## 3 ML Application in O-RAN
### Traffic Steering  
O-RAN traffic steering research aims to optimize network traffic management by intelligently routing traffic based on real-time network conditions and user demand. This involves analyzing various network parameters, such as traffic load, network congestion, and user behavior, to determine the best path for traffic flow. Traffic steering techniques can also be used to dynamically split traffic across different network functions and resources in real-time, improving network performance and reducing latency. ML algorithms can play a critical role in traffic steering research, providing real-time intelligence and decision-making capabilities to optimize traffic flow and improve overall network performance. However, there are still challenges related to scalability, complexity, and limited resources that need to be addressed to achieve optimal traffic steering in O-RAN. \begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2021 & [48] & Applied an ML algorithm in O-RAN to maintain QoS satisfaction by controlling admission of PDU sessions. \\ \hline
2021 & [49] & Applied DQN to optimize user-cell association in O-RAN by supporting the global RAN automation through load balancing over the network resources. \\ \hline
2021 & [50] & Proposed a smart user-centric access control in O-RAN using Federated DRL-based learning to mitigate frequent handover. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of current efforts on ML applications for O-RAN session management. In O-RAN, more processing is done by placing virtual network functions (VNF) in the DU before transferring data over midhaul links. This means that placing VNFs in the CU needs more bandwidth compared to placing in the DU [60]. Therefore, the question is how many functions should be left in the DUs to prevent network congestion on midhaul links arises. This is especially the case when DUs have limited computing/storage capacities compared to CUs. The authors of [61] have proposed an optimization problem to select the split points in O-RAN. The objective of this study is to balance the load across CUs as well as midhaul links with respect to the required delay and bandwidth and processing capacity of the DUs and CUs. Going beyond the static optimization, it is also noted that in real-world scenarios under traffic demands dynamicity and uncertainty at RUs, methods like RL and DRL can be used to provide dynamic VNF splitting across CUs and DUs. In [62], O-RAN decouples the Control Plane (CP) from the User Plane (UP) through the E1 interface, which is derived from the software defined network (SDN) architecture. This separation of CP and UP allows a network to be more flexible in programming. The CP is implemented in hierarchical RICs, and manages radio resource functions through A1 and E2 interfaces. The authors of [62] proposed using the hierarchical RICs to minimizes end-to-end delay of the data plane traffic by placing Containerized Network Functions (CNFs) effectively. In comparison to VNFs, CNFs are lighter and can be implemented through microservice architectures, enabling a dynamic, scalable, and flexible architecture towards 5G [63]. Additionally, paper [4] discusses the use of ML methods to achieve modular and flexible O-RAN implementations in 6G networks, with a focus on the traffic steering use case and O-RAN xApps. The authors describe several ML algorithms that can be used for traffic steering, including decision trees, k-nearest neighbor (KNN), and neural networks. They also discuss the use of RL to train an agent to make traffic steering decisions in real-time. In [51], a federated meta-learning approach for traffic steering in O-RAN systems is proposed. This approach allows multiple Radio Access Technologies (RATs) to learn from each other without sharing their private data. The authors present a neural network architecture that uses meta-learning to adapt to different RATs and learn to steer traffic in a decentralized manner.