Self-organizing networks (SONs) aim to raise the level of automated operation in next-generation networks. One of the use cases defined in this field is the optimization of the handover (HO) process, which involves a tradeoff between the amount of signaling load due to HOs and the quality of the active connections in the network. In this paper, first, a sensitivity analysis of the two main HO parameters, i.e., the HO margin (HOM) and the time-to-trigger (TTT), is carried out for different system load levels and user speeds in a Long-Term Evolution (LTE) network. Second, a fuzzy logic controller (FLC) that adaptively modifies HOMs is designed for HO optimization. In this case, different parameter optimization levels (network-wide, cell-wide, and cell-pair-wide) and the impact of measurement errors have been considered. Results of the sensitivity analysis show that tuning HOMs is an effective solution for HO optimization in LTE networks. In addition, the FLC is shown as an effective technique to adapt HOM to different network conditions so that the signaling load in the network is decreased while an admissible level of call dropping is achieved.

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.

Future wireless networks will be Heterogeneous Networks (HetNet), as they will comprise multiple Radio Access Technologies (RAT) and network architectures. The complexity of managing and operating such networks is forcing operators to find new strategies if they want to remain competitive. In this context, Self-Organizing Networks (SON) have arisen as one of the key technologies for enhancing operation and minimizing costs in future networks. SON functionalities can be classified as self-configuration, self-optimization, and self-healing. Although the first two categories have received considerable attention in the last years, the studies devoted to self-healing have been scarce. In addition, existing references use different terminology and concepts and they focus only on partial aspects of self-healing for a particular RAT. This article unifies previous research and proposes a reference model for self-healing in which terms and functions are unambiguously defined. In addition, this article presents a survey on the state of the art, in which the main references on self-healing are outlined. Finally, the main research challenges in self-healing that will have to be faced in the near future are summarized.

Fifth Generation (5G) is here to accelerate the digitization of economies and society, and open up innovation opportunities for verticals. A myriad of 5G-enabled use cases has been identified across disparate sectors like tourism, retail industry, and manufacturing. Many of the networks of these use cases are expected to be private networks, that is, networks intended for the exclusive use of an enterprise customer. This article provides an overview of the technical aspects in private 5G networks. We first identify the key requirements and enabling solutions for private 5G networks. Then, we review the latest 3rd Generation Partnership Project (3GPP) Release 16 capabilities to support private 5G networks. Next, we provide architecture proposals for single site private networks, including the scenario in which the radio access network (RAN) is shared. Afterwards, we address mobility and multi-site private 5G network scenarios. Finally, we identify key challenges for private 5G networks.

Mobile-broadband traffic has experienced a large increase over the past few years. Femtocells are envisioned to cope with such a demand of capacity in indoor environments. Since those small cells are low-cost nodes, a thorough deployment is not typically performed, particularly in enterprise scenarios. As a result, the matching between traffic demand and network resources is rarely optimal. In this paper, several load balancing techniques based on self-tuning of femtocell parameters are designed to solve localized congestion problems. In particular, these techniques are implemented by fuzzy logic controllers (FLC) and fuzzy rule-based reinforcement learning systems (FRLSs). Performance assessment is carried out in a dynamic system-level simulator. Results show that the combination of FLC and FRLS produces an increase in performance that is significantly higher than if techniques are implemented alone. Both the response time and the final value of performance indicators are improved.