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

Challenges remain, but making progress in all areas

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

0.5 page

1.1 Autonomous Network

1.2 LLM & GenAI

1.3 LFN & Previous AI WP

3  Motivation

1 page

1.1 traditional CSP pain points

l  Network Automation - reduce expenditure

l  Business Innovation - increase income

1.2 emerging CSP honey pots

l  Converged Infrastructure for the era of AI

l  Converged Service for the era of AI

4  Problem Statement

3 pages

4.1 network AI explorations so far

1.5 page

l  Radio Access Network ChangJin Wang 

l  Core Network Hui Deng ChangJin Wang 

l  Bearer & DC Network - Cisco?

l  Cross domain network AI platform Lingli Deng 

l  Telecom Cloud Hui Deng 

4.2 challenges towards fully autonomy Lingli Deng Andrei Agapi 

0.5 page

l  high quality structured data

l  AI trustworthiness

l  non-economic margin cost

l  TBA

4.3 Network LLM: the game changer?

1 page

4.3.1       issues solved/mitigated Andrei Agapi 

l  high quality structured data

l  AI trustworthiness

l  non-economic margin cost

l  TBA

4.3.2       issues added/aggravated

l  high quality structured data

l  AI trustworthiness

l  non-economic margin cost

l  TBA

5  How could Open Source Help?

2-3 pages

5.1 Common Vision: intelligence plane for 6G

1 page

l  natural intent interaction

l  cognitive smart orchestration

l  real-time meta-network verification

l  built-in knowledge open loops

5.2 Related Open Source Landscape

1-2 pages

l  Network communities

l  AI communities

l  Integration BPs

l  Verification programs

6  Call for Action

0.25 page