## 4 Proposed Architectural Approach
### Support of AI/ML in O-RAN  
O-RAN working groups have specified RAN components to host ML functionality across network domains, attending to both offline and online training and inference. Offline training refers to the time-consuming processes required for training a model. This is especially the case for deep learning-based models (or, in general, supervised and unsupervised learning), which use historically gathered data to construct predictive models. Online training refers to real-time agents that learn by interacting with the environment through trial-and-error, which is usually the case for Reinforcement Learning algorithms. The offline training support is essential in O-RANs because time-sensitive decisions have to exploit already pre-trained and validated models. In this respect, this approach mainly relies on the following design principles: (i) an offline learning module is by default essential, (ii) offline training refers to a pre-trained model that may be inferred during the online operation of the network, (iii) online training refers to the concept of real-time learners (e.g., reinforcement learning, actor-critic algorithms), as the model is trained through interaction with the network and (iv) completely untrained models cannot be directly deployed in the network without prior training and testing.  
In O-RAN based architectures, two critical building blocks will be responsible for the execution of the ML workflow: (i) the Near-RT RIC is a logical function that enables near-real-time control and optimization of RAN elements and resources via fine-grained data collection and actions over E2 interface, as depicted in Figure 6, and (ii) the Non-RT RIC, that enables non-real-time control and optimization of RAN elements and resources, AI/ML workflow including model training, inference and updates, and policy-based guidance of applications/features in Near-RT RIC (via A1 interface). It should be noted that in the deployment of supervised and unsupervised ML algorithms, the ML training host is essentially located in the Non-RT RIC, while the ML model host/actor can be located either in the Non-RT RIC or in the Near-RT RIC. On the contrary, in the framework of  
Figure 5: Proposed architecture for NT functionalities.  
reinforcement learning, both the ML training host and the ML inference host/actor shall be co-located as part of Non-RT RIC or Near-RT RIC.  
There are three types of control loops defined in O-RAN depending on the time sensitivity of the required decision-making process. Graphically, Loop 1 refers to the case whereby the model inference is hosted in the O-DU and the exact configuration of this loop is under consideration. Loops 2 and 3 are clearly defined as the loops that host the ML training at the Non-RT RIC, while the ML inference is typically running in the Near-RT RIC and the Non-RT RIC, respectively (Figure 6). In this context, the proposed approach implements a complete training-inference-retraining cycle, tested and checked within the whole parts of the O-RAN architecture. To this end, a holistic testing of ML models is supported, starting from the model deployment and training, model inference and action taking, and ending at the model evaluation for possible retraining and update.