The smart radio environment has become an eye-catching topic and is believed to own great potential in future wireless networks. Its essential demand lies in sensing and manipulating the electromagnetic (EM) fields accurately and simultaneously with the same aperture, which is hard to achieve using conventional methods due to the difficulty in integrating different systems. We propose a millimeter-wave space–time-coding digital metasurface (STCM) to realize simultaneous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> direction finding and field manipulation. An analytic method of direction finding is first developed using the STCM modulated by a space–time-coding (STC) matrix while maintaining its original capability of EM field manipulations. In addition, three STC matrices corresponding to different EM functions are applied to the STCM to verify the feasibility and robustness of direction findings experimentally. Finally, a proof-of-concept prototype is built to exhibit different EM functions according to the incident angle detected simultaneously. The proposed concept sets the stage for the integrated implementation of the perception and response of the radio environment, which may find promising applications in future intelligent wireless networks and self-adaptive systems.

To accommodate big data, new technologies and features have started to be used in core network. Consequently, optical transport network has evolved towards Elastic Optical Network (EON). Despite the effort made towards efficient and reliable network management, interoperability problem in multi-vendor setting of the EON remains unsolved and it hinders the network management and monitoring. Hence, Network operators need to rely on the proprietary management solutions. This phenomenon poses complexity in network configuration, control, monitoring and management and also in turn results in higher Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) for the service providers. In this paper, standard data definition model of network elements of EON is presented for configuration, management and monitoring purpose. Standard YANG data models for the network elements, namely, Reconfigurable Optical Add/drop Multiplexer (ROADM), Bandwidth Variable Transponder (BVT) and Optical amplifier (OA) are presented. The implementation scenario for the developed data definition models are demonstrated for managing state of the art EON. It is contended that the adoption of these standard YANG data models within the NETCONF protocol not only facilitates the network configuration and management, but also improves the multi-vendor interoperability in the control and management plane of the EON.

In a "managed network" where the location / point of attachment of network elements (NE) are entirely planned and the NEs are tightly controlled by an element management system, the roll-out of new NEs or changes to the NE HW and SW cause considerable overhead. The overhead is due in large part due to the concurrent processes at the network operator, NE factory and roll-out field service which require extensive interaction. An autoconfiguration method is proposed which is able to decouple the individual roll-out processes, i.e., changes in the NE configuration and association of NE with certain locations can be changed at any time in the roll-out process without incurring additional overhead. This is achieved by an autoconfiguration support system under control of the operator where all data relevant to a to-be-rolled-out NE is stored. When the NE is booting it can retrieve the required configuration and SW which is needed to fulfil the required network function at its point of attachment to the network. Finally it is shown that in a managed network it is advantageous to use the geo-location of a network element as its key identifier in network planning and rollout, rather than a hardware identifier as usually employed in DHCP-based autoconfiguration. This is the case because in a "managed network" it is important that autoconfiguration support is provided for a certain network function at a certain location, rather than autoconfiguration of a certain hardware which is attached at an arbitrary location.

Even though mobile communication traffic continues to grow fast, its revenue does not increase sufficiently. Hence, mobile communication network operators are seeking technologies and strategies to maintain qualitative network services and reduce the Operational Expenditure (OpEx). Self Organization Network (SON) technology is one of the effective solutions to reduce OpEx. This paper presents the economic analysis of SON-enabled Mobile WiMAX. We define Network Propagation Models (NPM), introduce feasible SON use cases that can reduce the OpEx efficiently, select OpEx factors that can be reduced by deploying SON use cases, and propose mathematical expressions for revenue, CapEx, OpEx, CF, DCF and NPV. For the analysis, we consider a sample site and perform its cost and financial analysis through comparisons before and after SON deployment. As a result, 69% and 89% of total OpEx are decreased at newly added sites and traditional sites, respectively. Moreover, profits are increased earlier. Finally, SON technology can be achieved substantial OpEx reductions in network operational tasks.