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.

Multi-connectivity (MC) is one of the most important features to be introduced in 5G networks, allowing User Equipment (UE) to simultaneously aggregate radio resources from several network nodes to enhance both data rates and reliability. Thus, this feature enables a further flexibility in the allocation of resources to the UEs in order to fulfil the users’ requirements in more complex 5G scenarios. This paper takes advantage of this wide flexibility to present a traffic steering approach that determines the amount of traffic to be held by each of the serving nodes in a multi-connectivity scenario. In this sense, the proposed technique is based on network and UE performance metrics in order to maximize the users’ perceived quality of experience (QoE) for enhanced Mobile Broadband (eMBB) services. It is then compared with a homogeneous traffic split among the serving nodes, with a single-connectivity approach and with state-of-the-art solutions. The benefits are analysed in terms of throughput and Mean Opinion Score (MOS), which is the main QoE metric. The analysis shows that a noticeable UE throughput improvement is reached when the proposed traffic steering method is applied. Consequently, this enhancement is noticed in the users’ QoE, which can lead to a reduction of operating expenses (OPEX) of the network.

Due to the great complexity, heterogeneity, and variety of services, anomaly detection is becoming an increasingly important challenge in the operation of new generations of mobile communications. In many cases, the underlying relationships between the multiplicity of parameters and factors that can cause anomalous behavior are only determined by human expert knowledge. On the other hand, although automatic algorithms have a great capacity to process multiple sources of information, they are not always able to correctly signal such abnormalities. In this sense, this paper proposes the integration of both components in a framework based on Active Learning that enables enhanced performance in anomaly detection tasks. A series of tests have been conducted using an online anomaly detection algorithm comparing the proposed solution with a method based on the algorithm output alone. The obtained results demonstrate that a hybrid anomaly detection model that automates part of the process and includes the knowledge of an expert following the described methodology yields increased performance.

The location of user equipments (UEs) allows application developers to customize the services for users to perceive an enhanced experience. In addition, this UE location enables network operators to develop location-aware solutions to optimize network resource management. Moreover, the combination of location-aware approaches and new network features introduced by 5G enables to further improve the network performance. In this sense, dual connectivity (DC) allows users to simultaneously communicate with two nodes. The basic strategy proposed by 3GPP to select these nodes is based only on the power received by the users. However, the network performance could be enhanced if an alternative methodology is proposed to make this decision. This paper proposes, instead of power-based selection, to choose the nodes that provide the highest quality of experience (QoE) to the user. With this purpose, the proposed system uses the UE location as well as multiple network metrics as inputs. A dense urban scenario is assumed to test the solution in a system-level simulation tool. The results show that the optimal selection varies depending on the UE location, as well as the increase in the QoE perceived by users of different services.