Joao F. Santos

João Paulo Esper, Yure Freitas, Pedro Souza et al.

While the Open Radio Access Network Alliance (O-RAN) architecture enables third-party applications to optimize radio access networks at multiple timescales, real-time distributed applications (dApps) that demand low latency, high performance, and strong isolation remain underexplored. Existing approaches propose colocating a new RAN Intelligent Controller (RIC) at the edge, or deploying dApps in bare metal along with RAN functions. While the former approach increases network complexity and requires additional edge computing resources, the latter raises serious security concerns due to the lack of native mechanisms to isolate dApps and RAN functions. Meanwhile, WebAssembly (Wasm) has emerged as a lightweight, fast technology for robust execution of external, untrusted code. In this work, we propose a new approach to executing dApps using Wasm to isolate applications in real-time in O-RAN. Results show that our lightweight and robust approach ensures predictable, deterministic performance, strong isolation, and low latency, enabling real-time control loops.

Merim Dzaferagic, Marco Ruffini, Nina Slamnik-Krijestorac et al.

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.

Erika Fonseca, Joao F. Santos, Francisco Paisana et al.

\ac{RAT} classification and monitoring are essential for efficient coexistence of different communication systems in shared spectrum. Shared spectrum, including operation in license-exempt bands, is envisioned in the \ac{5G} standards (e.g., 3GPP Rel. 16). In this paper, we propose a \ac{ML} approach to characterise the spectrum utilisation and facilitate the dynamic access to it. Recent advances in \acp{CNN} enable us to perform waveform classification by processing spectrograms as images. In contrast to other \ac{ML} methods that can only provide the class of the monitored \acp{RAT}, the solution we propose can recognise not only different \acp{RAT} in shared spectrum, but also identify critical parameters such as inter-frame duration, frame duration, centre frequency, and signal bandwidth by using object detection and a feature extraction module to extract features from spectrograms. We have implemented and evaluated our solution using a dataset of commercial transmissions, as well as in a \ac{SDR} testbed environment. The scenario evaluated was the coexistence of WiFi and LTE transmissions in shared spectrum. Our results show that our approach has an accuracy of 96\% in the classification of \acp{RAT} from a dataset that captures transmissions of regular user communications. It also shows that the extracted features can be precise within a margin of 2\%, %of the size of the image, and is capable of detect above 94\% of objects under a broad range of transmission power levels and interference conditions.