# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 3 Open RAN Movement and the Related Technologies
### Open RAN Movement  
The growing cellular network market demand forces MNOs to optimize each network component, including the RAN. Several challenges should be addressed to deploy a good and reliable RAN. One of them is how to meet specific QoS requirements. 5G introduces new three use cases domain, namely Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine Type Communication (mMTC) [17]. Every application and every wireless device has its own specific QoS requirements [1]. The existing monolithic RAN could not satisfy the diverse requirements of these use cases. Centralizing all use cases on a single network due to the limitations of the monolithic RAN would reduce both QoS and QoE [18]. Network upgrade for enhanced flexibility and adaptability is essential to meet different QoS requirements for each application. This flexibility can only be achieved by disaggregating RAN components [1]. It enables the RAN to behave differently to each application's particular QoS requirement, leading to a more intelligent and versatile RAN. RAN's architecture should support multiple levels of QoS in handoff scenarios at one time [15]. Another challenge for RAN is combining the existing NFV with artificial intelligence (AI). The trend of NFV has existed since the development from C-RAN to vRAN. However, the virtualization concept proposed in vRAN is complicated, needing mechanisms for management and orchestration [2]. Management and orchestration can be performed by leveraging AI. RAN needs NFV with AI embedded in it. As we know, AI has impacted the computing and networking world significantly until today [19]. AI can be used to analyze the enormous amount of RAN-generated data. Then, the information gathered can be used to anticipate problems in the network and take necessary action to ensure network QoS is delivered to the users. AI can make the NFV system more intelligent, optimized, and improved. Further explanation about NFV can be seen in the next subsection. It has already been stated that RAN's improvement is happening by introducing cloud-native principles. We shall develop the cloud-native approach by improving RAN's software and hardware. The previous generations of RAN had a similar weakness: MNOs needed more control over their software and hardware innovation. Before Open RAN, RAN's software and hardware were proprietary and remained optimized to one vendor only [16]. It caused an economic monopoly with extreme overhead and led to unsupervised systems because the only vendor that could supply the hardware and software was that one vendor. MNOs depend heavily on vendors to provide innovation and new features on RAN technologies. Let us take AI and NFV in the previous paragraph as an example. AI-embedded software is needed to complete the NFV advancement process. However, MNOs could not implement AI-embedded NFV because they depend on whether their partner vendors offer this solution. So, before MNOs can freely innovate their services, they must regain control of their network components. The remaining challenge for RAN involves the need for efficient interconnection among components within the network architecture [18]. It was caused by the uneven standards used for cellular networks around the globe due to the proprietary interfaces used. Because of these different characteristics of each vendor's interfaces, hardware and software from different vendors could not interconnect easily. This led to plenty of waste of wireless infrastructures and spectrum resources. When MNOs try to ignore these differences and treat all the cellular networks the same, their users will experience low QoS and QoE of the network. Another fundamental pillar of Open RAN is disaggregation. Generally, disaggregation means the software is detached from its hardware [16]. Vendor develops their software to make it compatible with hardware from different providers. Through disaggregation, RAN software can run in general-purpose processors in COTS hardware, further extending its interoperability.