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Mobile core networks can be thought of as the brains of mobile communication. In recent years, these networks have experienced a huge transformation from legacy proprietary hardware to telecom cloud native systems. Today, almost 100% of mobile core networks are deployed based on telco cloud architectures supported by NFV technologies. It mostly consisted of Intelligent networking will most benefit the packet core network networks such as 5GC and UPF which is are responsible for packet forwarding, IMS which help operators support delivery of multimedia communications such as voice, message and video communication of telecommunication operators, and operational functions that support the management of core network including telco cloud infrastructure and 5G network functionalities. F.or those 3 parts of AI evolvement,applications. There have been three areas where intelligent network has shown benefits:
Network Intelligence Enables Experience Monetization and Differentiated Operations
For a long time, operators have strived to realize traffic monetization on MBB(Mobile Broadband) networks. However, there are three technical gaps: not non-assessable user experience, limited or no dynamic optimization, and no-closed-loop operations. To bridge these gaps, there is strong requirement a need for an Intelligent Personalized Experience solution, aiming designed to help operators add experience privileges to service packages and better monetize differentiated experiences. In Typically in the industry, the user plane on the mobile core network usually processes and forwards one service flow using one vCPU. As heavy-traffic services increase, such as 2K or 4K HD video and live streaming, microbursts and extremely large network flows frequently occur. It is, therefore, become the norm, it becomes more likely that a vCPU will become overloaded, causing packet loss. To address this issue, Intelligent AI supported 5G core network can need to be able to deliver ubiquitous 10 Gbps superior experiences.
Service Intelligence Expands the Profitability of Calling Services
In 2023, New Calling was put into commercial use based on a 3GPP specification, it could can enhanced intelligence and data channel (3GPP specification)-based interaction capabilities; it is taking user to a enabling users to use multi-modal communication era communications, and helping operators reconstruct their service layoutconstruct more efficient service layouts. In addition, the 3GPP architecture allow users to control digital avatars through voice during calls, delivering a more personalized calling experience. An enterprise can also calling experience. One example where this can be seen as a business opportunity might be an enterprise using the framework to customize their own avatar as an enterprise ambassador avatar to promote their brandingbrand.
O&M Intelligence Achieves High Network Stability and Efficiency
Empowered by the multi-modal large model, the Digital Assistant & Digital Expert (DAE) based AI technology could reduces reduce O&M workload and improves improve O&M efficiency. It reshapes can reshape cloud-based O&M from "experts+tools" to intelligence-centric "DAE+manual assistance". With Using the DAE, it is possible that up to 80% of telecommunication operators operator trouble tickets can be automatically processed, which is much far more efficient than 100% the manual processing it is for the most part today. DAE also enables intent-driven O&M, avoiding manual decision-making. Before, it usually commonly took over five years to cultivate train experts in a single domain, however, with the multi-modal large model it is now able possible to for it to be trained and updated within just in merely weeks.
Thoth project - Telco Data Anonymizer
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The Thoth project, which is a sub project under the Anuket infrastructure project, has recently focused on a major challenge to the adoption of intelligent networks, the lack of a common data set or an agreement on a common understanding of the data set that is needed. AI has the potential in for creating value in terms of enhanced workload availability and improved performance and efficiency for NFV use cases. This Thoth's work aims to build build machine-Learning models and Tools that that can be used by Telcos Telecom operators (typically by the operations team in Telcos). Each of these models aims is designed to solve a single problem within a particular category. For example, the first category we have chosen is Failure prediction, and we aim the project plans to create 6 models - failure prediction of VMs. Containers, Nodes, Network-Links, Applications, and middleware services. This project also aims to define will work on defining a set of of data models for each of the decision-making problems, that will help both providers and consumers of the data to collaborate.
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