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Title: Intelligent Networking, AI and Machine Learning for Telecommunications Operators

Challenges remainProgress at last, but making progress in all areaschallenges remain


Table of Contents:

1  Executive Summary - Key Takeaways and Overview

0.25 page – Beth Cohen  will fill in when the paper is mostly complete.

2  Background Intro/History Beth Cohen 

...

Executive Summary - Key Takeaways and Overview

0.25 page – Beth Cohen  will fill in when the paper is mostly complete.

Since publishing  Intelligent Networking, AI and Machine Learning While Paper in 2021, the telecom industry has seen tremendous growth in both interest and adoption of the 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 the state of the art in 2021.  Intelligent networking is coming into its own as telecoms increasingly use it for operational support, whither that means deploying intelligence into their next generation 5G networks, where it is used for ?? and it continues its spread in operations.

Key Takeaways

  • Some intelligent networking has been deployed by Telecoms, but more research and development is needed to establish industry wide best practices.
  • Currently the industry is more interested in using artificial intelligence and machine learning to address operational, maintenance, and service assurance issues over network optimization. 
  • The Telecom industry needs to develop common AI platforms and intelligent networking frameworks and methodologies to support the delivery of new services quickly and efficiently. 
  • A shared understanding of intelligent networking will help to support interoperability.
  • Creating commonly understood sources of reliable data is difficult, both within a company across business units and across the Telecom industry
  • The Open Source community can play a key role in furthering the development of these frameworks and best practices. Some projects that look promising include, Anuket Thoth, O-RAN, 3GPP SA5 and ITU-T standards organizations, as well as the ONAP, O-RAN and TIP open-source projects.


2  Background Intro/History Beth Cohen 

0.5 page

Telecoms need to be able to incorporate new technologies and next-gen connectivity such as 5G, to customers and end users.  To achieve these ambitious goals, they need to optimize their networks – make them more intelligent if you will.  Some of the tools needed include artificial intelligence (AI), machine learning (ML) and artificial intelligence operations (AIOps). This document will explore what intelligent networking means to telecoms, vendors and customers, and how AI and ML technologies and tools can be used, the cultural shifts the industry needs to make it a success, and what to bear in mind when deploying machine learning across a telecom network.

The LFN (Linux Foundation Networking) End User Advisor Group (EUAG) is publishing this document to identify and highlight the latest thinking and recommendations for building and supporting intelligent networking and the tools needed to achieve it.  It will touch on the state of automation and adoption of intelligent networking tools by telecom operators.  This is a new area for many in the telecom industry, so the focus will be on the requirements, tools and approaches that have been deployed, and some potential futures for intelligent networking and AI/ML tools.  Some of the topics covered will include:

  • Fundamental issues and challenges that might be solved by applying Intelligent networking technology
  • Establish a definition of intelligent networking
  • The promotion of intelligent networking transformations
  • Results from a February 2021 survey of telecom operators and vendors in the industry ecosystem about the current state of industry adoption
  • How intelligent networking might be incorporated into networks and processes to improve operations and ensure that the solutions work as expected in production networks environments.
  • Recommended approaches and the potential for open source projects to contribute to the next generation of intelligent networking tools.

Beth Cohen  can we use drivers for AI side bars using following :

...

  • l  Core Network Hui Deng ChangJin Wang 
    • Mobile core network is kind of central  or brain of mobile communication, it is the only and largest part which experienced the transformation from legacy propritary hardware into telco cloud native, almost 100% of today mobile core network has been deployed based on telco cloud architecture by NFV technologies. It mostly consisted of packet core network such as 5GC and UPF which is responsible for packet forwarding, IMS which help operators multimedia communications such as voice, message and video communication of telecomunicaiton operators, and the management of core network including telco cloud infrastructure and 5G network functionalities.  F.or those 3 parts of AI evolvement,

    • Network Intelligence Enables Experience Monetization and Differentiated Operations

      For a long period of time, operators have strived to realize traffic monetization on MBB(Mobile Broadband) networks. However, there are three technical gaps: not assessable user experience, no dynamic optimization, and no-closed-loop operations. To bridge these gaps,  there is strong requirement for Intelligent Personalized Experience solution, aiming to help operators add experience privileges to service packages and better monetize differentiated experiences. 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 elephant flows frequently occur. It is, therefore, more likely that a vCPU will become overloaded, causing packet loss. To address this issue, Intelligent AI supported 5G core network can 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 3GPP specification, it could enhanced intelligence and data channel (3GPP specification)-based interaction capabilities; it is taking user to a multi-modal communication era and helping operators reconstruct their service layout. In addition, 3GPP architecture allow users to control digital avatars through voice during calls, delivering a more personalized calling experience. An enterprise can also customize their own avatar as an enterprise ambassador to promote their branding.

    • 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 O&M workload and improves O&M efficiency. It reshapes cloud-based O&M from "experts+tools" to intelligence-centric "DAE+manual assistance". With DAE, 80% of telecomunication telecommunication operators trouble tickets can be automatically processed, which is much more efficient than 100% manual processing as it used to be. DAE also enables intent-driven O&M, avoiding manual decision-making. Before, it usually took over five years to cultivate experts in a single domain, however, the multi-modal large model is now able to be trained and updated within just weeks.

  • l  Bearer & DC Network - Cisco?
  • l  Cross domain network AI platform Lingli Deng //moved to 4.3.1
  • l  Telecom Cloud Hui Deng 

...

4.3.2       issues remained/added/aggravated Lingli Deng 

...

  • AI trustworthiness
  • non-economic margin cost Compared with traditional data-driven dedicated AI/ML models in specific scenarios, the R&D and operation of large language models has higher requirements for pre-training data scale, training computing power cluster scale, fine-tuning engineering and other resource requirements, energy consumption, management and maintenance, etc. It is bound to become a playground for the few. How to open up the R&D, application, operation and maintenance chain of large models for the communications network industry so that it can serve the general public may become a stepping stone for operators to realize high-end autonomous networks.
  • TBA

...

  • Collaborate on Network Super Blueprints or develop new ones:

https://wiki.lfnetworking.org/x/ArAZB


  • Join LFN AI mailing list:

...