## 2 6G Key Enabling Technologies
### O-Ran  
The concept of O-RAN allows service providers to use resources or non-proprietary subcomponents from a variety of vendors. Therefore, disaggregated services can be supported by various heterogeneous infrastructures [42]. O-RAN enables programmable, intelligent, disaggregated, virtualized, and interoperable functions. AI/ML technologies can also be deployed with the O-RAN radio intelligence controller (RIC) architecture. According to the O-RAN Alliance specifications describing the AI/ML workflow and requirements [43; 44], two main entities, namely the non-real-time RAN intelligent controller (non-RT RIC) and the near-real-time RAN intelligent controller (near-RT RIC), will play a critical role in the AI/ML assistance and control loops, determining the optimization rationale of the O-RAN deployments according to the decision time scale each of them handles (e.g., the near-RT RIC operates in order of ms, while the non-RT RIC decides above 500 ms). A schematic overview of O-RAN in the context of 6G networks is depicted in Figure 4. The figure illustrates the architecture of an O-RAN framework, emphasizing the interaction between different functional components across various cloud domains. The service management and orchestration (SMO) layer oversees the network and interfaces with the non-RT RIC via the O1 interface. The non-RT RIC hosts rApps, which are AI/ML-driven applications that operate on a non-real-time scale (greater than one second). These applications focus on policy-driven, long-term network optimizations such as traffic forecasting, anomaly detection, energy-efficiency enhancements, and capacity planning. By leveraging extensive historical data, rApps contribute to proactive and large-scale adjustments that improve overall network performance. Below, the near-RT RIC operates within the edge cloud and manages network optimizations via the E2 interface. It hosts xApps, which execute real-time optimizations (10 ms to 1 s latency) for network elements. These xApps are responsible for functions such as dynamic resource allocation, interference management, power control, and handover optimization. By continuously analyzing network conditions and making rapid adjustments, xApps enhance user experience, minimize latency, and improve spectral efficiency. Their placement within the near-RT RIC ensures that they can make decisions quickly without the delays associated with higher-layer optimizations. Further down in the architecture, the centralized unit (CU) is split into the control plane (CU-CP) and user plane (CU-UP), which communicate over the E1 interface. The CU components connect with the distributed unit (DU) through the F1-c (control) and F1-u (user data) interfaces. The DU, in turn, interacts with the radio units (RUs) over an open fronthaul (FH) interface, enabling flexible and vendor-neutral RAN deployments. Within the DU, dApps operate as distributed applications tailored to localized and fine-grained optimizations. Unlike xApps, which function at the near-RT RIC level, dApps are deployed directly on the DU, allowing them to make ultra-low-latency optimizations at the radio access level. These applications can perform localized power management, adaptive modulation control, and beamforming adjustments, ensuring efficient and real-time operation at the edge of the network. The regional cloud, edge cloud, and cell area layers visually segment the architecture, showing where different components reside. The yellow arrows indicate the interaction of AI-driven applications (rApps, xApps, and dApps) within the O-RAN architecture, contributing to intelligent RAN optimization and management. By distributing intelligence across different levels of the network, O-RAN can facilitate a flexible, efficient, and vendor-neutral approach to modern wireless communication. On the other side, O-RAN introduces several potential security risks due to its open and disaggregated architecture.