Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby providing only a partial view of HOs. This paper presents the first countrywide analysis of HO performance, from the perspective of a top-tier MNO in a European country. We collect traffic from approximately 40M users for 4 weeks and study the impact of the radio access technologies (RATs), device types, and manufacturers on HOs across the country. We characterize the geo-temporal dynamics of horizontal (intra-RAT) and vertical (inter-RATs) HOs, at the district level and at millisecond granularity, and leverage open datasets from the country's official census office to associate our findings with the population. We further delve into the frequency, duration, and causes of HO failures, and model them using statistical tools. Our study offers unique insights into mobility management, highlighting the heterogeneity of the network and devices, and their effect on HOs.

As network complexity escalates, there is an increasing need for more sophisticated methods to manage and operate these networks, focusing on enhancing efficiency, reliability, and security. A wide range of Artificial Intelligence (AI)/Machine Learning (ML) models are being developed in response. These models are pivotal in automating decision-making, conducting predictive analyses, managing networks proactively, enhancing security, and optimizing network performance. They are foundational in shaping the future of networks, collectively forming what is known as Network Intelligence (NI). Prominent Standard-Defining Organizations (SDOs) are integrating NI into future network architectures, particularly emphasizing the closed-loop approach. However, existing methods for seamlessly integrating NI into network architectures are not yet fully effective. This paper introduces an in-depth architectural design for a Network Intelligence Stratum (NI Stratum). This stratum is supported by a novel end-to-end NI orchestrator that supports closed-loop NI operations across various network domains. The primary goal of this design is to streamline the deployment and coordination of NI throughout the entire network infrastructure, tackling issues related to scalability, conflict resolution, and effective data management. We detail exhaustive workflows for managing the NI lifecycle and demonstrate a reference implementation of the NI Stratum, focusing on its compatibility and integration with current network systems and open-source platforms such as Kubernetes and Kubeflow, as well as on its validation on real-world environments. The paper also outlines major challenges and open issues in deploying and managing NI.

As research in mobile networks is already transitioning from 5G to 6G, we identify a set of fundamental barriers in the current 5G architecture that limit efficient and global operations. We propose innovative architectural solutions that can remove such barriers and lay the foundation for 6G systems. Specifically, we introduce three novel architectural components: the Global Service-Based Architecture (GSBA), the Compute Continuum Layer (CCL), and the Zero-Trust Layer (ZTL). These components collectively aim to enhance network efficiency, security, and scalability, addressing future mobile networks' dynamic and demanding needs. Furthermore, we discuss the integration of Network Intelligence (NI) that exploits the afore-mentioned architectural innovations to ensure global operations and services. Ultimately, our proposed vision entails a more adaptive, secure, and intelligent network architecture, setting the groundwork for the next generation of mobile networks.

This paper studies a promising use case of a private 5G network for the sports industry: wearable bodycams and sensors in a football match. This use case requires a reliable and dedicated massive MIMO network to provide uniform coverage with a high capacity in the whole pitch area. The coverage of co-located and distributed (cell-free) massive MIMO, differing in the configuration of base station antenna placement inside the stadium, are evaluated through ray tracing using a 3D stadium model and players' mobility dataset. The results give insight into how distributed antennas inside the stadium should be deployed to enhance the uniform coverage of moving wearable devices. Besides that, the uplink capacity performance and the impact of channel aging on the capacity are also evaluated. The results demonstrate the advantages and disadvantages of different base station antenna distribution strategies in terms of coverage, capacity and channel aging impact.