Title: Intelligent Networking, AI and Machine Learning for Telecommunications Operators
Subtitle: Great progress in Significant industry adoption progress, but challenges remain
Authored by Members of the Linux Foundation AI Taskforce
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Muddasar Ahmed Muddasar Ahmed (Mitre)
LJ IlluzziLJ Illuzzi (Linux Foundation Networking)
Hui Deng Hui Deng (Huawei)
ChangJin Wang Chang Jin Wang (ZTE)
Beth Cohen can we use drivers for AI side bars using following :
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Since LFN published the seminal Intelligent Networking, AI and Machine Learning While Paper in 2021, the telecom industry has seen tremendous growth in both interest and adoption of Artificial Intelligence and Machine Learning (AI/ML) technologies. While it is still early days, the industry is now well past the tire kicking, lab testing phases that was then the state of the art. Intelligent networking is coming into its own as telecoms increasingly use it for operational support; whether that means deploying intelligence into their next generation networks, or for automation of network management tasks such as ticket correlation and predictive maintenance. LFN and Open Source have a pivotal role to play in fostering and developing intelligent networking technologies through the continued support of key projects, ranging from building a common understanding of the underlying data models to developing infrastructure models and integration blueprints. Link to some of the projects like Anuket Thoth, Super Blueprint and others that are related to AI/ML
The future of Intelligent Networking and AI is in the hands of the individuals and organizations who are willing and able to contribute to new and existing projects and initiatives. If you are involved in building and operating networks, developing network technology or consuming network services, consider getting involved. Engaging with the LFN projects and communities can be an educational and rewarding way to shape the future of Intelligent Networking.
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Beyond typical chatbot-style applications, LLMs have been extended to generate code [16][17][18][19], solve math problems (stated either formally or informally) [20], pass science exams [21], or act as incipient "AGI"-style agents for different tasks, including advising on investment strategies, or setting up a small business [22]. Recent advancements to the basic LLM text generation model include instruction finetuning [23], retrieval augmented generation using external vector stores [24][25], using external tools such as web search [26], external knowledge databases or other APIs for grounding models, code interpreters [27], calculators and formal reasoning tools [28]. Beyond LLMs and NLP, transformers have also been used to handle non-textual data, such as images, sound and arbitrary sequence data.
Intelligent Networking
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Differences
A natural question arises on how the the power of LLMs can be harnessed for problems and applications related to Intelligent Networking, network automation and for operating and optimizing telecommunication networks in general, at any level of the network stack. Datasets in telco-related applications have a few particularities unique to the industry. For one, the data one might encounter ranges from fully structured (e.g. code, scripts, configuration, or time series KPIs), to semi-structured (syslogs, design templates etc.), to unstructured data (design documents and specifications, Wikis, Github issues, emails, chatbot conversations).
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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% the majority of mobile core networks are deployed based on telco cloud architectures supported by NFV technologies. Intelligent networking will most benefit benefits the packet core networks such as 5GC and UPF which are responsible for packet forwarding, IMS which support delivery of multimedia communications such as voice, message and video, and operational functions that support manage the management of core network itself including telco cloud infrastructure and 5G network applications. There have been three areas where intelligent network has networking have 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: non-assessable user experience, limited or no dynamic optimization, and no-closed-loop operations. To bridge these gaps, there is a need for an Intelligent Personalized Experience solution, designed to help operators add experience privileges to service packages and better monetize differentiated experiences. Typically in the industry, the user plane on the mobile core network 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 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 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 can enhanced intelligence and data channel (3GPP specification)-based interaction capabilities; it enabling users to use multi-modal communications, and helping operators construct 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. One example where this can be seen as a business opportunity might be an enterprise using the framework to customize their own enterprise ambassador avatar to promote their brand.
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 reduce O&M workload and improve O&M efficiency. It can reshape cloud-based O&M from "experts+tools" to intelligence-centric "DAE+manual assistance". Using the DAE, it is possible that up to 80% of telecommunication operator trouble tickets can be automatically processed, which is far more efficient than the manual processing it is for the most part today. DAE also enables intent-driven O&M, avoiding manual decision-making. Before, it commonly took over five years to train experts in a single domain, with the multi-modal large model it is now possible to for it to be trained and updated in merely weeks.
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