The End Of Knowledge - Vault 4/83 - AI For Good - 2023 - Machine learning in communication networks

graph LR classDef ai fill:#f9d4d4, font-weight:bold, font-size:14px classDef concepts fill:#d4f9d4, font-weight:bold, font-size:14px classDef deployment fill:#d4d4f9, font-weight:bold, font-size:14px classDef 6g fill:#f9f9d4, font-weight:bold, font-size:14px classDef collaboration fill:#f9d4f9, font-weight:bold, font-size:14px classDef future fill:#d4f9f9, font-weight:bold, font-size:14px A[Machine learning in
communication networks] --> B[AI and ML rapidly
evolving, changing communications. 1] A --> C[Key concepts: AI native 6G,
open data ecosystems. 2] A --> D[Importance of open source
in AI-enabled 6G. 3] A --> E[Shift from AI-based to
AI-native networks. 4] A --> F[MLOps enables streamlined ML
development in telecom. 5] B --> G[Semantic communications, large models,
smart interfaces. 6] B --> H[Mathematical approach optimizes 5G
without extra resources. 7] B --> I[Four key 6G enablers: spectral
awareness, world models. 8] B --> J[Graph neural networks for
network digital twins. 9] B --> K[Cognition, autonomy key
to 6G redesign. 10] C --> L[AI enables trustworthy
5G applications. 11] C --> M[Deep learning advances physical
layer in 6G. 12] C --> N[China Mobile pursuing
AI in 5G. 13] C --> O[Hierarchical RIC architecture,
RAN-DAF proposed. 14] C --> P[6G needs AI-native design,
data-centric architecture. 15] D --> Q[Multimodal sensing, digital twins
for 6G challenges. 16] D --> R[Reinforcement learning for efficient
offloading decisions. 17] D --> S[Deep learning improves
5G network planning. 18] D --> T[Challenges for deep
learning in 6G. 19] D --> U[Generative AI automates
network solutions. 20] E --> V[FGAN includes exploratory evolution
subsystem. 21] E --> W[Wi-Fi and 6G coexistence
crucial. 22] E --> X[Dedicated efforts for
AI in Wi-Fi. 23] E --> Y[IEEE task group on
AIML for Wi-Fi. 24] E --> Z[ITU-T Y.3172 framework for
ML pipelines. 25] F --> AA[ITU AI/ML in 5G
Challenge collaboration. 26] F --> AB[UAE national AI strategy,
Ministry of AI. 27] F --> AC[AI adoption challenges
in UAE. 28] F --> AD[LiFi, laser-based FSO
for ultra-high bandwidth. 29] F --> AE[Mina Sandbox for AI/ML
collaboration. 30] G --> AF[Unify AI/ML initiatives
under digital strategy. 31] G --> AG[AI/ML evolution: embedded intelligence
to AI-native. 32] G --> AH[AI-native 6G needs standards,
testbeds. 33] G --> AI[Technology 20% of AI/ML success,
80% culture. 34] G --> AJ[Challenges: AI/ML expertise, MLOps,
data governance. 35] H --> AK[Change management for
AI/ML adoption. 36] H --> AL[Standardization for AI/ML solution
management. 37] H --> AM[Academia-industry-government collaboration
for AI/ML. 38] H --> AN[Governments adapt regulations for
AI/ML automation. 39] H --> AO[Leapfrogging to AI-native 6G
in developing countries. 40] I --> AP[Regional initiatives enable
AI in telecom. 41] I --> AQ[Wireless Innovation Academy builds
local AI expertise. 42] I --> AR[Leapfrogging needs integrity,
interoperability, adaptation. 43] I --> AS[Crashed courses, agile regulation
fast-track AI. 44] I --> AT[Challenges: human capital, funding,
infrastructure. 45] J --> AU[AI-native design, open source,
large models. 46] J --> AV[Pre-standard collaboration on AI/ML
for 6G. 47] J --> AW[Unified data collection
for AI/ML models. 48] J --> AX[Guidelines for AI/ML solution
management. 49] J --> AY[ITU-T Study Group 13
for 6G ML blueprint. 50] K --> AZ[Work program: architecture, APIs,
data governance. 51] K --> BA[Build AI-native 6G system
from ground up. 52] K --> BB[Open source, APIs for
vibrant AI ecosystem. 53] K --> BC[Federated sandbox environments
accelerate AI adoption. 54] K --> BD[Partnerships to bridge AI
skills gap. 55] L --> BE[Regulators adapt for AI/ML
consumer protection. 56] L --> BF[Societal impact of AI-driven 6G
addressed. 57] L --> BG[Collaboration with other verticals
for 6G use cases. 58] L --> BH[Environmental sustainability in
AI/ML architecture. 59] L --> BI[6G balances technology innovation,
market realities. 60] class A,B ai class C,D,E,F,G,H,I,J,K,L,M,N,O,P concepts class Q,R,S,T,U,V deployment class W,X,Y,Z,AA,AB,AC,AD,AE deployment class AF,AG,AH,AI,AJ deployment class AK,AL,AM,AN,AO collaboration class AP,AQ,AR,AS,AT collaboration class AU,AV,AW,AX,AY collaboration class AZ,BA,BB,BC,BD future class BE,BF,BG,BH,BI future

Resume:

1.- AI and ML are evolving rapidly, changing the communications landscape from 5G to 6G and beyond.

2.- Key concepts: AI native 6G, open data ecosystems, autonomy, building community. Need to determine next steps.

3.- Importance of open source in AI-enabled 6G discussed. LFN provides a platform for open source AI projects.

4.- Shift from AI-based to AI-native networks expected in 6G. Data is key. End-to-end view and AI capability focus needed.

5.- MLOps enables streamlined ML development and deployment in telecom networks. Addresses challenges like latency, reliability, distributed architecture.

6.- Semantic communications, large language models and smart interfaces enable AI-native approach for 6G control and orchestration.

7.- Mathematical approach enables 5G network optimization without additional physical resources. Deployed successfully in Seoul, surpassing other networks.

8.- Four key enablers for 6G: spectral awareness, physical world models, stack optimization, open data. Deep learning is crucial.

9.- Graph neural networks well-suited for building network digital twins, providing fast and accurate performance estimation.

10.- Cognition and autonomy are key to 6G. Shifting from enhancing network functions with AI to redesigning through AI.

11.- AI enables trustworthy 5G applications through analysis and optimization. Collaboration with standards bodies important.

12.- Deep learning enables physical layer advancements in 6G. AI-native air interface and massive MIMO among key focus areas.

13.- China Mobile pursuing AI and ML in 5G for efficiency and agility. O-RAN enabling embedded intelligence in RAN.

14.- Hierarchical RIC architecture and RAN-DAF proposed by O-RAN as way forward for AI/ML in 5G and 6G.

15.- 6G requires rethinking - AI-native design, holistic data-centric architecture, end-to-end optimization, and AI capability with multi-dimensional QoS.

16.- Multimodal sensing and digital twins enable overcoming 6G challenges like channel acquisition and proactive blockage prediction.

17.- Deep reinforcement learning enables efficient offloading decisions for compute-intensive applications like vision-based positioning in industrial environments.

18.- Deep learning enables improved network planning by predicting performance of candidate 5G cell placements using LTE data.

19.- Open challenges for deep learning in 6G: explainability, usability, automation, energy efficiency. Potential of large language models.

20.- Generative AI like evolutionary computing enables diversity in network solutions by automating engineer's role in assembling software blocks.

21.- Focus Group on Autonomous Networks (FGAN) architecture includes exploratory evolution subsystem for generative network solution design.

22.- Wi-Fi and 6G coexistence is crucial as Wi-Fi evolves to support deterministic networking and high reliability in Wi-Fi 7 and 8.

23.- Dedicated efforts needed for adopting AI/ML in Wi-Fi considering its unique challenges compared to cellular networks.

24.- IEEE has established a task group on AIML for Wi-Fi, similar to ITU-T Focus Group on Machine Learning for 5G.

25.- ITU-T Y.3172 architectural framework for ML pipelines provides a flexible foundation for applying AI/ML in Wi-Fi networks.

26.- ITU AI/ML in 5G Challenge enables collaboration between ML experts and telecom researchers to solve practical problems.

27.- United Arab Emirates has a national strategy for AI and is establishing a Ministry of AI to drive adoption.

28.- Key challenges for AI adoption in UAE: lack of MLOps platforms, data governance frameworks, and AI engineering expertise.

29.- LiFi and laser-based FSO can provide ultra-high bandwidth to support metaverse and combat spectrum crunch in 6G era.

30.- Mina Sandbox initiative proposed to facilitate collaboration between academia, operators and verticals on AI/ML development and testing.

31.- Scattered AI/ML initiatives at operators need to be unified under a cohesive digital transformation strategy.

32.- AI/ML evolution in telecom: from embedded intelligence in VNFs to platform services exposed via APIs to AI-native design.

33.- AI-native 6G requires standards specifying architecture and interfaces. Pre-standard Proof-of-Concepts and testbeds accelerate adoption.

34.- Technology only 20% of success in AI/ML transformation. 80% is culture, skills, organization, processes, procurement, and governance.

35.- Lack of AI/ML expertise, MLOps platforms, data governance frameworks are key challenges for operators in adopting AI/ML.

36.- Successful AI/ML adoption needs change management - cross-functional teams, new skills, data-driven culture, top management support.

37.- Standardization should provide guidance on AI/ML solution procurement, testing, deployment and lifecycle management for operators.

38.- Academia-industry-government collaboration via Mina Sandbox accelerates AI/ML innovation and develops local expertise in emerging markets.

39.- Governments should adapt telecom regulations and policies to facilitate AI/ML based automation while protecting consumer interests.

40.- Leapfrogging to AI-native 6G in developing countries requires overcoming challenges in infrastructure, skills, funding, and policy.

41.- Regional academic initiatives and collaboration with SDOs like ITU enable capacity building for AI in telecom in emerging markets.

42.- Wireless Innovation Academy developed local AI expertise in Nigerian telecom market in collaboration with ITU FG-ML5G.

43.- Leapfrogging needs embracing advanced tech, but also integrity tests, interoperability, transparency, interpretability, and local adaptation.

44.- Crashed courses, rapid R&D, agile regulation, and infrastructure sharing can fast-track AI transformation in developing telecom markets.

45.- Inadequate human capital, funding, infrastructure, and weak institutional frameworks are key leapfrogging challenges in developing countries.

46.- Presentations highlighted importance of AI-native design, open source, data access, large models, MLOps, and standards collaboration.

47.- Need for a platform for pre-standard collaboration on AI/ML for 6G across SDOs, open source bodies, and industry.

48.- Unified approach to data collection and management across operators critical for training and deploying AI/ML models.

49.- Operators need guidelines and frameworks for procuring, deploying and managing AI/ML solutions as part of digital transformation.

50.- ITU-T Study Group 13 identified as potential venue for developing a "blueprint" for machine learning in 6G networks.

51.- Proposed work program spans architecture, APIs, data governance, testbeds, PoCs, and collaboration with 3GPP, ETSI, O-RAN.

52.- Goal is to build an "AI-native" 6G system from the ground up rather than treating AI/ML as an add-on feature.

53.- Open source and open APIs critical to building a vibrant ecosystem for AI innovation in telecom networks.

54.- Globally federated sandbox environments and challenge programs can accelerate transition from research to standards to productization.

55.- Bridging AI skills gap in telecom workforce requires partnerships between industry, academia, and governments, especially in developing markets.

56.- Regulators need to adapt oversight frameworks to account for AI/ML based automation while preserving consumer protection.

57.- Societal impact of AI-driven 6G needs to be proactively addressed in standards to ensure inclusive, ethical, and sustainable deployment.

58.- Collaboration with initiatives in other verticals like smart cities, Industrie 4.0, precision agriculture can unlock new 6G use cases.

59.- Environmental sustainability considerations like energy efficiency need to be factored into AI/ML architecture from the start.

60.- 6G evolution needs to balance technology innovation with business and market realities to deliver tangible benefits to society.

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