Ericsson (Spain)

In this article we describe the QoS concept of the evolved packet system, which was standardized in 3GPP Release 8. The concept provides access network operators and service operators with a set of tools to enable service and subscriber differentiation. Such tools are becoming increasingly important as operators are moving from a single to a multi-service offering at the same time as both the number of mobile broadband subscribers and the traffic volume per subscriber is rapidly increasing. The "bearer" is a central element of the EPS QoS concept and is the level of granularity for bearer-level QoS control. The network-initiated QoS control paradigm specified in EPS is a set of signaling procedures for managing bearers and controlling their QoS assigned by the network. The EPS QoS concept is class-based, where each bearer is assigned one and only one QoS class identifier by the network. The QCI is a scalar that is used within the access network as a reference to node-specific parameters that control packet forwarding treatment. This class-based approach, together with the network-initiated QoS control paradigm, gives network operators full control over the QoS provided for its offered services for each of its subscriber groups.

A biometric identification system, based on the processing of the human iris by the dyadic wavelet transform, has been introduced. The procedure to obtain an iris signature of 256 bits has been described, as well as the feature extraction block and the verification system. The results have shown that the system can achieve high rates of security.

The increase in the size and complexity of current cellular networks is complicating their operation and maintenance tasks. While the end-to-end user experience in terms of throughput and latency has been significantly improved, cellular networks have also become more prone to failures. In this context, mobile operators start to concentrate their efforts on creating self-healing networks, i.e., those networks capable of troubleshooting in an automatic way, making the network more reliable and reducing costs. In this paper, an automatic diagnosis system based on unsupervised techniques for Long-Term Evolution (LTE) networks is proposed. In particular, this system is built through an iterative process, using self-organizing maps (SOMs) and Ward's hierarchical method, to guarantee the quality of the solution. Furthermore, to obtain a number of relevant clusters and label them properly from a technical point of view, an approach based on the analysis of the statistical behavior of each cluster is proposed. Moreover, with the aim of increasing the accuracy of the system, a novel adjustment process is presented. It intends to refine the diagnosis solution provided by the traditional SOM according to the so-called silhouette index and the most similar cause on the basis of the minimum Xth percentile of all distances. The effectiveness of the developed diagnosis system is validated using real and simulated LTE data by analyzing its performance and comparing it with reference mechanisms.

This article introduces the key innovations of the 5Growth service platform to empower vertical industries with an AI-driven automated 5G end-to-end slicing solution that allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, transportation, and energy), identify the key 5G requirements to enable them, and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control; (ii) an AI-driven closed-loop for automated service management with service level agreement assurance; and (iii) multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.

Adjusting antenna tilts is one of the most powerful techniques to solve coverage and capacity problems in cellular networks. In this paper, a novel self-tuning algorithm for adjusting antenna tilts in a Long-Term Evolution (LTE) system on a cell-by-cell basis is presented. The aim of the algorithm is to improve both network coverage and overall spectral efficiency, while eliminating cell overshooting problems. For this purpose, several new indicators are computed to detect insufficient cell coverage, cell overshooting, and abnormal cell overlapping from information available in connection traces. Algorithm assessment is carried out over a static system-level simulator implementing different real macrocellular scenarios. During the analysis, the proposed algorithm is compared with classical self-tuning algorithms that modify other base station parameters, such as transmission power or antenna tilt. Results show that the proposed algorithm can solve overshooting and overlapping problems more effectively than previous algorithms, regardless of the scenario.

In this letter, a novel algorithm for improving outer loop link adaptation (OLLA) convergence speed in the downlink of Long Term Evolution (LTE) is presented. The proposed heuristic algorithm adjusts the OLLA initial offset parameter based on OLLA adjustment histograms of large activity connections. The algorithm is validated with a connection-level simulator, fed with real connection traces collected from a live LTE network. Results show that average network block error rate, user throughput and spectral efficiency can be improved by properly adjusting the initial OLLA offset parameter.

This article addresses one of the main challenges related to the practical deployment of Internet of Things (IoT) solutions: the coordinated operation of entities at different infrastructures to support the automated orchestration of end-to-end Internet of Things services. This idea is referred to as "Internet of Things slicing" and is based on the network slicing concept already defined for the Fifth Generation (5G) of mobile networks. In this context, we present the architectural design of a slice orchestrator addressing the aforementioned challenge, based on well-known standard technologies and protocols. The proposed solution is able to integrate existing technologies, like cloud computing, with other more recent technologies like edge computing and network slicing. In addition, a functional prototype of the proposed orchestrator has been implemented, using open-source software and microservice platforms. As a first step to prove the practical feasibility of our solution, the implementation of the orchestrator considers cloud and edge domains. The validation results obtained from the prototype prove the feasibility of the solution from a functional perspective, verifying its capacity to deploy Internet of Things related functions even on resource constrained platforms. This approach enables new application models where these Internet of Things related functions can be onboarded on small unmanned aerial vehicles, offering a flexible and cost-effective solution to deploy these functions at the network edge. In addition, this proposal can also be used on commercial cloud platforms, like the Google Compute Engine, showing that it can take advantage of the benefits of edge and cloud computing respectively.

This letter presents a novel cell outage detection algorithm based on incoming handovers statistics. The main advantage of the proposed algorithm is that it uses neighbor measurements that allow to detect outage in two cases. First, when the cell in outage is able to report performance indicators; second, when these indicators are not available because the base station is affected. To evaluate the proposed algorithm and compare it with other approaches, a set of tests has been carried out using an LTE simulator and in a live LTE network.

Self-healing networks aim to detect cells with service degradation, identify the fault cause of their problem, and execute compensation and repair actions. The development of this type of automatic system presents several challenges to be confronted. The first challenge is the scarce number of historically reported faults, which greatly complicates the evaluation of novel self-healing techniques. For this reason, in this paper, a system model to simulate faults in Long-Term Evolution (LTE) networks, along with their most significant key performance indicators, is proposed. Second, the expert knowledge required to build a self-healing system is usually not documented. Therefore, in this paper, a methodology to extract this information from a collection of reported cases is proposed. Finally, following the proposed methodology, an automatic fuzzy-logic-based system for fault identification in LTE networks is designed. Evaluation results show that the fuzzy system provides fault identification with a high success rate.

Policy and charging control provides operators with advanced tools for service-aware QoS and charging control. PCC for the evolved packet system, defined as part of the 3 GPP Release 8 specifications, has evolved significantly from previous releases to support multiple-access technologies, roaming, and mobility. Within the PCC framework, a number of protocols have been specified to implement these functions. This article describes key PCC concepts and explains additional amendments to support PCC in the EPS.

Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.

In this letter, a comprehensive analysis of throughput performance statistics in a live LTE network is presented. The analysis shows the relationship between several widely accepted throughput performance indicators, i.e., the user throughput, the cell throughput, and the radio link throughput, and how these indicators are related to signal quality statistics. The analysis is performed on a per-cell and per-connection basis. For this purpose, throughput and signal quality statistics are collected from network performance counters and call traces in cells of a live LTE system. Results show that all throughput measures are strongly affected by chatty applications dominating current LTE networks due to the last transmission time interval transmissions and the outer loop link adaptation mechanism.

The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks.

We present a practical approach of how deep learning models can improve 5G network service. We demonstrate the potential of a deep Q-network agent applied to a traffic management problem, consisting in the path selection in a multi-path scenario. We use for the demonstration a multi-path QUIC implementation and we train an agent for improving the algorithm that selects the optimal path, with results in a better utilization of the network by increasing the aggregated throughput of the multi-path flows, as we detail in the results of this work.

The challenge before reaching the production stage for 5G is to assess its performance in large-scale facilities. EU-funded 5G PPP project 5G EVE is addressing this challenge by building a distributed and interworking 5G end-to-end facility in Europe across various sites. In this paper we explain the architecture of the 5G EVE end-to-end facility and present each site with their respective features, including the interworking among them, which provides a clear add-on to country-based trials. The 5G EVE site facilities are designed to offer automated network slice deployment tools and a new validation framework. This framework will offer tools for testing 5G radio and various core solutions. It will also allow experimenting and benchmarking different classes of end-to-end network slices and services. These services are defined by a set of selected vertical use cases in sectors like energy, transport, smart cities, tourism, and manufacturing (Industry 4.0). 5G EVE's large-scale trials are being deployed in four European countries: Italy, France, Spain, and Greece.

By 2020, mobile networks will support a wide range of communication services while at the same time, the number of user terminals will be enormous. To cope with such increased complexity in network management, innovative solutions for the next generation of self-organizing networks (SONs) need to be deployed. In the field of self-healing, the heterogeneous character of future fifth-generation (5G) radio access networks (RANs) will provide a diversity of measurements and metrics that can be translated into valuable knowledge to support detection and diagnosis activities. The more complete the information gathered, the better the SON mechanisms will be able to effectively analyze and solve radio problems. However, temporal dependence between metrics has not been previously addressed in the literature. In this paper, a self-healing method based on network data analysis is proposed to diagnose problems in future RANs. The proposed system analyzes the temporal evolution of a plurality of metrics and searches for potential interdependence under the presence of faults. Performance is evaluated with real data from a mature Long-Term Evolution (LTE) network. Results show that the proposed method exploits the available data in the context of heterogeneous scenarios, reducing the diagnosis error rate.

The current trend in the management of mobile communication networks is to increase the level of automation in order to enhance network performance while reducing Operational Expenditure (OPEX). In this context, the 3rd Generation Partnership Project (3GPP) has presented different solutions. On the one hand, Self-Organizing Networks (SON) include self-healing capabilities, which allow operators to automate their troubleshooting tasks in order to identify and solve the problems of the network. On the other hand, the use of mobile traces or Minimization of Drive Tests (MDT) are proposed to automate the collection of user's measurements and signalling messages. This paper proposes to combine both solutions, SON and traces, with the purpose of quickly detecting and solving issues related to the radio interface. That is, the user information gathered by the cell traces function is used to perform an automatic diagnosis of the RF condition of each cell. In addition, the proposed approach allows to precisely locate RF problems based on the assessment of the RF condition. Mobile traces constitute large sets of data, whose analysis requires the application of big-data analytics techniques. The proposed system has been evaluated in two different live LTE networks, demonstrating its validity and utility.

The challenging traits of 5G networks to support novel and diverse business requirements of vertical sectors have rendered current network security approaches impotent. To address various security requirements of 5G networks and services, a holistic and robust security architecture mindful of 5G technical and business features becomes vital. This paper describes a holistic security architecture for a multi-tenant NFV/SDN enabled 5G access network based on policy-based security management and monitoring & smart analytics.

The increasing amount of network elements in the current deployments of cellular networks is leading to an enormous complexity of the operation and maintenance. Self-organizing networks (SONs) is a good solution for operators to save operational expenditures by automating network management. One of the key challenges in this context is the automatic identification of degraded cells. In this letter, a method to detect degraded cells through the analysis of the time evolution of metrics is proposed. Results show that cell faulty patterns can be effectively detected by comparing them with a set of fictitious degraded patterns.

5G is playing a paramount role in the digital transformation of the industrial sector, offering high-bandwidth, reliable, and low-latency wireless connectivity to meet the stringent and critical performance requirements of manufacturing processes. This work analyzes the applicability of 5G technologies as key enablers to support, enhance, and even enable novel advances in Industry 4.0. It proposes a complete 5G solution for two real-world Industry 4.0 use cases related to metrology and quality control. This solution uses 5Growth to ease and automate the management of vertical services over a soft-ware-defined network and network function virtualization based 5G mobile transport and computing infrastructure, and to aid the integration of the verticals' private 5G network with the public network. Finally, a validation campaign assesses the applicability of the proposed solution to support the performance requirements (especially latency and user data rate) of the selected use cases, and evaluates its efficiency regarding vertical service setup time across different domains in less than three minutes.