## 4 Research Opportunities in O-RAN
### Proactive Maintenance  
A significant fraction of the cost of maintaining a cellular radio network is due to the need for site visits. Virtualization of the RAN within the O-RAN framework may help considerably by extending the scope for remote or automatic intervention to mitigate hardware failures, but ultimately the only way to repair faulty on-site hardware is by making a site visit. Ideally, it would be possible to predict failures before they occur, allowing proactive scheduling of site visits. Deep learning systems can potentially assist here by trawling very large datasets to detect \"precursor\" events occurring before failures, which might be difficult to find by an engineer tasked with scanning the fault logs. Before this can happen, however, at least two open issues need to be addressed. The first is to evolve the O-RAN framework in conjunction with ML system design so that detailed fault-related data are provided at the O1 interface, including additional information required by the virtualization of the RAN. The second is related to the fact that in a reliable network fault data are relatively scarce, with the further issue that equipment from different vendors may operate in slightly different ways. Here, the challenge is to develop ML training approaches which can be applied in a standardized way across the network, irrespective of equipment vendor or site location. In relation to the first issue, in [65], members of Chungwha Telecom Taiwan and their academic partner present a view of a proposed high level architecture for an O-RAN Network Management System from the network operator's perspective. The authors describe the architectures for 3 GPP NG- and O-RAN, and provide a list outlining the key Operations and Maintenance (OAM) functions supported by the O-RAN O1 interface. They then provide a high-level layered architecture for an O-RAN-based network manage \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \multicolumn{2}{c}{**Category**} & **Issues** \\ \hline Section 4.1 & Proactive Maintenance & - How to evolve the O-RAN framework in conjunction with ML system design; - To develop ML training approaches across the network irrespective of equipment vendor or site location (i.e., different configurations of multiple vendor equipment and unharmonized data across multiple sites). \\ \hline Section 4.2 & xApps,rApps,dAppsOperation & - Orchestration of xApps, rApps, and dApps in the O-RAN RIC when they are simultaneously operated for network automation; - Orchestration across the domain with SON functions in a core network and any newly added xApps, rApps, and dApps to the RAN. \\ \hline Section 4.3 & SatelliteNTN & - To optimize the resource allocation for capacity enhancement; - To mitigate network interference sources such as adjacent satellite interference and inter-system interference. \\ \hline Section 4.4 & MassiveMIMO & - To provide right interfaces to integrate multi-antenna processing in O-RAN to maximize the spectral efficiency; - To dynamically adjust the level of coordination/cooperation between DUs and to efficiently perform the RU clustering; - To effectively distribute the channel state information between the split baseband functions. \\ \hline Section 4.5 & MobilityManagement & - To jointly optimize the trajectory of UAV and the task offloading among diverse O-RAN elements. \\ \hline Section 4.6 & NetworkManagement & - To optimize the flexible functional split of RAN slices dynamically to respond to changing network environments. \\ \hline Section 4.7 & DataPrivacySecurity & - To make ML models to access and utilize the data without compromising user privacy; - To secure ML models against adversarial attacks and to develop the measure indicating protection against potential data breaches or cyberattacks. \\ \hline Section 4.8 & Big DataCollectionfor ML & - To improve data collection by using O-RAN, including collection and consolidation of hybrid empirical and synthetic data. \\ \hline \end{tabular}
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