The Open Radio Access Network (O-RAN) architecture is key to enabling AI-driven dynamic network management. However, the complexity of this architecture introduces challenges, especially in managing conflicts between different AI-driven applications that operate concurrently within the network. These conflicts, if left unchecked, can lead to degraded network performance and service disruptions. To address this issue, we propose XAI4C (Explainable AI for Conflict Detection), a framework that leverages the SHAP (SHapley Additive exPlanations) explainable AI technique. XAI4C enhances transparency and interpretability in AI decision-making by helping network operators understand the factors driving AI decisions across different network components thereby allowing for early detection of conflicts between applications. In this paper, we first present the architecture and operation of the XAI4C framework. We then demonstrate its effectiveness in conflict detection through two case studies related to network slicing. Our results demonstrate that XAI4C outperforms the state-of-the-art PACIFISTA providing a detection accuracy increase up to 30%, while reducing the number of samples required for conflict detection by 41.17%.

The rapid evolution of 5 G networks, with diverse traffic classes and demanding services, highlights the importance of Open Radio Access Networks (O-RAN) for enabling RAN intelligence and performance optimization. Machine Learning-powered xApps offer novel network control opportunities, but their resource demands necessitate efficient orchestration. To address these issues, we present OREO, an O-RAN xApp orchestrator that, using a multi-layer graph model, aims to maximize the number of RAN services concurrently deployed while minimizing their overall energy consumption. OREO's key innovation lies in the concept of sharing xApps across RAN services when they include semantically equivalent functions and meet quality requirements. Despite the NP-hard nature of the problem, numerical results show that OREO offers a lightweight and scalable solution that closely and swiftly approximates the optimum in several different scenarios. Also, OREO outperforms state-of-the-art benchmarks by enabling the co-existence of more RAN services (14.3% more on average and up to 22%), while reducing resource expenditure (by 48.7% less on average and up to 123% for computing resources). Moreover, using an experimental prototype deployed on the Colosseum network emulator and using real-world RAN services, we show that OREO leads to substantial resource savings (up to 66.7% of computing resources) while its xApp sharing policy can significantly enhance quality of service.

The Open Radio Access Network (O-RAN) architecture aims to support a plethora of network services, such as beam management and network slicing, through the use of third-party applications called xApps. To efficiently provide network services at the radio interface, it is thus essential that the deployment of the xApps is carefully orchestrated. In this paper, we introduce OREO, an O-RAN xApp orchestrator, designed to maximize the offered services. OREO’s key idea is that services can share xApps whenever they correspond to semantically equivalent functions, and the xApp output is of sufficient quality to fulfill the service requirements. By leveraging a multi-layer graph model that captures all the system components, from services to xApps, OREO implements an algorithmic solution that selects the best service configuration, maximizes the number of shared xApps, and efficiently and dynamically allocates resources to them. Numerical results as well as experimental tests performed using our proof-of-concept implementation, demonstrate that OREO closely matches the optimum, obtained by solving an NP-hard problem. Further, it outperforms the state of the art, deploying up to 35% more services with an average of 30% fewer xApps and a similar reduction in the resource consumption.

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated.In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their abstraction-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements.

The new generation mobile network requires flexibility and efficiency in radio link management (RRM), in order to support a wide range of services and applications with diverse target KPI values. In this perspective, the O-RAN Alliance introduces a flexible, intelligent and virtualized RAN architecture (O-RAN), which integrates artificial intelligence models for effective network and radio resource management (RRM). This work leverages an O-RAN platform to develop and assess the performance of an RRM solution based on Reinforcement Learning (RL) and deployed as xApp in the O-RAN ecosystem. The framework receives periodic reports from the O-RAN Distributed Unit (DU) about the network status and dynamically adapts the per-flow resource allocation as well as the modulation and coding scheme to meet the traffic flow KPI requirements.