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?
Jason Hunt (at least on how foundation models can be applied to network data)
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