This demonstration paper presents a real-life 5G Standalone proof-of-concept showcasing end-to-end network slicing with both inter-slice and intra-slice isolation across the 5G Core, Transport Network, and Radio Access Network domains. The system, based on a smart hospital scenario, highlights a decentralized 5G architecture that aligns slice selection, controlplane, and data-plane mechanisms to guarantee that network resources are securely partitioned across the entire infrastructure. The demonstration highlights the technical implementation of slice orchestration and performance isolation, showing how critical services such as robotic-surgery control or vital-sign sensors maintain guaranteed quality of service even under high load, while less critical services coexist without interference. The proof-of-concept confirms the practical viability of complete end-to-end network slicing for mission-critical applications that demand reliability, confidentiality, and predictable performance.

This paper presents a comprehensive benchmark study of a real-life 5G Standalone (SA) deployment with different Multiple Input Multiple Output (MIMO) configurations (1x1, 2x2, and 4x4) in an indoor office environment. We evaluate the impact of distance, obstacles, and material composition on key performance metrics, including throughput, Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), and Rank Indicator (RI). The results demonstrate that while higher-order MIMO configurations can deliver substantial throughput gains under favorable conditions, their effectiveness is fundamentally constrained by environmental factors such as signal attenuation, multipath propagation, and material-induced losses. Our results provide practical guidelines for indoor network planning and optimization, establishing concrete performance baseline for open-source 5G systems in typical office scenarios highlighting the critical importance of site selection.

The exponential growth in connectivity and computing demand has made Network Function Virtualization Infrastructure (NFVI) a major contributor to global energy consumption. Conventional network and service deployments, whether based on legacy hardware appliances or NFVI stacks, struggle to dynamically provision resources for peak data traffic demand. This results in nearly constant energy consumption, even during low-traffic periods, leading to inefficient resource use. Network softwarization and virtualization have enabled flexible and programmable service deployments, which are beneficial for the rapid and dynamic scaling of network functions. This paper validates energy-aware service and network orchestration with a Zero-touch Network and Service Management (ZSM) framework for the autonomous optimization of computing, network, and power resources from NFVI in a use case focused on connected mobility, and in particular, smart traffic management. By modeling road traffic based on vehicle count and type and 3rd Generation Partnership Project (3GPP) profiles for data formats in vehicular communication scenarios, the ZSM framework adjusts services and resources to service requirements and to actual demand. Experimental validation on the real-life Smart Highway testbed in Antwerp, Belgium, demonstrates a strong correlation between vehicular traffic and power consumption, supporting the hypothesis that adaptive compute and network resource management reduces unnecessary energy use and advances the vision of sustainable and self-optimizing Sixth-Generation (6G) networks.

Despite major advances in Connected and Autonomous Vehicles (CAVs), edge cases such as unmapped construction zones or dense urban areas still require human intervention. Teleoperation, often combined with Level 4 automation, addresses these situations but demands strict network performance i.e., latency below 5 ms, uplink throughput above 25 Mbps, and 99.999% reliability, to ensure safe, responsive control under heavy load. This paper presents a Proof-of-Concept (PoC) 5G Standalone (SA) network supporting end-to-end Network Slicing for teleoperation. The PoC achieves seamless slice isolation across the 5G Core (5GC), Transport Network (TN), and Radio Access Network (RAN), enabling dynamic and cross-domain Network Slicing to meet teleoperation requirements.

The distribution of services across heterogeneous edge and cloud infrastructures in Beyond 5G (B5G) and Sixth-Generation (6G) networks increases operational complexity, while existing MANagement and Orchestration (MANO) solutions remain single-domain and lack unified telemetry or autonomous cross-domain decision mechanisms. Prior work proposes cross-domain orchestration concepts, but most validations rely on simulation and overlook interoperability issues, non-stationary latency, and inconsistent Key Performance Indicator (KPI) models in real Network Function Virtualization Infrastructure (NFVI) deployments. This paper introduces a modular, technology-agnostic Zero-touch Network and Service Management (ZSM) framework that provides unified NFVI abstraction and a multi-criteria decision engine that jointly considers latency, compute load, and energy consumption for autonomous service placement, scaling, and dynamic resource provisioning. Evaluation in a real-world deployment across geographically distributed testbeds demonstrates that our ZSM framework mitigates latency spikes by up to 96%, reduces overloaded-node latency by 36%, and maintains stable performance under variable load. These results confirm the practicality and effectiveness of zero-touch, multi-domain orchestration for future 6G compute-continuum environments.

Internet of Things (IoT) devices are increasingly being deployed in critical applications, such as eHealth systems, enabled by advancements in 5G technology, which offer more than 100 Mbps of throughput, less than 5 ms of latency, and $99,999 \%$ of reliability. However, to overcome computing limitations and security measures, IoT devices rely on cloudbased solutions to outsource data processing. This dependency introduces significant security concerns, as sensitive data must be transmitted over the network and processed in external environments, increasing the risk of interception, unauthorized access, and data breaches. To mitigate these security risks, within the scope of the MOZAIK project, we deploy Network Slicing to ensure end-to-end inter-slice and intra-slice isolation across all network domains i.e., 5G Core (5GC), Transport Network (TN), and Radio Access Network (RAN). We deploy a synergy across the entire network infrastructure i.e., $5 \mathrm{GC}, \mathrm{TN}$, and RAN, to isolate the IoT data flows from the moment the data is generated until it reaches the cloud, safeguarding sensitive data during transmission. The results of our real-life experiments demonstrate that our proof of concept provides robust isolation between slices, effectively addressing the security concerns of IoT devices and enhancing the reliability and security of IoT applications. Additionally, we also include aspects of secure data storage and secure data processing, covered in the MOZAIK project.

Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement. Compared to CEViT, a state-of-the-art vision transformer-based estimator, HELENA reduces inference time by 45.0\% (0.175\,ms vs.\ 0.318\,ms), achieves comparable accuracy ($-16.78$\,dB vs.\ $-17.30$\,dB), and requires $8\times$ fewer parameters (0.11M vs.\ 0.88M), demonstrating its suitability for low-latency, real-time deployment.

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.

The transition from 5G to 6G networks will catalyze the development of advanced 6G Applications (6G Apps) with enhanced network programmability and intelligence, providing vertical industries and Communication Service Providers (CSPs) with new opportunities to optimize their operations. This article explores the future of the 6G Apps tailored to verticals in the 6G era, highlighting their role as middleware that abstracts network complexities and exposes Application Programming Interfaces (APIs) to enable dynamic interaction and real-time adaptation. Key enablers such as network exposure, Artificial Intelligence (AI), and edge computing are studied in the context of optimizing operations across verticals, and improving Quality of Service (QoS) and fostering innovation. A case study on teleoperated vehicles exemplifies the real-world applicability of these technological enablers for 6G Apps. Furthermore, this article offers insights and explores new research opportunities for 6G Apps tailored to verticals to evolve in the 6G era while addressing key challenges in deploying these applications in real-world commercial networks as a service.