ai empowers 5g intelligent operation

©TM Forum | 1

AI Empowers 5G Intelligent Operation

Champion Participants

©TM Forum | 2

Wireless network: 4G/5G col laboration, 5 networks

with 11 bands, and M-MIMO (Massive Multiple Input Multiple Output)Core network: virtualized, microservice-oriented, and edge sinkingBearer network: SDN (Software Defined Network) and

cloud-network synergy

Dense deployment: complex configurations and high costs of site visitsEnergy consumption: high electricity fees and 5G energy consumption

Agility: available once provisioned and self-serviceDeterministic SLA (Service Layer Agreement): delay-sensitive and high reliability

New experience

High bandwidth

Mbit/s-> Gbit/s

Low latency10 ms-> ~1 ms

100,000-> 1 mi l lion

Mass connectivity

Network

complexity

Cost pressure

New 2B

requirements

New experience New challenges

New challenges

At the TM Forum Autonomous Networks Summit 2020 in Beijing, China, Wei Lihong, General Manager of China Mobile's Network Department, delivered a speech titled "AI Enables Autonomous Driving Network and Practices 5G+", which explained the new challenges brought by 5G and called for intelligent network capabilities. This catalyst project is an in-depth exploration into TM Forum AN, with contributions from both China Mobile and industry partners.

New Challenges Brought by 5G

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Call for Intelligent Network Capabilities

eMBB

uRLLC

mMTC

AI Automation

"Soft" capability in the fourth dimension

"Hard" capability of 5G networks

eMBB(enhanced Mobile Broadband)

mMTC(Massive Machine Type Connection)

uRLLC(ultra Reliable Low

Latency Connection)

Large-scale IoT

1 mill ion connections/km2

Ultra-high reliability and low latency

1 ms, 99.999%

Network + AI: lower cost, higher efficiency

Operation + AI: simplified operation and

maintenance

Business + AI: agile support

• IoE's (Internet of Everything) cornerstone is the "hard" pipe capability of 5G networks.

• AI-empowered network automation will become an important "soft" capability of 5G networks, enhancing existing eMBB, URLLC, and mMTC

capabilities while also increasing network dimensionality.

• Autonomous driving network (AN) will bring improvements in three aspects: network + AI (better network performance), operation + AI (simplified

operations and maintenance), and business + AI (deterministic capabilities), meeting the network requirements of vertical industries in different

scenarios and promoting value-oriented network operations.

©TM Forum | 4

Intelligent mining of mass data helpsdetect hidden risks, locate the root causes of alarms, and intelligently

preprocess faults, improving operation and maintenance efficiency.

Intelligent analysis of performance indicators helps proactively identify and

automatically process poor-quality services, improving network quality.

Intelligent modeling based on home network characteristics and customer intents helps accurately identify and

solve customer complaints, improving customer experience.

Operation + AI Network + AI Business + AI

◆ 4G/5G intelligent incident management

◆ Intelligent optimization of 5G wireless networks

◆ NFV(Network Function Virtualization) log analysis

◆ Premium home broadband◆ AI-driven comprehensive improvement of

network satisfaction

AI Drives Higher Operation and Maintenance Efficiency, Better Network Quality, and More Agile Services

©TM Forum | 5

Principles:1. Single-domain, single-

vendor autonomous management and control (small closed loops)

2. Multi-domain, multi -vendor orchestration (large closed loops)

BSS/Application

Plan

Construct Maintai

n

Optimize

Networkautomation

Networkintelligence

Analyzer

Manager

Controller

Network management &control system

OSS (cross-domain orchestration)

Networkautomation

Networkintelligence

Analyzer

Manager Controller

Network management &control system

Autonomous domain X

Autonomous domain Y

Single-domain autonomy


Operate

5G transport Premium private linePremium broadband

AN framework:3 layers, 4 closed loops

Overall Solution: Autonomous Domains (Small Closed Loops) + Network Automation (Large Closed Loops)

RFS = Resource Facing Services

CFS = Customer Facing Services

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Operation + AI — 4G/5G Intelligent Incident Management :Alarm Aggregation,

RCA, and Accurate Tickets

Mass alarms⚫ 500,000+ alarms a day on a local network⚫ Ticket storm (300+ alarms per second)

ChallengesSolution Benefits

Experience-basedAlarm filtering and RCA (Root Cause Analysis) rely on specialists' experience, which are inefficient and error-prone.

Low troubleshooting efficiency

Alarm compression rate: 99.9%; tickets: reduced by 35%

35%43%

23%

9%

0

100

200

300

400

2019.1 2019.12 2020.1 2020.2

Tickets for or iginal alarms (daily)

Tickets for intel ligent incidents (daily)

Decrease rate

Recall: 95%; precision: 90%

Recall Precision

95% 90%

OSS (Operations Support Subsystem)

Ticketing


Alarm reporting

Intelligent incident API(Open API)

Network management and control system (single-domain, single-vendor intelligent incidents)

MBB FBB

Ala

rm n

oise

red

ucti

on

Ala

rm

aggr

egat

ion

RCA

Fault 1

Fault 2...

Fault 1

Alarm Monitoring

• Ticket storm handling• Ticket correlation/recall• Ticket delay analysis

• Multi-domain, multi-vendor analysis

• Multi-domain RCA• Handling Policy delivered

to the root fault domain

Delivery policies to the root fault domain.

(Open API)

Multi-domain collaboration

Troubleshooting⚫ Tickets are dispatched repeatedly, not

dispatched as expected, or overstocked.⚫ Low alarm & ticket accuracy, difficult to

locate and rectify faults across layers and vendors

RCA = Root Cause Analysis MBB = Mobile BroadbandAPI = Application Programming Interface FBB = Fixed Broadband

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⚫Massive MIMO service complexity: 1000+ patterns, involving horizontal and vertical beamwidths, downtiltangles, and azimuth

⚫Complex and diversified scenarios: wide coverage, high-rise buildings...

⚫Highly dependent on drive tests: overshoot, overlapping, or weak coverage


5G network in XX city (1426 cells)

Coverage improved by 15.8% on 91% of roads

15.59

18.07

14

16

18

20

优化前优化后

Average SINR (dB)

2.38

% 0.98

%

0.00%

2.00%

4.00%

优化前优化后

Percentage of SINR < 0

Network management & control system

LTE NR(New Radio)

MR(Measurement report)

MDT(Minimizationof Drive Tests)

DT

Initial network construction phase:

Automatically improve coverage by importing drive test (DT) data.

Routine operation and maintenance phase:

Continuously improve coverage and user experience based on AI and online data

Pattern adjustment

CHR/MR/...Before After

Before After

Online learning, iterative optimizationIterative optimization

Tilt angle Optimal point

Start point

Azimuth

Performance

Optimal solution

Network + AI — 5G Intelligent Optimization :Coverage Improvement with Massive

MIMO(multiple-input multiple-output )

Network management &

control system

SINR = Signal Interference Noise Ratio

CHR = Call History Record

©TM Forum | 8


Risk monitoring scope ↑⚫ More comprehensive device risk

monitoring, preventing major risks from being missed

⚫ Auxiliary measures for alarms and traffic statistics

Risk detection speed ↑1-30 minutes earlier than alarm generation

Device security ↑⚫ Prevent faults in advance⚫ Keep network devices secure

Risks on NFV devices

⚫ Open and multi-layer NFV architecture⚫ Risks on any nodes can cause

accidents.

Manual, inefficient log analysis

⚫ NFV logs are large in quantity. In a region of country C, more than 10 billion logs are generated every day.

⚫ Manual analysis relies heavily on specialists' experience, efficiency, and status.

Log-based risk detection

⚫ IT device logs record 90% of information and are therefore very important for O&M.

⚫ Logs are more sensitive than alarms.

Log f iles

Log distribution featuremodel training

Anomaly detection

Log clustering analysis

Faultscenario matching

Manual analysis and labeling

Log templates

Fault log feature database

Anomalykey logs Fault symptom description

Un

kno

wn

fau

lt m

on

ito

rin

g

Kn

ow

n f

ault

mo

nit

ori

ng

1. Data mining-enabled unknown and known fault monitoring based on logs

2. Real-time display of device status

Network + AI — AI Driven Log Analysis :Intelligent Risk Warning based on NFV Logs

©TM Forum | 9

Challenges Solution Benefits

Router of vendor A

Antenna: 2 x 2Single/dual-band: 2.4GStandard: Wi-Fi 5

High-dimensional mapping of neural

network models and features

Collect three types of data

• ONT/AP/STA capabilities:frequency bands, network ports, and antennas

• Network connectivity:network cables

• Wi-Fi capabilities:Wi-Fi strength, interference...

Device capability learning and bottleneck

identification

200 Mbit/s 20- 100 M bit /s

1

32

45

6Subscribed bandwidth

Actual bandwidth

For 76% of users, subscribed bandwidth ≠ actual bandwidth

6%

15%

35%

38.50%

40%

100BAS E-T cable

100BAS E-T port

Si ng le-ba nd ON T

Si ng le-ba nd router

Si ng le-ba nd termina l

The home network is complex, in which user experience is affected by

multiple factors.

According to data of 27,000 broadband users in XX city

Marketing conversion rate increased 5-7 times

Future

Now

Precis ion marketing based on data analysis

Ground promotion

User Device Capability Wi-Fi CapabilityPotential

Customer Level

User ASingle-band optical modem

Poor coverage High

User BSingle-band optical modem + single-band router

Poor coverage and high interference

High

User C Dual-band optical modemGood coverage and low interference

Low

...

?

Subscribe to 200 Mbit/s but receive

only 50 Mbit/s

End users: Subscribe to 200 Mbit/s and receive 200 Mbit/s.

A p air o f M AC

ad d re sse s

Digi tal twin

Sh are p arame te rs

M AC ad dress in vector re p re se n tatio n

M AC ad dress in vector re p re se n tatio n

Business + AI — Premium Broadband : Accurate Identification of Home Network Bottlenecks to Meet Bandwidth Experience Commitments

©TM Forum | 10

Business + AI ： AI-Driven User Satisfaction Improvement

Challenges Solution Benefits

Time consuming

◼ Cal center In-time answer rate < 50%

◼ 80% of Ticket handling duration > 4hours

Poor solving

◼ Root cause accuracy < 50%

Purely Reactive

◼ Lack of evaluation on overall usersatisfaction

◼ Not able to prevent potentialcomplaint

Definition of network characteristics

XDR, MR, KPI, FM, CM

Dimension Reduction

u1

u2

u3

⋮un

G1

G2

⋮Gk

Clustering

Initial portrait of user perception based on network data

Accurate portrait of user perception based on AI algorithm

❶ ❷

❸❹

Root cause analysis and pinpoint the user complaint on map

Efficiency Improvement↑

- Call center In-time answer rate 70%

- resolution is available within 3 seconds。

Accuracy Improvement↑

- Root cause accuracy > 85%

- Locating accuracy > 95%

Proactive Enhancement↑

- Overall user satisfaction evaluation is proven with 92% accuracy

- Prevent complaints from high potential users

5

XDR= X Detail Record FM= Fault ManagementMR = Measurement Report CM= Configuration ManagementKPI = Key Performance Indicator

©TM Forum | 11

September 2019

Research

December 2019

Topicdeclaration

Team

building

January 2020

June 2020

Achievements

R&Ddeployment

April 2020

September 2020

October 2020

Catalyst phase-1achievements

Attain L3

Exploratory phase Systematic phase

Optimization and

expansion

Summary and Outlook

• In this catalyst, China Mobile has been progressed on fully implementation on it

“5G+” strategy to promote 5G+AICDE(AI,IoT, Cloud computing, Big Data, Edge

Computing) innovations. Based on the TM Forum Autonomous Network concept,

this catalyst project focuses on 5G+AI explorations.

• With Huawei, Nokia, AsiaInfo and BOCO, we deeply explored network operations

and maintenance with five use cases in this catalyst.

ai empowers 5g intelligent operation

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