# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 2 Related Work
Furthermore, mapping RU shift requirements to local mobile edge computing centers for future centralized processing would significantly reduce power consumption in cellular networks. That is, the study of the resource allocation problem between the RU-DU of the O-RAN system was modeled as a 2D packing problem of RU-DU resources in O-RAN, proposing a self-play reinforcement learning strategy. They apply the combined approach of deep neural networks with Monte-Carlo Tree Search (MCTS) to solve this combinatorial optimization problem. With high throughput and function approximation capability, a deep neural network modeled resource allocation policy can generalize under dynamic network conditions. Learning by auto-run eliminates the need for demonstrator data, which can be expensive and time-consuming to collect [19]. The authors describe that the application of ML as a data analysis method allows machines to explore data and make predictive and proactive decisions in real time [20]. ML can play significant roles in learning from variations in the wireless environment, categorizing problems, anticipating challenges, predicting outcomes, and exploring possible solutions, decisions, and actions [20]. The ML framework can exploit data from different types of UEs to predict traffic volume and dynamically allocate available network resources. In 5G, the RRM (Radio Resource Management) framework in the RAN includes various control functions based on radio measurements and other observations of various user devices or network elements. Current RANs are reactive and base stations run algorithms on CUs to meet user demands. However, in 5G, even milliseconds of delay can cause a great impact. To enable certain mission-critical applications, such as remote robotic surgery, the 5G network must be predictive and proactive, rather than just reactive. In O-RAN, computing and storage resources must be distributed among the different datacenters that can host the ML algorithm to proactively serve users. Sustaining advanced Radio Link Monitoring (RLM) requires an efficient implementation of ML-based traffic optimization that can handle large volumes of data in 5G networks. This learning framework would be able to run algorithms autonomously to deal with latency, meeting the RRM functionality and reliability requirements of users [19]. [PERSON] et al. [21] define the management and optimization of new network systems that require solutions that, by opening the RAN access network, expose data and analysis and allow advanced data-driven optimization, closed-loop control and automation. They describe that the challenges are not easy to overcome, as current approaches to cellular networks are the opposite of open. In RAN, network components are monolithic units, complete solutions that implement each layer of the cellular protocol stack, provided by a limited number of vendors and seen by operators as black boxes [21]. O-RAN embraces and extends the 3 GPP NR 7.2 split to base stations between the RU and the DU. We can thus have the functionalities of the base station in CU, DU and RU. Furthermore, it connects them to intelligent controller systems through open interfaces that can transmit telemetry of the RAN and implement actions and policies to control it. Note that the O-RAN architecture includes two RAN Intelligent Controllers (RICs) that perform network management and control at near-real-time (10 ms to 1 s) and non-real-time (greater than 1 s) timescales, which have not been used in the OpenCare5G project. Future work will be focused on extensions to real-time control loops, including loops that operate in the real-time domain, i.e., below 10 ms, for managing radio resources at the RAN node level, or even below 1 ms for device management and optimization. Typical examples of real-time control include programming, beam management and feedback-free detection of physical layer parameters (e.g., modulation and coding scheme, interference recognition).