The world is currently undergoing a new industrial revolution characterized by the digitization and automation of industry through the use of Information and Communication Technologies (ICTs). The construction sector is one of the largest sectors of the industry. Most of the tasks associated with this sector are carried out at worksites that are defined by their dynamism, decentralization, temporality, and the intervention of a large number of workers, subcontractors, machinery, equipment, and materials. These characteristics make this sector a great challenge for the implementation of ICTs. In this paper, the benefits of the use of the Fifth-Generation (5G) of mobile networks in the construction industry are presented. To that end, first, the digitization and automation needs of the sector are jointly analyzed, establishing different use cases and identifying the requirements of each one. Second, the main characteristics of 5G that address these use cases are identified. Third, a global framework for the application of 5G technology to the construction industry is proposed. Finally, an overview of some directions for future work are provided.

Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key features. However, estimating the end-user experience is not an easy task for network operators. The amount of end-user performance information operators can measure from the network is limited, complicating this approach. Here we explore the calculation of service metrics [known as key quality indicators (KQIs)] from classic low-layer measurements and parameters. We propose a complete machine-learning (ML) modeling framework. This system’s low-layer metrics can be applied to measure service-layer performance. To assess the approach, we implemented and evaluated the proposed system on a real cellular network testbed.

The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems.

Next-generation mobile communications networks will have to cope with an extraordinary amount and variety of network performance indicators, causing an increase in the storage needs of the network databases and the degradation of the management functions due to the high-dimensionality of every network observation. In this paper, different techniques for feature extraction are described and proposed as a means for reducing this high dimensionality, to be integrated as an intermediate stage between the monitoring of the network performance indicators and their usage in mobile networks’ management functions. Results using a dataset gathered from a live cellular network show the benefits of this approach, in terms both of storage savings and subsequent management function improvements.

5G communication systems are one of the major enabling technologies to meet the needs of Industry 4.0. This paper focuses on the use case of automated guided vehicles (AGVs) in an outdoor industrial scenario. To meet the communication requirements in these type of use cases, dual connectivity (DC) with resource aggregation in the uplink (UL) is generally proposed. However, uncontrolled use of DC schemes may negatively affect the network causing effects such as reduced network capacity, increased signaling, and increased interference. To overcome these issues, this paper proposes and evaluates the use of a proactive DC activation algorithm based on the instantaneous quality of service (QoS) and network conditions. The proposed algorithm has two phases, a first phase in which the QoS prediction is performed, and a second phase in which the DC activation decision is made. The performance evaluation of the algorithm has been carried out in two different scenarios: a single-frequency (SF) network and a dual-frequency (DF) network; and compared to two baselines. Our results show that our predictive DC algorithm is sufficiently robust and can offer benefits in terms of reduced signaling and increased UL performance, especially in scenarios with low to medium traffic load.

Proper management of failures in mobile communication networks is essential to provide quality services to users. This management consists of several tasks, being the first of them the detection of network failures. To carry out this task, key performance indicators (KPIs) that reflect the network state are analyzed. However, due to the different nature of these KPIs, the same detection method is not able to correctly find the anomalies in all of them. In addition, most of the techniques proposed at the moment, focus on the detection of certain types of anomalies. This paper proposes a framework for the detection of anomalies, capable of finding different types of anomalies in KPIs of different nature. This framework includes as well certain configuration parameters that allow to perform the detection based on the policies of network operators. As a result, the proposed framework indicates which of the anomalies found are actually KPI degradations as well as the start and end time of each degradation, and its percentage of degradation with respect to the normal behavior of the KPI.