AI Simplify 5G Network

Zhang Sihong

Chief Engineer of AI Solution

ZTE Corporation

Future Network Requires More Intelligence

Business Operation faces highly diversified requirements

Network O & M changes from passive to proactive

Resource Scheduling needs to be more flexible

DU CU NFVI VIM

AMF SMF

UPF

Clouded networks are decoupled

Layering and decoupling causes complicated management

Real-time resource adjustment brings aboutnetwork operation complexity

difficult to locate end-to-end faults.

New services require differentiated operation

Traffic Path Optimization

Cloud Resource Scale-In/Out

IP Data center

OTN

Massive MIMO Parameter

Configuration; 4/5G Coordination

mMTC eMBB URLLC Industry Slicing Operation

The Telecom Industry Will Become the Largest AI Market

$0

$10,000

$20,000

$30,000

$40,000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Software Service Hardware

Global Telecom total AI revenue forecast by industry: 2016-2025

Telecom AI Software Revenue by Use Case: 2016-2025

Source: Tractica/Ovum

According to Tractica/Ovum forecast and research:

•By 2025, the global telecom industry will invest US $36.7 billion in AI software, hardware, and services.

•Among the major use cases of AI technology in the telecom field, the operation monitoring and management of network/IT will

become the largest AI use case in the telecom industry, accounting for 61% of AI expenditure in the telecom industry during

2016-2025.

Large-scale deployment of SDN/NFV and 5G will promote the explosive growth of AI in the telecom industry

Network/IT operation monitoring and management will become the largest application scenario of AI

$36.7 billion

AI-Driven Network Intelligence Is a Focus Area

Machine learning in 5G core through NWDAF

Two AI-related ISG: ENI (Experiential Networked Intelligence) and ZSM (Zero touch network and Service Management)

FG-ML5G: Focus Group on Machine Learning for Future Networks including 5G

Introduce RAN intelligent controller to realize hierarchical, open and smart radio network

Acumos: An open source platform to build an open AI application and service ecosystem

ML platform to study AI scenarios in networks; Open source platform to cultivate ecosystems

CTNet2025 moves from 1.0 to 2.0, using AI technology to promote the network evolution

Announce 5G + plan, Put forward 5G+AICDE, promote the integration of 5G and AI

Introduces AI to network O&M to improve network performance, resource utilization and user experience

CUBE-Net 2.0+：AI-powered Intelligent Network based on SDN/NFV

Intent Engine Automatic Orchestration

AI Engine

Real-time AI engine

Lightweight AI engine

EMS SDN controller MANO

NR/eNB 5GC/EPCIP/OTN

Self-loop: Closed loop inside a device such as a base station.

Big Loop: Inter-network and inter-domain closed loop

Operation Evolution

Rapid Response and Continuous Increase in Revenue

O & M Evolution

Simplification and Continuous Cost Reduction

Network Evolution

On-Demand Scheduling and Continuous Investment Optimization

OPEX

REVENUE

CAPEX

Data Lake

AI training platform

Small Loop: Single-domain closed loop, such as wireless network.

General principles of top-level design: Hierarchical closed-loop, gradual evolution, and modular introduction

Hierarchical closed loop to build NE-Level, single-domain, and cross-domain intelligent network systems

Modularized design of AI capability, Introduced as needed, and controllable investment in overall evolution

Overall Design about Intelligent Network

Management and Control

Layer

Operation Layer

NE Layer

Intelligent Network Evolution Needs Systematic Grading Method

Intelligent level

Intelligent Range

Workflow

NE

Subnet

Whole network

Planning&design Provisioning&Deployment O&M Optimization Business Operation

Full Manual L0

Work with operators to propose a three-dimensional grading standard in ITU-T.

Advanced intelligence L4

Full intelligence L5

Primary Intelligence L2

Intermediate intelligence L3

Assisted Operation L1

L4L3L1 L2Assisted Operation Network L5

Boost Automation Capability and Evolve to Full Intelligent Network

Do TogetherInstruct to Do

Make DecisionSet Target

Unmanned

Simplify some operations Liberate operators Liberate analysts Liberate decision makers Unmanned

Demand

Decision

Policy

Excution

Tool assisted

Demand

Decision

Policy

Excution

Demand

Decision

Policy

Excution

Demand

Decision

Policy

Excution

Demand

Decision

Policy

Excution

Emergency intervention

only

Human AI

AdvancedIntelligence

PrimaryIntelligence

Intermediate Intelligence

Full Intelligence

Human AI Human AI Human AI Human AI

Scenario Planning and Design Network O & M Network Optimization Service OperationProvisioning and

Deployment

ZTE's Full Capability of Network Intelligence

The unified AI& Big data platform is adopted for both CT and IT industry.

ZENICVMAX

NR BigDNA

UME

FPGA Acceleration GPU Acceleration HPC Cluster

Big Data

RSRP Prediction Model

Coverage Assessment

AI Offline Training

AI Online Training

AI Model Acceleration AI Cloud Inference AI Edge

InferenceAI TerminalInference

Log Association Model Traffic Model Alarm Correlation

ModelKPI Association

ModelUser Behavior

Model

Parameter Optimization Traffic Forecast Alarm RCA KPI Detection Intent Translation

Cloud

Studio

Network+AI

Product

Capability

Infrastructure

AI Application Components

AI Algorithm Components

AI Framework

5G Intelligent Network Planning

RCA for 5G base station

AI Trials in 5G Smart Operation Project

Jiangsu

Root Cause Analysis(RCA) for 5GC

Effect

FujianThe planning time is expected to reduce from more

than 45 man-days to about 4 man-days.

RCA for 5G Base Station

5G Intelligent Network Planning

Alarm compression rate is above 48%.

The expected alarm compression rate is above 60%.

RCA for 5G Core Network

The expected detection accuracy is above 70%.

Optical Module Fault Detection

Optical Module Fault Detection

Establish alarm association between the

hardware layer, virtual layer, and NE

layer by using AI, reduce alarms and

analyze the root cause.

Use AI to establish the alarm association

of the DU-CU-AAU- cell, and combine

alarm messages and analyze the root

cause.

The existing 4G data is used for site

reuse and coverage planning, and 5G

planning in hotspot areas.

Active fault prediction is implemented

by using the optical module data and

fault information in the existing network.

Current LB Has the Lack of Accuracy and Timeliness

Challenge Solution Benefit

AI Empowered Load Balancing Based on O-RAN Architecture

Increase LB Accuracy

48.5%

Reduce LB Time

38%

Passive

Blind Selection

Blind handover

Slow handover Prediction

Radio Fingerprint

Scenario Recognition

Proactive user association

Make precise strategies

Customized optimization

Core AI Model

Cell1

Cell2 Cell3 Cell4

Non-RT RIC

Near-RT RIC

CU/DU

AI-based Construction of RF fingerprint DB

Load Balance Execution

Update RF fingerprint &

AI-based LB Policy

160 seconds260 seconds

61% 90.6%

Intelligent Application and Platform Pilot in 4/5G Network

Service Scenarios

Cell Traffic Prediction

Root Cause Analysis Based on LTE Base

Station Alarms

eNodeB Energy Saving Parameter Self-Optimization

Capacity Expansion CellIdentification

Antenna Feeder Parameter

Optimization

Root Cause Analysis of LTE KPI

Graphic User Interface

Data driven

Support machine learning, deep learning, andreinforcement learning

Open and Interconnection

ZTE uSmartInsight Platform

ZTE and China Unicom established the intelligent network joint inovation lab in shandong.

Open SourceStandard

Alliance

Founding member of Deep

Learning Foundation

Code contribution to the

community every year

Vice chairman

Daily management; AI case

collection and open source

project initiation.

WG3 chairman

ML-AWARE contribution

World's first intelligent network

architecture standard

ZSM: Initial member, ZSM004 reporter

ENI: promote the intelligence of SDN

network

NWDAF: proposals about mobility

management and data-plane NE

selection

Actively participate in TC1WG1

telecom network intelligent project

Lead TC5 mobile intelligent project

Cooperate with China Mobile in O-RAN

ZTE's Activities in AI Related Standard&Open Source Organizations

Industrial Ecology

Open Source

Alliance

Standard

Thank you