# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 6 Challenges and Future Research Directions
### Open RAN Field Issues
This abstraction leads to the formation of slices, and each slice is given a set of functions, services, and system resources, making it an independent, dynamically created logical entity that can support UEs with multiple needs [92]. However, each slice has been forced to carry out dynamic management due to increased demands. The management process has become critical and more challenging, especially regarding resource scheduling. Because of this reason, the slice needs new mechanisms that need to be developed, which requires a more optimized way of allocating resources. There has been a lot of research that discusses improving resource scheduling and network slicing processes in Open RAN. Recent research by [92] provided a new algorithm in 5G's slices called dynamic scaling multi-slice-in-slice-connected user equipment servicesfor system resource optimized scheduling (DMUSO) algorithm. Like its name, the DMUSO algorithm uses a concept where, in an UE, slices are connected, so this concept is called multi-slice-in-slice connected UEs. This concept connects slices to services and resources to slices, not among slices in a UE. DMUSO algorithm can improve the 5G system performance by learning the service demands, data rates, resources, bandwidths, efficiency, transmission rates, and channel-related SLAs for a user equipment's specific level in network slicing. Results showed that DMUSO achieves efficient and optimized system resource scheduling, with significant performance gains from 4.4 times to 7.7 times compared to other methods and algorithms. Further research related to DMUSO is still open, and [92] still has to analyze the effect of varying user equipment mobility on resource scheduling across slices in the future. The potential significance of AI/ML in O-RAN 5G is believed to lie in its capacity to enhance system performance and improve the overall user experience. A Paper discussed the AI/ML workflow by providing details and illustrating the workflow of AI/ML in O-RAN-based 5G system architecture. The authors present a specific use case focusing on cell load prediction by creating a model through data training using the LSTM algorithm and integrated into xApps on the on the Near-RT RIC. The study shows enhanced performance in predicting the periodic traffic variations [93]. However, the workflow implementation remains incomplete, with training data still requiring offline processing. A comprehensive implementation of the entire workflow is essential to demonstrate the effectiveness of AI/ML methodologies in closed-loop control for optimizing network performance within the O-RAN 5G framework. There is also a resource allocation improvement issue in Open RAN. A resource allocation optimization model that can minimize the cost of updating a RAN infrastructure is proposed by [13]. The model allows the architecture to have a hybrid combination of different hardware and software generations by considering costs involving links, cell sites, and the capacity limit of RAN resources. Another method proposed to solve this resource allocation problem is hierarchical orchestration. Hierarchical orchestration is proposed to address over-simplified resource allocation and limited support for different network segments for the current one-size-fits-all orchestrators in E2E networks [94]. The E2E network is divided into three segments: the RAN segment, the transport network segment, and the core network segment. Every segment has its own distributed orchestrator. These distributed specialized orchestrators enable independent management of each segment. Hierarchical orchestration also introduces a hyperstrator, a higher-level orchestrator to coordinate the distributed orchestrators and deploy the slicing process across multiple network segments. The hyperstrator is a central point, interacting with the whole E2E network. Therefore, hyperstrator has many tasks related to its orchestrators; one of them is to ensure cohesive performance across segments and slices to guarantee consistent QoS of the network slicing.