# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 6 Challenges and Future Research Directions
### O-RAN Alliance Architecture Issues  
In this subsection, we will mention some issues regarding the O-RAN Alliance architecture and also its implementation in OSC. A common problem that occurs upon the entire O-RAN building blocks is that the standard specifications are not finished being written yet by the O-RAN Alliance. We have mentioned several nodes that still have not complete specifications in Section 4, which are SMO's Non-RT RIC ML, O-FHI M-Plane, O1 interface, and O2 interface. OSC's code development for the currently available specifications is also not finished yet, as mentioned in Section 4. Some of them have not or have just started like the O-Cloud. Some of them are postponed so that OSC can focus on developing other nodes, such as the O-CU and the O-DU MAC SCH. Others are waiting for the official specification release, such as the M-Plane. SMO has an issue regarding copyright with 3 GPP. Since some of the O-RAN specifications also reference 3 GPP specifications, the occurrence of this issue is inevitable. Currently, this copyright issue focuses on the OAM part. However, O-RAN Alliance WG1 also plans to resolve this issue on the overall O-RAN architecture level. Besides these implementation problems, SMO also faces some design challenges and future research directions that we will explain in the following paragraphs. Network densification causes the management and optimization of neighboring cell relationships more complex. O-RAN-based proactive automatic neighbor relations (ANR) optimization is a well-known application of SON that is used to manage neighbor cell relationships, or called neighbor cell relation table (NCRT) [69]. An ANR technique is proposed that identifies the trends before handover failure, marks the cells for handover prohibition, and identifies time-based trends to remove and add back the cells in the NCRT table by [69]. The ML-embedded ANR model is made as an rApp. The rApp has tasks to give several data updates to its neighbor cell. These data will also be used by rApp to improve default cell removal and prohibit any possible handover policies by generating a new policy. The rApp will send the new policy to xApp in Near-RT RIC. After that, the xApp executes the policy on Open RAN network nodes. This design will minimize the handover failure, reduce the operational cost, and thus increase the network's performance and QoS. One of the problems in network optimization/automation is that even though the objective is clear, there currently needs to be a practical way to select which intelligence model should be deployed, where to deploy it, and which RAN parameters to control. A rApp called OrchestRAN is proposed [70]. The OrchestRAN framework offers to solve this. MNOs can directly specify high-level control/inference objectives, and OrchestRAN will automatically compute the optimal set of algorithms and where to execute them. OrchestRAN prototype is tested on the Colosseum. Experiment results show that OrchestRAN can instantiate data-driven services on demand at different network nodes and time scales. OrchestRAN successfully achieved minimal control overhead and latency. Another method proposed by researchers for SMO is to improve the RRM. The proposed intelligent RRM scheme is called cell splitting. The application of cell splitting is made by [71], where the cell splitting is involved in the Open RAN architecture to prevent cell congestion. Long short-term memory (LSTM) recurrent neural network (RNN) is utilized to learn, thus making several predictions about the traffic pattern that could arise from a real-world cellular network in a densely populated area. From these traffic patterns, LSTM will detect any cell that may be congested. LSTM model is trained at Non-RT RICusing long-term data gathered from the RAN.