## 3 AI/ML in the Standards and Industry
### Open RAN
#### 3.3.1 RAN Intelligent Controllers  
The RAN Intelligent Controllers (RICs), including non-real-time (>1 s) and near-real-time (>10 ms and <1 s) operations, lie at the heart of the O-RAN architecture and introduce intelligent management of radio resources, self-organisation, and self-optimisation in the RAN by applying closed-control loops between the RICs and the RAN functions. Traditionally, the optimisation of the RAN is a vendor-specific problem, but introducing RICs clearly disaggregates the RAN from its optimisation process. Through open interfaces and RICs, the RAN functionality becomes available to operators, service providers and other parties, who can also contribute to RAN optimisation, using AI/ML. The non-real-time RIC resides outside the RAN at the service management and orchestration (SMO) layer and is responsible for optimising in non-real-time the operation of the RAN, e.g., the orchestration of RAN slices, as well as enhancing network policies and boosting the E2E network performance. To do that, the controller receives real-time RAN data over the A1 interface (see Figure 9); data from other sources, such as the SMO framework, e.g., topology and configuration data; data from external repositories, e.g., contextual information; and historical data from the management data analytics function (MDAF). By applying big data analytics and AI/ML tools, the non-real-time RIC can optimise not only the performance at the RAN, but can also suggest joint network and RAN policies, e.g., coupling together the orchestration of network slices with radio resource scheduling techniques, making a step towards cross-layer network optimisation. To give some representative examples of network optimisation using the non-real-time RIC, let us consider an emergency response scenario, where public safety personnel is deployed in the affected area. Using data collected from various sources, such as the map of the area received from an external database, the non-real-time RIC can prioritise the personnel operating at critical locations. Given the location of an UE, the non-real-time RIC may also apply traffic steering so that an emergency voice call is served by a macro cell, while eMBB video transmission from the scene to the command-and-control centre is let through a small cell in the area that operates at mm-wave frequencies. These sorts of functionalities are not supported by existing wireless networks; therefore, it is the non-real-time RIC that has mostly attracted the interest of the telecommunication industry. In addition, the non-real-time RIC is responsible for ML model selection and training, while the near-real-time RIC carries out the ML inference once it receives the selected and  
Figure 8: The two main changes in the evolution from virtual RAN to Open RAN are the commodity remote radio unit (RRU) and the open fronthaul (FH) interface. Note that 3 GPP TR 38.801 has divided the baseband unit (BBU) into the centralised unit (CU) and the distributed unit (DU), where a single DU usually manages multiple RRUs. The common public radio fronthaul interface (CPRI) is a vendor-specific interface. trained models. Recall from Section 3.2 that in the architectural framework proposed by ITU, an MNO can look at the ML marketplace for available models and use the ML sandbox to train and test them before deployment. At the specified timescales, the non-real-time RIC handles, for instance, with interference and spectrum management, traffic steering, handover, and resource allocation for specific users or slices. The ML models implementing these functions are embedded into the non-real-time RIC, in the form of programmable applications (xApps), which might belong to different vendors. The near- and non-real-time RIC are connected through the A1 interface but can also communicate via the O1 interface as illustrated in Figure 9. The non-real-time RIC controls through the E2 interface the centralised and distributed unit (CU/DU) functions.