# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture
### Use Cases  
In this subsection, we will introduce the O-RAN architecture's use cases. The O-RAN use cases are generally divided into category I and category II. The reason for the division is the complexity and prioritization involved. Category I use cases address more general use case and category II addresses more specific use cases where these use cases require specific requirements [67]. These use cases have been explained deeply in one of the O-RAN Alliance's white papers [68]. Several new use cases and updated deployment scenarios of O-RAN architecture are also mentioned in [42]. This subsection will slightly explain these use cases, including those new ones. In category I, there are two key use cases, which are white box hardware design and AI-enabled RAN [68]. For the former, its use case is low-cost RAN white box hardware design. The white box was initially developed by the O-RAN Alliance's WG7 as a method to reduce the cost of 5G deployment, and generally, the whole industry chain [39; 68]. Releasing this white box can also reduce the difficulty of deploying the 5G tech, thus making it more attractive for small and medium enterprises in the telecommunication industry to modify the design [68]. The white-box hardware design is mostly focused in O-DU and O-RU, plus an additional FH gateway. The white box hardware design requires support of O-RAN's O-FHI, O-DU open-source software, and facilitates NFV in the cloud. For AI-enabled RAN, many use cases are described in category I [68]. These are TS, QoE optimization, QoS-based resource optimization, and massive MIMO optimization. TS means a process of mobile load balancing [68]. It is a widely used network solution to achieve optimal traffic distribution based on desired objectives. TS is becoming increasingly important nowadays, as the mobile network has grown rapidly in recent years. Optimizing connection issues such as inefficient, low quality, low QoE, and slow feedback response using a traditional optimization system is no longer the best approach. Because of that, the O-RAN architecture has developed the TS leveraging O1, A1, and E2 interfaces, thus making the network more flexible and agile and reducing network intervention. Another category I use cases for AI-enabled RAN is QoE optimization. As we know, a 5G network can provide several applications with high bandwidth consumption and extreme sensitivity to latency, such as applications in Cloud Virtual Reality (VR) [68]. The current QoE framework can not support such applications because of dynamic traffic volume and fast fluctuating radio transmission capabilities. To reduce this problem, O-RAN Alliance deploys a new QoE framework, a vertical application made for specific QoE prediction and QoE-based proactive closed-loop network optimization. This closed-loop optimization is performed in real-time, involving the software-defined RIC. Before QoE is reduced, the radio resources can be allocated more to users and apps who need the most urgent radio resources. Because of its capability to adapt to users' needs, the QoE is optimized, and the radio resources are utilized more efficiently. QoS-based resource optimization is the next category I AI-enabled RAN use case. The network needs careful planning and configuration to support the diverse 5G services and applications. However, the traffic demand and radio environment are dynamic. Default configuration and planning might not be sufficient to provide highly demanding requirements in extreme situations. QoS-based resource optimization could be used in these situations to optimize RAN resource allocation between users when all user's requirements could not be fulfilled. Analytics function in Non-RT RIC might conclude that it is better to prioritize a group of users through close examination of congestion situations. The last category I use case for AI-enabled RAN is massive MIMO optimization. The MIMO framework has been considered one of the key technologies for 5G [68].