Context-Aware Self-Healing for Small Cell Networks

These can be an invaluable source of information for the management of the network, in a way that we have denominated as context-aware SON, which is the approach proposed in this thesis. To develop this concept, the thesis follows a top-down approach. Firstly, the characteristics of the cellular deployments are assessed, especially for indoor small cell networks. In those scenarios, the need for context-aware SON is evaluated and considered indispensable. Secondly, a new cellular architecture is defined to integrate both context information and SON mechanisms in the management plane of the mobile network. Thus, the specifics of making context an integral part of cellular OAM/SON are defined. Also, the real-world implementation of the architecture is proposed. Thirdly, from the established general SON architecture, a logical self-healing framework is defined to support the context-aware healing mechanisms to be developed. Fourthly, different self-healing algorithms are defined depending on the failures to be managed and the conditions of the considered scenario. The mechanisms are based on probabilistic analysis, making use of both context and network data for detection and diagnosis of cellular issues. The conditions for the implementation of these methods are assessed. Their applicability is evaluated by means of simulators and testbed trials. The results show important improvements in performance and capabilities in comparison to previous methods, demonstrating the relevance of the proposed approach.The last years have seen a continuous increase in the use of mobile communications. To cope with the growing traffic, recently deployed technologies have deepened the adoption of small cells (low powered base stations) to serve areas with high demand or coverage issues, where macrocells can be both unsuccessful or inefficient. Also, new cellular and non-cellular technologies (e.g. WiFi) coexist with legacy ones, including also multiple deployment schemes (macrocell, small cells), in what is known as heterogeneous networks (HetNets). Due to the huge complexity of HetNets, their operation, administration and management (OAM) became increasingly difficult. To overcome this, the NGMN Alliance and the 3GPP defined the Self-Organizing Network (SON) paradigm, aiming to automate the OAM procedures to reduce their costs and increase the resulting performance. One key focus of SON is the self-healing of the network, covering the automatic detection of problems, the diagnosis of their causes, their compensation and their recovery. Until recently, SON mechanisms have been solely based on the analysis of alarms and performance indicators. However, on the one hand, this approach has become very limited given the complexity of the scenarios, and particularly in indoor cellular environments. Here, the deployment of small cells, their coexistence with multiple telecommunications systems and the nature of those environments (in terms of propagation, coverage overlapping, fast demand changes and users' mobility) introduce many challenges for classic SON. On the other hand, modern user equipment (e.g. smartphones), equipped with powerful processors, sensors and applications, generate a huge amount of context information. Context refers to those variables not directly associated with the telecommunication service, but with the terminals and their environment. This includes the user's position, applications, social data, etc

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

Anomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required performance of the telecommunication services. This activity originally relied on direct human inspection of cellular metrics (counters, key performance indicators, etc.). Currently, degradation detection procedures have experienced an evolution towards the use of automatic mechanisms of statistical analysis and machine learning. However, pre-existent solutions typically rely on the manual definition of the values to be considered abnormal or on large sets of labeled data, highly reducing their performance in the presence of long-term trends in the metrics or previously unknown patterns of degradation. In this field, the present work proposes a novel application of transform-based analysis, using wavelet transform, for the detection and study of network degradations. The proposed system is tested using cell-level metrics obtained from a real-world LTE cellular network, showing its capabilities to detect and characterize anomalies of different patterns and in the presence of varied temporal trends. This is performed without the need for manually establishing normality thresholds and taking advantage of wavelet transform capabilities to separate the metrics in multiple time-frequency components. Our results show how direct statistical analysis of these components allows for a successful detection of anomalies beyond the capabilities of detection of previous methods.Optimi-EricssonJunta de AndaluciaEuropean Union (EU) 59288Proyecto de Investigacion de Excelencia P12-TIC-2905project IDADE-5G UMA18-FEDERJA-201European Union (EU) ICT-76080

REDUCCIÓN DEL COSTE COMPUTACIONAL DE LA INFERENCIA DE CONTEXTO BAYESIANA MEDIANTE EL USO DE RANGOS DE VALORES DINÁMICOS

This paper shows how to reduce evaluation time for context inference. Probabilistic Context Inference has proven to be more powerful and better adapted to the challenges of the physical reality with uncertain or missing information. As the inference complexity is very high, the complexity of the to be evaluated rule (representing a share of the real world) should be reduced as far as possible. Therefore we present an approach to select only relevant values of context types and to adapt this selection during its usage time. In an evaluation we show that with only a few evaluations of the reduced inference rules the reduction costs will have amortized and the system brings significant benefit to context aware computing

A Semi-Supervised Location-Aware Anomaly Detection Method for Ultra-Dense Indoor Scenarios.

Over the past few years, indoor cellular deployments have been on the rise. These scenarios are characterized by their user density and fast-changing conditions, thus, being prone to failures. Moreover, the steady development of indoor and outdoor positioning techniques is expected to provide a reliable source of information. Thus, the availability of user location is being considered to be a key enabler to improve the resilience and performance of automatic failure management and optimization techniques. Taking this into consideration, the present work proposes a semi-supervised location-aware anomaly detection method for the management of failures such as cell outages and interference problems.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Failure management insights in 5G using ns-3 network simulator

Failure management has been one of the most researched fields in cellular networks paradigm. Networks operators has experienced many problems on their deployments with each of the past generations. 5G networks aim high to encompass a wide variety of services, which means a large amount of resources on network management and failure resolution. The objective of the present work is to use the previous generation as base and provide, together with the updates on 3GPP specification, insights about what would be the problems that networks will handle. For this, they were identified and categorized some of these failures at the same time their effect on system performance was evaluated.This work was supported by the European Union’s Horizon 2020 research and innovation program under Grant no. 871249, project LOCUS. This work has been also funded by: Junta de Andalucía and ERDF: projects IDADE-5G (UMA18-FEDERJA-201) and OptiRAN5G (UMA18-FEDERJA-174), and postdoctoral grant (Ref., DOC 01154, “selección de personal investigador doctor convocado mediante Resolución de 21 de mayo de 2020”, PAIDI 2020); University of Malaga, through the I Plan Propio de Investigación, Transferencia y Divulgación Científica de la Universidad de Málaga. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Análisis del efecto del número de beams sobre un escenario 5G

5G has been presented as the most revolutionary generation in the mobile network paradigm. With regard to the RAN part, the main achieved improvements in comparison with its predecessor are based on the use of mmWaves. To overcome the high propagation losses that are inherent to mmWaves, beamforming scheme usage becomes essential. In this scope, the aim of this paper is to provide a first approach regarding the effect of the beamforming configuration in these radio networks. To do so, a complete scenario has been simulated in ns-3, enabling the evaluation of the signal-to-interference-plus- noise ratio (SINR) received by a UE under different number of configured beams.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

A Framework to boost the potential of networkin- a-box solutions

The expected heterogeneous connectivity provided by the fifth generation mobile network (5G) implies a huge revolution in the telecommunication field. Here, virtualisation and software implementation of network elements have been positioned as a key elements for this revolution. At the same time and as a consequence of the evolution of these two paradigms, network-in-a-box solutions have also emerged as a potential way in the deployment of networks, offering a portable infrastructure. Here, this work presents a framework for easing the management tasks of the network-in-a-box devices, allowing abstracting the hardware and software implementation of these kind of solutions. We provide an experimental validation of the framework through the deployment of a portable cellular network. Besides, a Cloud Gaming service is launched on this scenario, showing the versatility and strengths that the framework provides to these novel solutions.This work has been partially funded by “Ministerio de Asuntos Económicos y transformación digital” (red.es, “Piloto 5G Andalucía, Caso 31 OpenRAN”), by “Ministerio de Ciencia e Innovación” (grant FPU19/04468), and by Junta de Andalucía and European Regional Development Fund (ERDF) through AECMA-5G (UMA-CEIATECH-14) and post-doctoral grant (DOC01154, PAIDI 2020

5G in airports: challenges and use cases.

5G is the new generation of cellular communications that aims to provide high-throughput high-reliability connectivity to greatly diversified scenarios. With this objective, it shall act as a viable solution for environments as complex as an airport terminal, whose daily work cycle includes a wide range of diversified activities. As such, in this paper, 5G capacities are assessed, identifying those airport processes that can benefit from its application. From the proposed use cases, the monitoring of luggage trolleys is identified as a key use case that poses a problematic that is usually approached in a very inefficient way, due to the lack of information about the position and state of the trolleys. In this sense, a management system for the luggage trolleys using NarrowBand Internet of Things (NB-IoT) and Bluetooth Low Energy (BLE) is proposed.This work has been partially funded by: Junta de Andalucía and EDRF in the framework of 5G-SCARF: 5G Smart Communications for the AiRport of the Future (Ref. UMACEIATECH- 17) project, Ministerio de Asuntos Económicos y Transformación Digital and European Union – NextGenerationEU within the framework “Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia” under the project MAORI and Universidad de Málaga through the “II Plan Propio de Investigación, Transferencia y Divulgación Científica”. The authors are grateful to Aertec Solutions’ Airport Area for their support and collaboration in this work