Automation
A key benefit of cloud native networks is the ability to automate the network, increasing efficiency and lowering the total cost of ownership. It is also a major challenge of O-RAN, as it requires many different technologies to work in harmony, like the conducting of an orchestra to deliver a piece of music that is more than the sum of its parts. Intelligent automation turns Open RAN from a resource-intensive challenge into a source of value. But much must be done for automation to reach its full potential.
First, why automate? Some of the benefits include:
The ability to deploy in multi-vendor RAN environments consisting of complex multi-technology networks, where the automation platform can create the greatest operational impact.
Lower operational costs, thanks to the automation of network deployment and network operation, leveraging new automation rApps and xApps (specialized software apps for network automation) deployed over O-RAN service management and orchestration (SMO).
The ease of harnessing proven operational models of legacy RAN applications, by modernizing to cloud-native services on a nRT (near real time) and nRT RIC platform.
The ability to deploy RAN automation across multi-technology networks, using design patterns in alignment with the O-RAN Alliance, also providing future-proof flexible automation across varying technology and vendors. The level of automation can also be adjusted in the network’s constituent layers to varying degrees – for example, a high level of automation on RAN compute and connectivity infra, and a medium level in radio resource management.
Solving the riddle of efficient Open RAN automation is only the beginning. With reliable RAN, telcos can create new innovative services, like network slicing, to create customized networks for different applications and data needs, and open and closed loop service assurance.
Even a few years ago, the complexity of trying to link multiple software systems would have been unthinkable. Today, disaggregated RAN is not only possible – it provides a very real competitive edge. The problem is, Open RAN requires a level of intelligent automation that is difficult to build completely in-house.
Telcos know that they must move towards an automated and efficient network if they want to support agile service innovation and delivery on a competitive level. But at present, automation has only reached varying levels of maturity across the network span. For many telcos, RAN automation is still limited to discrete trials with small groups of vendors; it’s mostly experimental, and its scalability remains unproven.
Radio networks are inherently complex. Add to that business requirements that mandate compatibility between new, next-gen networks and legacy technologies, and that complexity multiplies.
Operators find themselves facing two options: automation that’s fairly easy to implement, but limited in scope, or automation that links entire networks, but must be custom built, which typically requires some help from outside software experts.
Add to that the steadily increasing number of sites, plus the need to keep software expenditures and OPEX in check, and that complexity becomes a serious obstacle.
5G generates a flood of data that – for all the reasons listed above – creates some very real challenges for operators. These data need to be classified and prioritized for effective network control and management to be possible.
The solution? Classic automation is not enough. Open RAN needs intelligent automation. This requires the RAN intelligent controller.
The RIC is a software-defined component designed to optimize the Radio Access Network (RAN).
The RIC has an essential 5G network architecture element responsible for facilitating network intelligence, flexibility, and optimization. Separating control plane functions from the base station hardware and centralizing them in a software-defined and cloud-native environment greatly enhances the RAN’s capabilities.
The RIC addresses several challenges of deploying and operating 5G networks. These include:
5G networks introduce complex requirements, like high data rates, low latency, and massive device connectivity. RIC employs real-time network data collection, analytics, and optimization algorithms to optimize the network dynamically. It can intelligently allocate radio resources, manage interference, and adjust network parameters to ensure efficient resource utilization and optimal network performance.
Efficient traffic steering has long been a challenge in wireless networks, and 3GPP and other organizations have proposed numerous solutions. The key challenge is that typical traffic steering schemes use the radio conditions of a cell by treating all users of that cell in the same way. Hence, these schemes can only adjust cell priorities and handover thresholds. RIC can handle traffic steering by adopting UE-centric strategies and ensuring proactive optimization, by predicting network conditions and allowing mobile network operators (MNOs) to specify different objectives for traffic management, depending on the scenario. It can then flexibly configure optimization policies.
Traditionally, RAN solutions have been proprietary and vendor-specific, leading to limited interoperability and vendor lock-in. The RIC architecture promotes an open ecosystem through standardized interfaces, such as the E2 interface, O1 Interface, etc., which O-RAN specifications define. This enables multi-vendor interoperability, allowing operators to combine components from different vendors. The RIC, combined with SMO, acts as a control and management layer, abstracting the underlying hardware and facilitating seamless integration of diverse RAN elements.
As a software-defined component, the RIC adds flexibility and scalability to the RAN. It allows operators to create virtual network instances tailored to specific applications or user groups through dynamic network slicing. By managing resources centrally and orchestrating them dynamically, the RIC helps efficiently allocate network capacity based on demand, ensuring scalability and adaptability.
Operators have to meet stringent energy efficiency goals by 2030. RIC xApps and rApps are developing innovative solutions that help reduce energy bills. Capgemini’s energy-efficiency rApp/xApp is being deployed under trial in an operator-live network in Europe, and it has already shown more than 10% savings in energy. The xApp brings us closer to reaching the EU Green Deal goals and, in the process, also saves operational costs for operators who spend billions of dollars every year on energy bills.
Network slicing is a fundamental concept in 5G that enables the creation of virtual network instances with tailored characteristics. The RIC applications (xApp/rApp) act as key components in managing network slicing, by orchestrating the allocation of resources, enforcing service-level agreements, and dynamically adjusting network parameters. This enables operators to offer differentiated services, allocate resources on demand, and support diverse use cases, while ensuring isolation and quality of service.
As 5G networks become more complex, automation is essential for efficient operations. RIC utilizes advanced analytics, machine learning, and AI techniques to automate network optimization, resource management, and fault detection. It can detect network anomalies, proactively address issues, and even self-heal by dynamically reconfiguring the network to ensure continuous service availability and quality.
The heart of Capgemini’s OpenRAN Operations Automation solution lies in a set of RAN applications driven by our NetAnticipate AI-Model platform. To address the issues of automation in real time – when millions of impulses are streaming through networks and each one must be routed correctly and immediately – something more than standard automation is required.
The innovative solution we’ve created uses the near Real-Time RIC (nRT-RIC) model. This is based on an extendible, abstracted architecture, that enables easy integration of multi-vendor xAPPs on nRT-RIC.
So, whatever vendors an operator is working with, the same powerful AI is able to handle the traffic. RAN-specific AI models that learn with no supervision can make O-RAN NonRealTime RIC implementation possible. The result is a complex network that essentially runs on autopilot.