The Power of Automation in Open RAN Networks

Analytics will be deployed as rAPPs as part of the Non-real time RIC and will utilize big data to provide an overall view of the network conditions.
scheduling them for upgrades when a crew is available to go on site and upgrade manually.

Automation of testing and upgrades

implementing a CI/CD model in the telecoms industry helps to migrate the testing, integration, software release, and actual software deployment of the RAN from manual fieldwork to automated and remote deployment. Manual on-site upgrades are subject to mistakes, and the maintenance window is short. With automation, mistakes are eliminated, and the time window gets expanded.

With CI/CD the fast delivery cycle makes it easier for developers to work as the business requires. Kubernetes makes finding faulty code easier, meaning it can be reverted or fixed much faster without impacting the business. If there is an issue with infrastructure, automation will enable moving the application to another data center, edge or centralized, depending on the application. Rollbacks for application or container failing are automated, so the latest stable version is always available, minimizing downtime and any impact on the end user.

Artificial intelligence (AI) and machine learning (ML)

In a recent Omdia survey, 80 percent of mobile operator responders stated that they plan to use AI to automate network operations starting in 2021 and beyond. AI coupled with ML will be the main tools to guarantee the quality of network performance and the quality of the resulting end-user experience across ALL Gs.

AI will be responsible for analyzing data and using ML algorithms to adjust network conditions, provide proper load balancing, ICIC, and managing handoffs seamlessly—all to ensure the subscriber gets the best experience possible.

All data sources, as in big data, will need to be considered to first classify the data, then second to recognize the pattern or abnormality, then third to predict the behavior. As time progresses, ML algorithms will evolve and become better at predicting and helping AI to make real-time network decisions. This will be critical for 5G when humans and things will be connected.  

Any AI can only be as good as the data that goes into it. The data will need to cover different use cases, which need to be supported and include data from different vendors across not only all components of the RAN, but also the overall network. This is where openness will play a critical role and where the ecosystem must be created.


Analytics is a tool to see and understand what’s going on in the network and how those changes affect the subscriber experience. Analytics will provide a visual representation of patterns or abnormalities and will help a mobile operator to understand what needs to be corrected to improve network performance for a better subscriber experience. It’s an opportunity to review the AI data and see reports on how ML is improving the network.

Analytics will be deployed as rAPPs as part of the Non-real time RIC and will utilize big data to provide an overall view of the network conditions. There will be a need for better openness and better APIs between vendors that enable that data mining.


A clear automation strategy and defined processes across CI/CD, ZTP, AI/ML, and analytics will help mobile operators to move into a fully automated RAN world, which is key when RAN components come from different vendors as with Open RAN.

The scope of work is the same as with legacy RAN; what is different is the number of vendors that will be a part of the Open RAN ecosystem. With automation of configuration with ZTP and automation of ongoing maintenance with CI/CD, AI/ ML will help mobile operators to realize the promise of Open RAN to avoid vendor lock-in while Increasing efficiency, providing better resource utilization, and driving down overall TCO.


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