智慧网 络论坛 AI in Network Seminar – Powered by Beta Labs
Keynote 主 题演讲 Henry Calvert Head of Future Network 未来网 络负责人 GSMA
NETWORK AUTOMATION IN 5G ERA HENRY CALVERT, GSMA
Overall it is estimated that AI will add $16tr to the global economy by 2030… +14 % GDP growth $16 tr GDP contribution By 2030 By 2030 72 % 48 % Adoption by Absorption enterprise by enterprise By 2030 By 2030 Source: PwC analysis 2018, McKinsey, 2018 notes from the new frontier 6
Not exhaustive Operators are exploring on AI in different areas. The roundtable we will focus on network operations Operations Services Platforms Network operations Digital assistants Micro-services Current Area of Focus Network planning & Incubator Smart Devices & Robotics deployment Security Marketing & Sales Strategic Investment AI as a Service Customer care Advertising Operators propositions tend to be discreet services
NOT EXHAUSTUVE AI in the networks: status of deployment of applications Operations Planning & Deployment Security • Threat / anomaly Site maintenance detection In deployment or Launched Hardware detection Subscriber mobility & usage patterns • Fraud detection • RAN planning and prevention • VM failure Load balancing / Traffic prediction prediction • MiMO QoS degradation Level of maturity • Fiber roll-out beamforming prediction • Alarm failure • Resource VOLTE optimisation Trials / • prediction utilisation Enhanced Pilots • Threat hunting carrier VIDEO Root causes Site Energy aggregation optimisation analysis efficiency • DDoS • Orchestration for load prevention • Ran model for balancing LAB energy efficiency Lifecycle • Dynamic allocation / • Policy composition/ management Network orchestration Network slicing • AI enabled trouble-shooting handover Radio QoE prediction for Real time data path propagation dynamic allocation NOC & SOC* ACCESS NETWORK CORE 8 * Centralised functions that monitors across core and access networks Source: GSMA Analysis
AI in networks: from automation to zero touch Closed feedback Autonomous Automation Era loop networks Short-medium term Today Long term • Automation is pervasive • Realise the promise of self- • Networks adapt continuously healing, self-optimising learning from experience • Execution of repeatable • ML orchestrates and input from • AI systems take over decision instructions based on well defined rules different models to close the loop making and execution • ML capabilities are introduced, • Executi on still left to rule based • Human supervision , event based focus on discrete applications policies 9 Source: GSMA Analysis
ILLUSTRATIVE AI in networks: from automation to zero touch examples Closed feedback Autonomous Automation Era loop networks Short-medium term Today Long term Action taken by human Traffic VOLTE Domain expert rule based automation prediction optimisation supervision Feedback End to End predictions AI powered decisions Load Overarching AI input balancing Feedback and input actions output output Resource utilisation Video Traffic Volte Video Load Traffic Volte Video Load optimisation prediction optimisation optimisation balancing prediction optimisation optimisation balancing 10 Source: GSMA Analysis
However companies need to overcome business challenges… 1 2 3 AI Skillsets AI infrastructure Data strategy Acquisition, training, managing In-house vs outsource Public/hybrid/private cloud 4 5 6 Use cases & Organisation Societal and privacy business case & Culture concerns Prioritisation 11
… and technical limitations of the technology 1 2 3 Difficulties of Labelling & Need for massive explaining results training data data sets 4 5 Generalisability Potential bias Data and algorithms of learnings 12
Network operations: examples of holistic approach In-house development In-house & open source Open source framework Network Clouds TANGO AI-powered OSS Jiutian platform Virtual machine boxes To improve network CM Network Brain Edge computing performance and network systems and apps monitoring Network services AI marketplace 13 Source: Company websites, press releases
Network operations: example of applications Self maintenance / Predict Peak traffic Automated AI VoLTE optimisation MIMO beam forming Failure prediction in Enhanced carrier aggregation/ virtualised networks Load balancing Infrastructure SD WLAN maintenance 14 Source: Company websites, press releases
Network planning and deployment Planning & Network Design deployment Predict best route for Fiber RAN planning and design roll-out 15 Source: Company websites, press releases
Security: Fraud and advanced threat protection Fraud Threat Hunting prediction / Investment services prevention Enterprise services Prevent Subscriber Identity Module Box (SIM box) fraud 16 Source: Company websites, press releases
How GSMA is helping operator to tackle these challenges Deep dives Applied AI Forum Applied AI initiatives Strategy GSMA Global group led AI Challenge Regulation External Affairs & Public policy Technology Policy Modernisation Industry Purpose group led AI activities Competition policy, AI / big data for social good and toolkit customer and AI Technology Future Network programme IoT Programme Identity+ group and Sharing best practices on Pilots on IoT Pilot on risk programme network automation, verticals scoring energy, etc. led 17
The move to 5G will further push these developments and increase complexities that will need to be solved Intent driven Billions of IoT New products and networks & slicing devices connected services 18
…with integration of AI & 5G across multiple verticals Entertainment Financial services Health Energy Manufacturing Agriculture Transport First response Autonomous vehicles 19
4 A ARE REAS S OF OF FOC OCUS US IN BETA LABS AI & Energy Infrastructure Backhaul Automation omation
BETA LABS S LIBR BRARY Y - www.gsma. a.co com/b m/bet etal alabs abs Website Blogs & Interviews Case studies 22
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