# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 6 Challenges and Future Research Directions
### Open RAN Field Issues
Ecosystem integration means the system integrator will be responsible for integrating the entire architecture, including open radios and BBU software. Meanwhile, software system integration is a process that mainly focuses on COTS hardware. In this integration, Open RAN can achieve its automation by using the same DevOps tools and the same CI/CD principles, thus simplifying the integration. Integration and interoperability are critical challenges in Open RAN. Significant effort and collaboration are needed to test the Open RAN system. [PERSON] mentioned that a standard development environment, standard optimization metrics, and standard test and validation methodologies are required to unlock the full potential of Open RAN [44]. It is where TIP, O-RAN Alliance, and other Open RAN standard organizations play an important role in helping mitigate this challenge. Testing methodologies, testing centers, WGs, and plugfests have been created, as discussed in Section 4. Future research should be undertaken in this direction. OSC offers standardization labs called OSC Community Labs [44]. Further information on the OSC Community Lab will be explained in Section 7. A pipeline for designing, training, testing, and experimental evaluation of DRL-based control loops in Open RAN was proposed by [91]. The proposed pipeline is called ColO-RAN. ColO-RAN addressed the development and adoption of DRL in real networks. This problem was caused by the unavailability of large-scale datasets and experimental testing infrastructure. ColO-RAN enables ML research using O-RAN components. The capabilities of ColO-RAN were showcased on Colosseum and Arena testbeds. Performance evaluation of ColO-RAN was performed by developing three xApps to control the RAN slicing, scheduling, and online training of ML models. ColO-RAN and its related dataset will be publicly available to research communities worldwide. The last issue relating to Open RAN implementation is to cross-examine Open RAN's credibility in the promise of a multivendor environment and lower entry barriers for new vendors. Most leading Open RAN software companies have all based their products on FlexRAN, which is x86 dependent. Fortunately, the Open RAN ecosystem is expected to be more diverse. Nvidia's BlueField-3 Arm-based data processing unit will compete against x86 processors. We also have mentioned several other competitors in the previous subsection. Amazon has already used ARM processors in some of its COTS, and other companies will presumably follow soon. As with any other technology, the cellular network market and industry will try every possible solution that gives them better performance and lower prices. ARM claims that its server platform would provide up to 60% lower upfront infrastructure costs, up to 35% lower ongoing infrastructure costs, and up to 80% cloud infrastructure cost savings compared to traditional server [78]. Looking at the performance difference between ARM and traditional servers, inferring they are x86-based, ARM could become more popular. Furthermore, Arm has officially joined the 5G Open RAN policy coalition. Eventually, ARM vendors such as NXP and Qualcomm would emerge. Nevertheless, ARM products presently are more custom-built and are not general processors. In addition, Arm still lacks the flexible industrial-grade virtualization capability that rivals x86. Improving the performance and cost is the common theme of the evolution process of the RAN, as we have explained in Section 1. This improvement process continues endlessly, including the current Open RAN era. One of the problems related to improving O-RAN and Open RAN performance is the resource scheduling process. The resource scheduling process is heavily related to network slicing. We have learned that network functionality is abstracted from its hardware and software components [4].