## 3 ML Application in O-RAN
### Dynamic Function Split  
Dynamic function splitting is an essential technique for enhancing the efficiency and flexibility of O-RAN. It involves breaking down a network node or application's functions into smaller and more modular components, which can be distributed across different computing resources. By leveraging ML, O-RAN can benefit from real-time intelligence and decision-making capabilities. ML algorithms can analyze network traffic patterns and resource usage to predict future demand and allocate computing resources accordingly, leading to better resource utilization, reduced latency, and improved overall network performance. Additionally, ML can optimize the selection of computing resources for specific functions based on factors like location, processing power, and energy efficiency, resulting in better dynamic function splitting and better service delivery to users. \begin{table}
\begin{tabular}{c p{34.1 pt}} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2022 & [37] & Introduced the application of ML to network slicing discussed some open challenges and potential solutions. \\ \hline
2022 & [38] & Provided an intelligent closed-loop SLA assurance scheme for O-RAN slicing. A real-world dataset of a large operator is used to train a learning solution for optimizing resource utilization in the proposed closed-loop service automation process. \\ \hline
2022 & [39] & Developed a novel O-RAN slicing framework over an evolutionary-based DRL approach to manage network slices dynamically in the rapid changing environment. \\ \hline
2022 & [40] & Addressed the elastic O-RAN slicing problem for industrial monitoring and control in IIoT and introduced a matching game for solving the IIoT association problem, and then applied an actor-critic-based deep reinforcement learning model for O-RAN slicing-based resource allocation. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of ML studies for network slicing in O-RAN. In [41], the authors provide a RL-based approach to the problem of optimizing dynamic function splitting in O-RAN compliant disaggregated and virtualized RANs. Their paper addresses the specific scenario of function splitting between a CU and one or more DUs in a fully virtualized environment, as opposed to splitting between virtual and physical resources. They adopt a multi-agent RL approach using either Q-Learning or State Action Reward State Action (SARSA); a similar but slightly different alternative to Q-Learning. Optimization takes into account the traffic type (e.g., eMBB or URLCC) and also energy efficiency. It is assumed that the CU and each of the DUs run on separate physical environments, each with their own renewable energy source backed up by a battery-based energy storage facility, with a grid connection as a reserve input. Optimization is designed to minimize operational expenditure (Opex), including maximizing the usage of the renewable energy source, taking into account the energy remaining in the battery and also the variation in grid electricity prices during the day. Optimization takes place over a 48-h period and comparative Opex results are presented using solar radiation data from Stockholm, Cairo, Jakarta, and Istanbul, combined with broad traffic level assumptions. In [42], a novel and efficient energy-efficient RAN disaggregation and virtualization method tailored for O-RAN is presented. This method effectively tackles challenges related to dynamic traffic conditions. By formulating the energy consumption as a multi-objective optimization problem, the authors integrate the Advantage Actor-Critic (A2C) algorithm with a sequence-to-sequence model to effectively address the sequential nature of RAN disaggregation and capture long-term dependencies. The results demonstrate the effectiveness of the proposed solution to reduce energy consumption for dynamic Virtual VNF splitting over traditional approaches like D-RAN and C-RAN.