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Capgemini : Industry Collaboration is Key to Transform Cloud-Native, AI-Optimized 5G Open Radio Access Networks (O-RANs)

08/08/2022 | 08:25am EDT
Industry Collaboration is Key to Transform Cloud-Native, AI-Optimized 5G Open Radio Access Networks (O-RANs)
Shamik MISHRA 8 Aug 2022
Rome wasn't built in a day. The same can be said of cloud-native, AI-optimized 5G open radio access networks (O-RANs), which are fundamentally different from any previous mobile generation. They are not just evolutionary, but a step change in how radio networks are designed and built.

As a part of the evolution from virtualized RANs (vRANs) to O-RANs, the industry faces a dauntingly steep climb. For a long time, RANs remained outside the boundaries of the transformation achieved through virtualization.

Now, with the O-RAN Alliance defining open interfaces, interoperable cloud-native RAN is a reality. Much like all cloud-native workloads, this requires massive intelligent automation at scale to realize the full economic potential of reduced TCO offered by these open networks. In fact, the step up from cloud-native to AI-optimized networks requires a team effort by vendors, operators, and systems integrators because no single company has all the necessary technologies and expertise to do it alone.

Identifying focus areas and gaps

Open, cloud-native networks have six focus areas:

  1. A cloud-centric architecture for vertical applications and for transforming the operator's BSS
  2. Cloud-native edge compute (MEC)
  3. A 5G standalone (SA) core
  4. Disaggregated O-RAN and cloud RAN
  5. A data-driven autonomous network
  6. A sustainable cloud-native network

Areas four through six require broad industry collaboration to create the common standards, architecture and models that will be critical for test and automation. The i14y Lab is an example of how this collaboration is already well underway. Backed by Capgemini and other major companies, the i14y Lab is focused on identifying and closing gaps in the test and automation space to verify functionality and multi-vendor interoperability.

Currently, there are three major gaps:

  • An open approach to testing that spans all of the domains. This open approach should be automated and API-based, with standardized test benches and performance benchmarks. Collaboration initiatives such as the i14y Lab could provide test tools such as robust, 3GPP- and O-RAN-compliant simulators that can emulate Layer 1, user equipment or base stations.
  • Standardized automation platforms for network operations. These platforms would automate both the network and the alerts that it generates. Besides providing the NOC staff with greater visibility, automation would also maximize their productivity and make the network more predictable, which aligns with one of O-RAN's major goals - to achieve a lower TCO than traditional networks. For example, automation can enable the use of digital twins for multiple RANs, where the NOC monitors the twins rather than the real RAN, thus providing better observability and manageability of the network.

Automation platforms require enormous amounts of data so they can be trained to handle a wide variety of real-world scenarios. Industry collaboration labs such as i14y can play an important role by providing that data.

  • An open orchestration architecture. Driven by open APIs, this architecture would include common data platforms and models. The collected data would be open so developers can build use cases around it.
Achieving Automation at Scale for Cloud-Native O-RAN

Although the migration toward cloud-native, open networks includes the use of commercial off-the-shelf (COTS) IT hardware, it can't simply be used as-is. To meet stringent telecom requirements such as five-nines reliability and RAN functionality, COTS gear requires additional components, such as hardware accelerators. The radio unit must also be managed through the same automation framework. In vRAN, many edge data centers will be utilized to host the baseband functionality of the RAN as a software on such COTS gear. This kind of robust and highly distributed infrastructure - supported with tools for monitoring the network and predicting failures - is key to capturing the interest of systems integrators and infrastructure and cloud vendors interested in the mobile market. Their participation ensures that systems integrators do not have to take on all of the responsibilities and liabilities associated with implementing cloud-native, open networks.

Cloud-native, open networks can generate significant telemetry, which can be leveraged through machine learning operations (MLOps) at scale to develop and test network AI algorithms. This will ensure that the networks will work in a real-world environment. But like COTS IT equipment, the MLOps currently used for cloud workloads will need to be modified to meet telecom's unique requirements.

The remaining challenges

When it comes to industry collaboration to address these gaps, each type of member faces its own unique set of challenges and opportunities.

This technology is highly complex, and the only way that cloud-native, open networks will become reality and live up to their potential is through broad industry collaboration. . The good news is that many operators, vendors and other stakeholders have recognized that need and begun working together to pioneer the future of this industry.

Shamik Mishra CTO of connectivity, Capgemini Engineering


Capgemini SE published this content on 08 August 2022 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 08 August 2022 12:24:03 UTC.

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