How AI and ML in Open RAN mitigate network complexity
New technologies, along with the complex distributed nature of most network environments, have complicated network management. Ironically, to overcome this complexity, some companies are integrating even more technologies, including AI and machine learning, or ML, into their network architectures.
Businesses aren’t the only ones facing this dilemma with network management. Mobile Network Operators (MNOs) are also experiencing complexity within their networks, including with the deployment of the 5G network. But MNOs are evaluating new technologies to simplify operations.
One of the ways operators are trying to reduce complexity is by using an open radio access network (Open RAN). With the Open RAN architecture, operators use non-proprietary equipment to improve interoperability, reduce costs and increase programmability.
In a recent webinar, “How to Automate AI/ML and Open RAN Predictive Operations,” Brian Walsh – Product Marketing Manager at Parallel Wireless – discussed how MNOs can benefit from using AI and ML in Open RAN, with the cellular to solve complex operations.
“Open RAN and new AI and ML approaches can help mobile operators optimize operations across their networks, while reducing cost and complexity,” Walsh said.
According to Walsh, MNOs are under pressure to scale their business operations to increase profits and differentiate themselves from competitors. In addition to these challenges, new technologies like 5G have given end users higher expectations for the quality of service than they can expect from providers.
Some of these expectations include the following:
- low latency
- network modernization
- quality and consistency
“Mobile operators must find new ways to increase their margin and improve network operational efficiency with a more flexible and agile service delivery environment, while reducing both Capex and Opex,” Walsh said. .
But providing these network services increases both cost and complexity, Walsh said. For example, using 5G and previous generations of cellular connectivity – such as 3G and 4G – to support access networks requires higher RAN capacity and denser networks.
Network densification – the process of adding cell towers to increase a network’s capacity – can improve connectivity and reliability, among other benefits. However, the challenge of densification is that it is difficult for operators to obtain permission to install new cell sites. But 5G networks should be 100 times denser than 3G networks, and densification is key to deploying 5G and reaping its benefits, Walsh said.
AI and ML in Open RAN can enable self-optimization
Open RAN architectures allow operators to provide network self-optimization capabilities, which use automation to manage a network more efficiently. Walsh described the following four components that help improve network efficiency:
- AI and ML
Network task automation and zero-touch provisioning can simplify network operations and management, Walsh said. Operators use automation to increase the scale of their operations without employing more staff.
One of the advancements of the Open RAN architecture is the incorporation of ML frameworks. For example, the RAN Smart Controller allows operators to programmatically control the RAN in both real-time and non-real-time.
Open RAN applications support ML models, which automate the network and make data-driven decisions. According to Walsh, third-party organizations or MNOs can develop the Open RAN applications.
4. AI and ML
AI and ML predictive models use algorithms to process data by analyzing previous and current data events and finding patterns. Implementing these tools and automation in Open RAN architectures helps eliminate human error and represents a significant advancement in the networking industry, Walsh said.
Open RAN deployment challenges
Ideally, according to Walsh, the combination of the above four components results in the creation of intelligent, self-optimizing networks.
But skepticism about Open RAN remains, despite the use cases. Similar to AI and ML, Open RAN is a recent development in the networking world, and MNOs are reluctant to implement it. A study of mobile world live, in collaboration with Aspire Technology, surveyed 370 operators from various industries and found that operators are divided on the deployment of Open RAN.
Much of this reluctance stems from operators being uninformed about the technology. An estimated 57% of respondents said they needed to know more about Open RAN or didn’t know the technology at all. Eightfold AI’s research reaffirms this; According to a study drawn from a dataset of 500,000 telecommunications employee profiles, 33% of network and operations engineer positions are not equipped to offer innovative offers. Some of these offerings include emerging network technology trends, such as 5G and Open RAN.
The future of AI and ML in Open RAN 5G
Experts predict that the Open RAN market will explode as technology develops, and skill and knowledge gaps are likely to narrow in that time frame. The majority of respondents said Mobile world live they believe that Open RAN still needs two to five years of maturation before large-scale deployments can take place. More vendors may also have services to integrate Open RAN 5G with AI and ML at that time.
Parallel Wireless, for example, currently offers an Open RAN cloud architecture that supports 5G and previous cellular generations, coupled with AI and ML frameworks, to help operators modernize their networks. However, Parallel Wireless is not the only vendor making progress in this area. Other industry players are beginning to make progress in automating Open RAN-enabled 5G networks.
In February 2022, Anuta Networks unveiled ATOM, its standalone 5G network configured with Open RAN as a service. According to Anuta, ATOM integrates AI and ML predictive analytics tools, among other features. At the end of 2021, Ericsson announced its Intelligent Automation Platform, a service that adds automation capabilities to traditional networks and 4G and 5G RAN – Open RAN included.