# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### Use Cases
Aside from being a key technology, the massive MIMO has also become a reason why the 5G technology requires energy more than the 4G [36]. To overcome this problem, O-RAN architecture has tried to optimize the massive MIMO framework. The objective of this optimization is to improve cell-centric network QoS and user-centric network QoE in a multi-cell deployment area [68]. If needed, this optimization can provide a multi-vendor massive MIMO deployment area with multiple transmission points. However, this area can only be provided by MIMO when using specific MNOs. When applying this kind of optimization, O-RAN architecture has several advantages, including the possibility to apply and combine non-RT and near-RT analytics; apply ML; and make decisions for various tasks related to Non-RT and Near-RT RIC. Because of this optimization, the Non-RT and Near-RT RIC can oversee the current traffic, coverage, and interference situation in a whole cluster of cells. When doing this massive MIMO optimization, the O1, A1, and E2 interfaces will give necessary data, policy, and configuration exchanges between two components in the O-RAN architecture. In category II, [68] mentioned several AI-enabled RAN use cases. The first use case for the interfaces is RAN slice service level agreement (SLA) assurance [68]. The network-level slicing occurred in 5G as a process that can provide E2E connectivity and data processing tailored to specific business requirements. These requirements include several network capabilities, such as support for high data rates and low latency. These capabilities are specified based on SLA, which was agreed upon between the MNO and the customer. Mechanisms to ensure slice SLAs and prevent any violations are becoming more popular. By combining the AI and ML models into SLA assurance mechanisms through Near-RT and Non-RT RIC, O-RAN Alliance can raise the possibility of SLA slice assurance being fulfilled. This importance of SLA assurance mechanism can lead to further research. Network slicing process still has so many opportunities to be explored by MNOs, thus making the network more efficient and changing the way how MNOs do their telecommunication business. Another category II AI-enabled RAN use case is context-based dynamic handover management for vehicle-to-everything (V2X). Nowadays, the application of V2X concept in real life has increased rapidly. Because of this rapid increase, the V2X applications can be found easily, such as in self-driven vehicles, seat entertainment, and traffic assistance. These applications of V2X can increase traffic safety, reduce emissions, and save more time when riding [68]. The V2X works based on real-time information about the driver, the road, and the traffic conditions. Regarding these V2X applications, the O-RAN architecture can do several things, such as collecting and doing maintenance of the past traffic and radio conditions, deploying and evaluating AI and ML-based applications that can detect or predict abnormal users' behavior, monitoring the traffic and the radio real-time condition, and deploying real-time xApps that predict and prevent anomalous situations in UEs. The AI-enabled RAN can also be used in flight path-based dynamic UAV resource allocation use case. Like V2X, the application of UAV has been applied in many industries, such as agricultural plant protection, power inspection, police enforcement, geological exploration, and environmental monitoring [68]. The 5G technology can provide a high-speed network for several applications that need low-altitude UAVs. In O-RAN architecture, the Non-RT RIC can provide necessary information about the aerial vehicle and everything around that vehicle, including flight path information, climate information, flight limitation area, and space load information. To provide such information, the RIC can deploy Unmanned Traffic Management (UTM) for constructing and training AI and ML models that should be applied in RAN.