White paper Intelligent Network Awareness System (iNAS)-尺寸 ...

White Paper Intelligent Network Awareness

System (iNAS)

MANAGING CONNECTIONS SMARTER

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WUHAN

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High-tech Development Zone, Wuhan, China

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TST, Hong Kong

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BEIJING

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6 Zhongguancun South Street，

Haidian District, Beijing, China

Tel: +86 10 6870 9986


White paper Intelligent Network Awareness System (iNAS) —— Catalogue White paper Intelligent Network Awareness System (iNAS) —— Catalogue

1.1 Scope

1.2 Definitions

01

01

02

03

05

05

14

15

18

20

21

23

26

30

07

08

11

14

2.1 Industry Background

2.2 Mobile Network Data Service Establishment

2.3 The Operators Requirements

2.4 Solution Ideas

Background

4.1 Solution Domains

4.2 Perception Analysis Subsystem (QOE Domain)

4.3 MR Analysis Subsystem (Wirless Domain)

4.4 Monitor Alarm Subsystem

4.5 Customer Service Support Subsystem

4.6 Thematic Analysis Subsystem

4.7 Quality Analysis Subsystem

4.8 Typical Cases

3.1 Network Awareness System

3.2 System Composition

3.3 Typical Network Deployment

3.4 Product Features

CATALOGUEIntelligent NetworkAwareness System

Solution Features

01 02

This document is targeted for any audience who is interested in learning more about GreeNet’s Network

Awaremess System (iNAS). In this whitepaper, we analyse the broadband and mobile data market

requirement and the importance of network visibility for impoving customer expereince as well as introduce

GreeNet’s iNAS solution, it’s technical implementation, product architecture, major functions, and application

use cases.

1.1 SCOPE

2.1 INDUSTRY BACKGROUND

1.2 DEFINITIONS

PREFACE1

2BACKGROUND

Term Description

Signaling acquisitionmachine

Service preprocessmachine

Service analysis machine

Application server

Browsing QOE

Video QOE

Cluster management node

Cluster computing node

Extract, Transform & Load

IM QOE

Gaming QOE

Service QOE

White paper Intelligent Network Awareness System (iNAS) —— Preface White paper Intelligent Network Awareness System (iNAS) —— Background

Following with the popularity fo mobile devices such smart phone, tablets or even some IoT devices, the

demands for mobile data service has been increasingly sharply. The communication service providers face a

lot of challenge from networking improvement to attaining customer satisfaction as their users expect fast

and reliable mobile internet services.

With the growing demands on mobile internet resources,CSP’s are facing a continous increase in complaints

from users due to poor service quality as network resources are stretched to the limit. To help manage these

Term Description

Identifies the signal data acquired from the control plane, carries out protocol analysis, such as GTP-C, S1AP, NAS, Diameter, etc., extracts key field information, associates, backfills, and synthesizes signaling XDRs.

Acquires user plane data records, perform stream session management, protocol, and application recognition, performs deep packet analysis/deep stream analysis per packet/flow characteristics for protocol and application identification.

Acquires original service data records, scans the session table, and outputs XDRs.

Provide web UI access, permissions, and log management.

Users awareness quality to be excellent and good overall ratio when using im service.

The awareness quality rate when using the game business.

Page opening delay The time(ms) between browsing request and the first [FIN.ACK] message.

Video download speed The download rate(kbps) of video after initiating a video play request from user.

The overall awareness quality rate of browsing business, video business, instant messaging, and gaming business.

First screen delay The time(ms) between the user initiating the browsing request and the terminal loading all the resources on the first screen.

Video stalling frequency

IM success rate

In the process of playing the video, the number of times of stocking(times/minute).

When users use instant messaging, the proportion of successful times.

Interaction delay During the gaming, the total delay of TCP three handshakes(ms).

TPON Telephone one Passive Optical Network.

eHRPD eHRPD is being standardized as a method of interworking multiple access networks (eHRPD, E-UTRAN) under a single packet switched core network, SAE/EPC

The awareness quality rate when using browse business.

The awareness quality rate when users use video services.

Cleans the XDRs received from the Service analysis machine, transposes the format according to the service needs and load the data into the database.

Map Data block, process client read and write requests; Configure replica policy; Manage task scheduling; Manages the namespace of the database system.

According to the functional requirements of Network Awareness System and the XDR attributes, implement data modeling, data storage, distribution scheduling and correlation analysis.

Mobile network users need to go through a series of processes at the signaling level and the business level

to access the Internet. which can be roughly divided into:

1. Signaling level:

Network attachment, which is attached to LTE network after starting;

Bearer establishment, and a default load bearing connection is established between the terminal and EPC.

TAU update, position change or periodic position update.

2. Business level:

DNS domain name resolution, to request the domain name for the target IP address resolution;

TCP handshake establishment, TCP establishment with the target IP address;

HTTP Get request, send a Get request to the target IP address server, download the page.

Traditional approach such as TOPN cell analysis, DT/CQT, user complaints and so on are limited in their

ability to identify network problems. This only represents local points and line problems in the network and do

not directly correlate with user expereince awareness.

Traditional methods lack a means of accurate demarcation

Accuracy of traditional methods to pin point the location of the network performance problem is not high so

KQI and KPI have not establish able to a correlated analysis. They lack of end -to-end insight.

Traditional methods lack a means to support complaint handling

In the face of user complaints about service awareness and network unavailability, the traditional methods of

network performance monitoring lack the ability to effectively support customer service agents in complaints

handling .The reason is that the traditional methods neither provide insight from the user persepctive nor

provide end to end insight in real time.

Traditional methods do not help optimize operational proceedures

The traditional pinpoints monitoring is not end to end. They do neither provide any closed loop monitoring of

user perception, nor do they provide predictive network problem capabilties before a user feels network

The service flow is as follows:

2.2 MOBILE NETWORK DATA SERVICE ESTABLISHMENT

White paper Intelligent Network Awareness System (iNAS) —— Background White paper Intelligent Network Awareness System (iNAS) —— Background

complaints, it has become particularly important for CSP’s to have increased network visibility and

awareness capability for their mobile data services. With the current level of extremely fierce competition,

mobile operators must quickly & continously improve their business and network quality to retain good

corporate image and reputation in order to reduce churn.

In order to comprehensively measure the level of network quality and to ensure user satisfaction, it is

necessary to take a holistic view of the network. Network monitoring systems must be able to perceive the

changes in network operation from the perspective of users, improve mobile network quality management,

collect indicators that can reflect user experience, and allow network expansion and optimization to deliver in

a target way. At present many operators are investing heavily to expand their network capacity as a way to

improve customer satisfaction.

browsing behavior Network procedure decomposition

Power on/online

Open a browser

Network attached

Build an Internetconnection

Domain name query

Establish aconnection

Business use

1

2

4

5

Attachment

EPS bearer

DNS query

TCP Three-Way Handshake

SP response

3 Open the web page

Page download

Page Browsing

03 04

In order to enhance operators' network operation efficiency, the following strategies can be implemented:

Update production procedure: integrate construction, maintenance, and optimization of the network with

intelligent network awareness to establish an end-to-end work process mechanism that enhances both

business support systems and network to improve end to end user experience

Build evaluation platform: based on DPI, establish a set of evaluation systems that can objectively reflect

users experience, and conduct awareness evaluation on users, network, and service. DPI data can be

correlated with MR, CM, PM, alarm and other data, for precise fault locating that encompasses end to end

analyis.

Introduce smart operation: Use data analyics to promte efficiency, improve network operation and data

operation capabilities, strengthen network basic capabilities and improve team capabilities, and enhance

execution efficiency.

Improve user perception: provide more personal and differentiated services for users, discover network or

business systems problems before users feel them, and give predictive regional warning of poor quality;

Multi-scene thematic analysis, grid management optimization.

2.3 THE OPERATORS REQUIREMENTS

Mobile Internet Network Awareness System (iNAS), is built on a combination of DPI and bigdata analytics

platform. Through network service and terminal awareness analysis iNAS is able to reflect user’s real

Internet service experience (QoE) , thus it achieves the following capabilities:

Model building: the system combines the analysis of OMC network management MR data, LTE KPI data,

2.4 SOLUTION IDEAS

congestion. This means the traditional methods offer very little to help improve efficency of network support,

maintenance and expansion.

Network monitoring using DPI engine and Big Data Analytics to locate user’s pain points when using the

network. The iNAS solution is able to help address the network quality. As a result it helps improve user

experience for a better QoE.

alarm, DPI data, and so on to form an end-to-end awareness, big data modeling and improvement of

operation and maintenance capability.

Intelligent troubleshooting: from the awareness assessment results, the weak spots from regional areas to

network elements can be located. An end-to-end intelligent troubleshooting and analysis will be carried out to

locate the troubled areas. The system will provide relevant processing suggestions for the use to improve the

user quality perception.

Problem closed-loop: verify the problem after optimization, and form a problem handling closed-loop to

improve user experience on an ongoing basis.

Procedure support: From the analytics results, the support and maintenance’s standard operating

procedures will be updated to include the all required services including network optimization and services

enhancement.

Awareness evaluation Intelligent fault-locating Close-loop optimization

2 types of data: APP, DPI

Two aspects: optimization an assessment

Six bases: network element, scene, region, user, terminal, business

Cell: correlation analysis of MR alarm, performance, configura-tion, CDR, etc.

Service: TCP connection quality, CDN scheduling, IDC resources

User: terminal, service, network quality

Monitoring: assessment of services, monitoring in key areas quality poor alarm: alarm of inferior cell, service, and user

Dispatch: intelligent dispatch optimization and verification

05 06

White paper Intelligent Network Awareness System (iNAS) —— Background White paper Intelligent Network Awareness System (iNAS) —— Background

Typical hierarchical structure:

Acquisition layer: the production or acquisition XDRs.

Adaptation layer: analysis, correlation, adaptation, data extraction, and cleaning of XDRs.

Data layer: index establishment, aggregation, and other processing.

Preprocessing: management of configuration, logs, permissions, applications, reports, maps, etc.

Application layer: statistical analysis & presentation.

Signaling acquisition:

Signaling acquisition: Acquire the original data stream flowing through the signaling acquisition machine,

identify automatically the logical interface data in the link, carry out protocol analysis, such as gtp-c, S1AP,

NAS, and diameter, extract the key field information, corelate, backfill, and then aggregate the signaling

XDRs.

Signaling backtracking: Store the acquired original data,and provide query interface and stack parsing

function; support application server to query raw data, realize user signaling backtracking presentation

function.

Service acquisition:

User plane data acquisition: manage the traffic by session, and the number of traffic data packets will be

3.1 NETWORK AWARENESS SYSTEM

3.2 SYSTEM COMPOSITIONINTELLIGENT NETWORKAWARENESS SYSTEM

3

SYSTEM ARCHITECTURE

Use of BigApplicationLayer

Pre-ProcessingLayer

KPI KPI KPI

KQI KQI

QoE

AwarenessManage surveillance Customer

ComplaiSales andMarketingSupport

QaulityAssessment MR Panel Network

Planning &

Analysis processing

QoE (Awarenes index)

KoI (business index)

KPI (network index)

Controldata

BusinessData

AppData

Resourcesdata

MRData

PerformanceData

CDRRecords

JobOrderAlertData

ComplaintData

CRMData

Adnormaly billingprocessing

Consolidated BillingProcessing

CatergorizationProcessing

External Data processing

GIS processing Reportingprocessing

DispatechProcessing

QoE

Awarenessm

odel

Quality

Assessment

model

Data storage

Special topicsprocessing

Data Layer

AdaptationLayer

DataAcquisitionLayer

Storagecluster

ApplicationServices

Warehouse /Reporting

ControlAcquisition

Control Acquisitionsystem

Business DataAcquisition System

Control AcquisitionSystem

Business Data AcquisitionSystem

ControlAcquisition

Business DataAcquisition

Business DataAcquisition

Warehouse /Reporting

Supervisor node

EquipmentManagemet Node

ELT/FTP/interface

Control backtrack

Control backtrack Business Analytics

Business Analytics Control backtrack

Control backtrack Business Analytics

Business Analytics

ELT/FTP/interface

Storage Node

Storage Node

Storage Node

ApplicationServer

Storage Node

07 08

White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System

counted for both upstream and downstrea traffic.

Protocol stack parsing: extract IP & transport layer properties. Support all kinds of tunnel layer

decapsulation.

Protocol and application identification: conduct deep packet and deep flow analysis through packet

characteristics and flow characteristics,and identify protocol and application types.

Feature library online upgrade: update dynamically the protocol and application feature library.

Original XDR output: scan the session table, and then output XDR.

XDR backfilling: acquire user attributes of the signal acquisition machine, match user with IP, TEID and

other information, and backfill the user number, IMSI, IMEI, location, APN and other public information.

The acquisition interface supported by the user plane’s acquisition machine is s1-u, S5/S8, S2a and many

more.

Loading/reporting:a server that consists of ETL, FTP and interface.

ETL services:

Data Cleaning: clean the XDR that does not meet the requirements of the specification.

Translation: data translation is performed according to service needs. The destination IP address is

translated into flow direction (including operator and administrative region), and IMEI is translated into

terminal manufacturer, brand, model, operating system type and other information, and the cell ID is

translated into access address.

Real-time data statistics: perform real-time statistic calculations from data gathering from base stations,

cell users and traffic according to functional requirements to support real-time monitoring and alerting.

Data loading: load processed data in batch into the storage clusters.

ETL can horizontally be extended to meet the function and performance requirements of any data volume.

FTP service:

Provide FTP service for xDR output from the acquisition machine.

Provide FTP (or SFTP) service for the third-party system.

External interface service:

Prepare data according to the requirements of a third-party system by Socket, Web Service, FTP and so on.

Storage/computing cluster:

Data modeling: according to the functional requirements and xDR attributes, the data modeling is

established.

Data storage: storage of original XDR data, multi-dimensional analysis data, application data, and report

data.It also stores various XDR data and statistical analysis results. Data is stored with redundancy and

compression.

Scheduling: according to aggregation and statistical rules to form varies of computing tasks.These tasks are

distributed to a computing cluster for multi-dimensional analysis, computing scheduling, and management

and ooperations computing management.

Correlation analysis and multi-dimensional aggregation statistics: conduct various statistics and

KQI/KKPI reports from multi-dimensions and mult granular data including region, network element, user,

APN, SP, and granularity of hour, day, week, month.

The storage and calculation management nodes: it consists of Master and Slave mode.The Master node

mainly manages data nodes, data block mapping, processing client’s reading and writing requests, configure

replica policy, task scheduling management, and manages the namespace of the database system. At the

time when the Master server is deactivated, the Salve will automatically be activated to undertake the cluster

management work.

Application services:

Service awareness

Provide web UI to query the statistical analysis results of the system. The presentation methods include

report forms, bar charts, pie charts, curve charts, GIS presentation and so on.

Awareness functions: service quality analysis, network quality analysis, application mining, operation

optimization, awareness evaluation, etc.

Provide geographical map service for the system.

Authentication and log management.

09 10


4G network acquisition point:

5G NSA Option3X acquisition point:

5G NSA Option3X acquisition point are marked in the diagram:

The acquisition signaling interfaces include: S6a, S1-MME, S10/S11, S5/S8, Gx, Gy and so on.

The user plane interfaces include: S1-U, S5/S8, S2a of EPC (including eHRPD).

When compared to the acquisition point of 4G network, the S1-U between NR-SGW was added.

5G SA networking from traditional network elements to network functional NF, based on NFV architecture,

software defined network and network connectivity.

The control plane reflects the servitization interface, such as: N7, N8, N10, N11, N12, N13, N14, N15.

The user plane represents point-to-point interface, such as N1, N2, N3, N4, N5, N6, N9.

Deploying on x86 server architecture, ETL, storage computing nodes and application servers is installed on

clustered platforms.

3.3 TYPICAL NETWORK DEPLOYMENT

3.3.1 4G DATA ACQUISITION POINTS

3.3.2 5G DATA ACQUISITION POINTS

3.3.3 DEPLOYMENT OPTIONSMointor Point

Signalling

Data

SGW PGW

HSS

S1-MME

S1-U

PCRF

S6a

S5/S8

Gx

OCS

GyS11

SGi

eNodeB

BTS eAN/ePCF HSGW

Operator’s IPServices(e.g.IMS etc.)

S2a

AAA Proxy

AAA Server

PI*

S6b

A10/A11

STaS103

AN-AAA

A12

3.75G/eHRPD

4G/LTE Rx

Swx

Evolved Packet Core(EPC)

eHRPD Network

UE

UE

UE

UE

S10

MR Data

MME

Application Server

Storage node 1

ETL Server

Control signalacquisition

Data acquisition

ETL Server

Storage node 2 Storage node 3

Application Layer

DataPre-processing Layer

MME

SGWeNodeB

HSS NSSF AUSF UDM

AMF

UE RAN UPF

SMF PCF AF

NAT

PCRF

UE

OMC Network DataAcquisition

OMC DataAcquisition

OCS

PGW

Acquisition Layer

EPC/EPC+

Mobile Internet, business awareness

analytics platform

Service ProviderNetwork Sides

4G/5G NSA 5G SA

5GC

Internet

NAT

Internet

UE

LTE-Uu

E-UTRAN

NR

S1-MMEMME

S10S1-U

S1-U

S11

S6a

HSS

Gx

PCRF

ServingGateway

PDNGateway

Operator’s IPService(e.g.IMS.PSS etc.)

SGi

Rx

Ga

S5

CG

NSSF AUSF UDM

N22

N12

AMF

UE (R)AN UPF DN

SMF PCF AF

N8N10

N11

N1 N2N14 N15

N3

N4

N6

N9

N7 N5

N13

11 12


intelligent

The intelligent obstacle detection algorithm can be used to judge the faults classified by cell, business

platform, user terminal and so on. The processing suggestions are offered automatically by using the

knowledge base.

customized

Customized quality analysis can be make for specify use cases.

security

Predict maintenance with email alert from real time database monitoring

High efficiency

Using parallel processing data loading technogy, the data is stored both accurately and fast loading at daily

rate of 20TB capability. Distributed intelligent indexes provides a good load balance for efficient statistic

analysis.

Taking 4G network as an example, the traffic of S1-MME, S11, S6a, S1-U, S5/S8, Gx and Gy links can be

obtained by fiber splitting method. The aggregation and shunt equipment are optional. When there are too

many links, the aggregation and shunt equipment should be configured for the aggregation and signaling

separation of multiple links. The XDR is generate, through ETL server to clean and translate record, after the

original traffic flows through the signaling acquisition machine, the service aggregation machine and the

Service analysis machine The data is then loaded into the storage and computing/storage cluster. The

computing/storage cluster stores the record details. As a results, various reports can be generated for the

application server to query and display. Typical deployment mode are.

Distributed deployment: the acquisition layer adopts distributed deployment methodology to deploy

signaling acquisition machine, service acquisition machine and service analysis machine respectively

according to the operator's office address/switch room.

Centralized deployment: the acquisition layer adopts a centralized deployment methodology to gather and

transmit traffic to a machine room according to the operator's office address/machine room, and to the

centralized deployment of signaling acquisition machine, business front-end machine and Service analysis

machine .

Data adaptation processing and application layer: generally, centralized deployment is adopted to certain

central machine room, to collect xDR telephone bills in uniform way and transmit them to the central machine

room for data adaptation processing.

3.4 PRODUCT FEATURES

3.3.4 TOPOLOGY

Transmission

N*10GE

Splitter Splitter

······

Node 2

Node 1

Access Point 1 : signal, data interface

Access Point 2: signal and interface

N*10GE

Data acquisition Control signalacquisition Data acquisition Control signal

acquisition

ManagementNode

Switch

Storage Node ETL/FTP/interface

BackupManagement Node

ApplicationServer

4.1 SOLUTION DOMAINS

4SOLUTION FEATURES

Mobile Internet business perception analysis system is composed of seven subsystems. These subsystems

provide the operator with different analytics domains;

13 14


perception analysis subsystem MR wireless analysis subsystem

monitoring and alarm subsystem customer service support subsystem

thematic analysis subsystem quality analysis subsystem

marketing support subsystem

Each analytics domain is organized hierarchically, with each typically having the summary view (Dashboard),

analysis view, data details view, definition bit view, etc., The system supports drill-down analysis to meet the

requirements of personnel from different departments.

4.2 PERCEPTION ANALYSIS SUBSYSTEM (QOE DOMAIN)

The Awareness analysis subsystem provides a summary view dashboard, detailed views, data views and is

configurable to support the operator's own network analysis requirements. The network domain uses

information from the entire network data set such as, cities, scenario, business, community, terminals, and

users to determined perceved user QoE. The subsystem allows for step by step analysis to find out

potential network problems. It also gives flexiblity to provide recommended actions to resolve network

issues.

QoE Domain supports drill down functions. This is to give a visual infographics data to help viewing of user’s

QoE. It supports from the whole network view, city, scene, business, community, terminal, user to other detail

levels for fine view and analysis.

From the summary dashboard and analysis view you can visualise and monitor overall network quality from

a panoramic perspective. Monitor key network performance indicators, such as browsing, video, instant

Overview view example -1



4.2.1 SUMMARY VIEW & ANALYSIS VIEWData View and Query allows operators to have self service detailed data analysis. The operator can query,

filter, apply conditions, and export data. Data View and Query can be used to analyise all data sets , user,

terminal, wireless, bearer , network core, SP, end to end fault segment delocalization reservation, output

positioning results and conclusions.

4.2.2 DATA VIEW & QUERY

Mobile Internet business perception analysis system

Awareness analysis

subsystem

MRanalysis

subsystem

Monitoralarm

subsystem

Thematicanalysis

subsystem

Qualityanalysis

subsystem

Marketingsupport

subsystem

Customerservicesupport

subsystem

15 16

White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features

messaging, gaming and overall network performance.The overview scope can easily be changed to monitor

performance at the overall network level or drill down to apecific sections of the network.

Awareness quality overview

Region setting Time granularityAll All 09-03Day Month Analysis

111365

42.73

92.79%

91.75%

Communities

Excellent rateof browsing

Comprehensiveexcellent rate

427340Users

111365TB volume

111365

217.63

Types of services

TB volume

111365“4H1C1M” cells

111365Cells of poor quality

Excellent rate analysis Service type analysis

5025 75

0 10091.13%

Excellent rateof video playing

5025 75

0 10098.53% 97.27%

Excellent rateof messaging

5025 75

0 100

Excellent rateof gaming

5025 75

0 100

User analysis Volume analysis

Total number of users(in 10,000)

Service type

BrowsingVideo playingMessagingGaming

Poor quality % of the total Quantity

Browsing

Video playing

Messaging

Gaming

Browsing

Video playing

Messaging

Gaming

13.41%

3.38........1.55%9.46 22.1%

1.29 3.0%

7.71 18.0%

0.80 1.9%

212.1........97.46%

1.94........0.89%

0.21........0.10%

5228034

132

28.75%11.76%

5.03%

Scenario analysis Excellent rate indicator overview by category

92.79%Excellent rateof “4H1C1M”

0

20

4060

80

100

120 0

20

4060

80

100

120 0

20

4060

80

100

120

0

20

4060

80

100

120 0

20

4060

80

100

120 0

20

4060

80

100

120

92.29%Excellent rate of high

speed rail network


volume network


density network

94.8%Excellent rate of metro

subway network

91.38%Excellent rate ofcampus network

92.86%Excellent rate ofhighway network

Comprehensiveexcellent rate

“4H1C1M”

Excellent rateof browsing

Excellent rate ofvideo playing



0%

20%

40%

60%

80%

100%

Non-“4H1C1M”

Excellent rate trend of “4H1C1M”

Business Browsing Video playing Gaming Messaging

40%

60%

80%

100%

20%

0%00 01 02 03 04 05 06 07 08 09

Excellent rate trend of non-“4H1C1M”

Business Browsing Video playing Gaming Messaging

40%

60%

80%

100%

20%

0%00 01 02 03 04 05 06 07 08 09

4.3 MR ANALYSIS SUBSYSTEM (WIRLESS DOMAIN)

MR (Measurement Report) analysis is an important means of wireless network optimization. MR files

backfilled with user information and location information using triangulation positioning, fingerprint database

and OTT positioning technology to generates MR application table. The MR Analysis subsytem is used for

wireless coverage analysis, interference analysis and supports correlation analysis of MR and DPI, displays

10m*10m rasterized layer and analyzes the reasons for positioning wireless network to guide network

optimization.

Segment determination: able to achieve statistics and display ability in segmentation indicators such as DNS

delay, TCP delay, HTTP Get delay, page opening delay, etc.

Delinking: can realize the ability of index statistics and presentation of the whole sample in user terminal,

wireless IP-RAN, core network and SP business.

Positioning: able to realize the ability to process the predetermined bit information and output the

predetermined bit information on the basis of time, segment and boundary.

Business perception details example:

Example of community problem:

An example of an end-to-end analysis process:

Indoor coverage is affected by different building types, floor height, building density, signal penetration, and

to some extent tower location. As a result, indoor coverage quality has always been an important focus of

network optimization. Based on MR wireless quality analysis and combined with DPI business perception

analysis, the MR analysis subsystem can effectively guide the optimization of wireless networks and has a

positive effect on quality of expereince indoors.

MR coverage statistics:

4.3.1 INDOOR COVERAGE ANALYSIS

eNodeB SGW PGW

IP Bearer IP Bearer

Terminal Wireless Transport Core Transport Application Server

? ??? ?

MRanalysis

indoorAnalysis

of the

accurateplanning

The roadAnalysis

of the

17 18


Service awareness details

Region setting Scenario setting Scenario type Service class Service sub-class

Time granularity

Time City District/county Scenario type Service class Service sub-class Number of users(in person) Volume (in MB) Excellent rate

of browsing (%)Excellent rate of

page opening (%)Excellent rate offirst screen (%)

Details of excellent rate of services

All All All All

09-03Day Month Query Export

Non-“4H1C1M” Browsing

Web browsing

Web browsing

Web browsing

Web browsing

Web browsing

Web browsing

2018-09-03

2018-09-03

2018-09-03

2018-09-03

2018-09-03

2018-09-03

-

-

-

-

-

-

Ordinary Web browsing



Navigation of AutoNavi

Public use of Tencent

Navigation of AutoNavi

1167

1034

844

706

610

601

15103.66

17818.19

60998.16

701.21

4957.88

664.48

93.44

91.72

92.84

96.82

96.98

95.66

93.44

91.72

92.84

96.82

96.98

95.66

0

0

0

0

0

0

Segment determination, delinking, positioning of cell

Condition setting eNodeBID cellID Time granularity YesterdayDay Month Analysis

Terminal Side Wireless Side

Wireless coverage quality

A 64.04 0 70.00

Excellent rate of IPRANPoor network elements

% of the totalPoor quality alarm services

% of the total

IPRAN_A Core Network Service Side

Wireless Side delimits the Wireless Side by analyzing the cell wireless coverage quality, interference, warning and other information.

IPRAN_A delimits the IPRAN Side by analyzing the quality and alarm of the IPRAN equipment associated with the cell.

Core Network delimits the Core Network side by analyzing the IP quality and alarms of core network elements connected to the community.

The Service Side delimits the Service Side by analyzing service access and poor-quality alarm service percentage of the total in the cell.

Poor qualityalarm terminal

Poor quality% of the total


Total numberof terminals

Total volume / GB

Poor quality alarmterminal volume / GB

112.5%

0%

8

0

1.7

MR Coverage Statistic

Coverage Rate

RSRP <= -110dBmCollection Point Ratio

-110dBm<RSRP<=-85dBmCollection Ratio

-85dBm<RSRPCollection Point Ratio

SINR <=3dBmCollection Point Ratio

3dB<SINR<=15dBCollection Ratio

15dB<SINRCollection Point Ratio

RSRQ <=-12dBCollection Point Ratio

-12dB<RSRQ<=-6dBCollection Ratio

-6dB<RSRQCollection Point Ratio

100

0 72.5 27.5

0 22.5 77.5

27.5 67.5 5

20.03 -89.85 -9.73 5880

Average SINR Average RSRP Average SRQ Collection Points

Flow User

AlertManagement

FaultManagement

DispatchManagement

Networkflow control

VIP Userflow control

DNS Qualitycontrol

RegionalAssessment

Control

DNS Assessment

4.4 MONITOR ALARM SUBSYSTEM

The function of monitoring and warning provides real-time and quasi-real-time monitoring of VIP users, key

areas and major activity venues. The monitoring content includes: KPI index, KQI index, excellent and good

rate index, traffic, user number, etc.

This analysis is used for monitoring and quering the historical network and business-related data in real time

to understand the actual situation of the monitored systems. General monitoring objects include: network

unit flow, network billing quality, success rate of network attachment/paging /TAU, DNS request

frequency/success rate/delay, rate of first packet formation/delay, success rate/delay of first packet

penetration/overflow, etc.

Traditional road measurement can only analyze the quality of the air interface (terminal to base station). The

road scene analysis tools uses MR and DPI data to improve road cover analysis and provides end-to-end

QoE analysis and suggests improvement ideas. Road coverage and QoE improvement is an important part

of perception assessment. The combination of MR and DPI provides support for road users' perception

guarantee and road optimization.

Road sampling point rendering, raster layer:

4.3.2 ROAD COVER ANALYSIS

MR and DPI data not only can analyze and locate network faults, guide network optimization and improve

user perception, but also guide network construction. Based on the full data multi-dimensional analysis, it is

possible to locate coverage holes and QoE degradation, providing necessary data for accurate planning

support.

4.3.3 PRECISE PLANNING SUPPORT

4.4.1 MONITOR CONFIGURATION MANAGEMENT

4.4.2 ROUTINE MONITORING ANALYSIS

Support for adding, deleting, modifying,

enabling, disabling, and subscribing to

monitoring policies. The configured

monitoring content can include policy

name, monitoring object, user number,

specific monitoring metrics

(traffic/perceived business), and time

granularity (5/15/30 minutes).

19 20


Existing Base Station

Indoor and Outdoor

Outdoor

Indoor

Planning Base Station

Indoor and Outdoor

Indoor

Outdoor

Submit Cancel

New Configuration

Base Station

Monitoring granularity

Buinsess

Normal

Awareness

KPI

KQ

5 minutes 10 minutes

Base Station Group

Enterprise

Browsing

Attachment Rate

TCP

Video Information Gaming

Totalflow

Uploadflow

Downloadflow

Usernumber

InfiltratedUser

Type

Base Station ID

30 minutes

4.5 CUSTOMER SERVICE SUPPORT SUBSYSTEM

The customer service support subsystem focuses on two custmer scenarios. Users complain the network is

unavailable for internet use, and users complain the network is available but the perceieved experience is

poor.

Real-time monitoring and query of historical monitoring data for key monitoring objects. The monitoring

object analyzes the indicators of general category, perception category, KPI/KQI category, and business

category/small category, and can support the GIS thermal diagram to present the key monitoring indicators.

4.4.3 KEY MONITORING ANALYSIS

This function is mainly used to assist the customer service front desk staff to deal with customer complaints

automatically. Upon receiving a complaint, the customer service agent will ask a few simple questions to

analyse the fault. Customer service can quickly determine if the network is not available in the users’ area or

whether QoE is poor. The customer service agent can commence a background realtime analysis of the

network that creates a profile of the user, his/her experience and the problems that identified in causing the

quality or connection issue. The customer service agent can update the user with information on the reason

why he/she is having network performance issue. At the same time it also provide the analysis result to

network engineering team to investiage and to resolve the user’s complaint.

4.5.1 CUSTOMER SERVICE FRONT DESK COMPLAINT HANDLING

Flow

Awareness

Fault

ComplaintManagement

FaultAnalaysis Dispatch

Customer services- Management Zone

- Service Zone

- Sales Zone

User Pro-activeSupport- BS zone

- BS service

- BS terminals

- Reduce users complaints

21 22


Network Quality – TAU successful Rate (%) POOL Group1 Group2 Group3 HZ_POOL

018:02 18:06 18:10 18:14 18:18 18:22

HZ_POOL

18:26 18:30 18:34 18:38 18:42

20

40

60

80

100

Network Quality – DNS successful Rate (%)

018:02 18:04 18:06 18:08 18:10 18:12 18:14 18:16 18:18 18:20 18:22 18:24

20

40

60

80

100

Customer service front desk complaint handling

Unavailable network

User profile

Poor Internet awareness

2018-09-03 2018-09-03 Analysis

Basic user information: City

User terminal analysis: Terminal brand

Service location:

Terminal model: Terminal type: 4g

Factor identification

Factor no Factor item Factor value

Conclusion & suggestion

After querying, the attach 4G network signal of the

complaining user is abnormal. There might be reasons for

the unsuccessful service resumption after service

suspension. It is suggested to try the flight mode or restart

the terminal.

2

4

6

8

10

4G signaling available

Attach

Attach failure category

Authentication

Service suspended

Yes

Failure

Attach reject

Failure

No

4.6 THEMATIC ANALYSIS SUBSYSTEM

The thematic analysis subsystem is mainly based on the five special use

cases and unlimited package users. The content of the thematic analysis

includes perception analysis, network performance analysis, network

business analysis, user behavior analysis and so on.

By setting area, high-speed line, time granularity selection criteria to analyze and understand the high-speed

rail users/flow, high-speed rail users/flow, high iron users/high iron district business sense, GIS map display,

high speed rail station classification statistics, statistical indicators, MR covered all the lines of high-speed

users/traffic trends, high-speed rail users and weak TOP20 is detailed. And it allow to drill down to see the

quality of a poor cell KPI, KQI, cell user detailed data.

4.6.1 HIGH SPEED RAIL AWARENESS ANALYSIS

Analyzing time against certain network conditions such as data quality in high speed train environment. The

system will analyze the performance at high speed environment including user performance, access

performance, stability, user mobility, eHRPD (internetwork multiple access) performance and many other

indexes.

4.6.2 HIGH-SPEED RAIL NETWORK PERFORMANCE

In the high speed rail environment, it analyzes all kinds of business users/flow, statistics of all kinds of

business request/business hours, statistic browse/video/im/fine rate and the rate of good appraisal,

browse/video game business/im/games business users need the reason of this business problem was

judged and analyzed by drilling down the fixed section and demarcating to the three-fixed interface of the

business problem.

4.6.3 HIGH-SPEED RAIL BUSINESS ANALYSIS

High SpeedRail Use

CaseAnalysis

CampusNetwork

Use CaseAnalysis

HighDensity

User CaseAnalysis

MetroSubway

Use CaseAnalysis

Highvolume

Use CaseAnalysis

HighwayUse CaseAnalysis

perceptionAn overview

of

High-speed rail user identificationand analysisHigh speed rail lineperception analysis

User trajectory distributionAbnormal event analysis

Analysis of accessibility,maintenance and mobileperformanceTOP regional statistics

Analysis of business andterminal characteristicsUser behavior analysis

The userThe trajectory

behaviorCharacteristics

of the

performanceAnalysis

of the

23 24


High speed rail awareness Excellent rateof browsing

Excellent rateof video playing



Excellent rateof services

0%

20%

40%

60%

80%

100%

High-speed rail network performance

Top 20 cells by switch length Top 20 cells by TAU length High-fallback ehrpd cellHigh-drop cell

Time eNodeBID Base stationname

cellID Number of high-speedrail network users

High-speed rail networkuse volume (in MB) Number of drops View cell awareness

2018-09-05

2018-09-05

2018-09-05

2018-09-05

2018-09-05

2018-09-05

2018-09-05

2018-09-05

30

11

4

1

3

4

2

4

2888.061

6174.0555

1183.326

15.996

982.884

1588.134

70.7475

23.5935

3600

1200

600

600

600

600

300

300

View cell awareness

View cell awareness

View cell awareness

View cell awareness

View cell awareness

View cell awareness

View cell awareness

View cell awareness

Service statistics

0

2000

Webbrowsing

Number of users Volume (in MB)

Real-timeinteraction

Video Appstore

Othernetwork

applications

Onlinemusic

Onlinereading

Gaming Securitiestrading

P2Pdownload

Voip E-mailservice

WAPapplications

4000

6000

8000

10000

12000

0

30000

60000

90000

120000

150000

210000

180000

4.7 QUALITY ANALYSIS SUBSYSTEM

Quality analysis mainly provides professional analysis tools for operation and maintenance staff to locate the

faults in the 5G network. According to different network interfaces and signaling processes, the system

supports the statistical analysis of the corresponding interfaces, signaling process failure types, failure times,

proportion, and cause description within the query time, as well as the segmentation and demarcation.

High-speed rail user analysis to identify high-speed rail users to high-speed railway, signaling events,

business event statistics, abnormal event analysis, high-speed rail user business use analysis.

Example high-speed rail user trajectory:

Example of abnormal signaling events for high-speed rail users:

4.6.4 HIGH-SPEED RAIL USER ANALYSIS

This function calculates the health of the whole network through the score of health index factors in wireless

network, access network, core network and business platform. The health index is shown in the following

figure:

4.7.1 NETWORK HEALTH EVALUATION

4.6.5 5G SPECIAL ANALYSIS

5G user developmentanalysis

5G up to speed limitanalysis

5G user complaintanalysis Flow model evaluation

Analysis on the

development trend of 5G

users.

5G online connection

number, traffic/application

distribution statistics.

5G terminal quality

analysis/occupancy

analysis.

5G business frontier

analysis.

Automatic identification of

5G user signing rate,

automatic identification of

5G speed limit users.

5G user speed limit

verification, apn-ambr

comparison after speed limit

before speed limit, user

peak rate comparison.

PCRF policy issuance

verification, PGW policy

implementation QoS

mapping verification.

Detailed list of signaling

inquiries, signaling fault

location.

Detailed list of business

inquiries, traffic dispute

processing.

Support customer service

system docking capability.

Analyze and study the traffic

behavior model (HTTP

proportion/video proportion

/FTP download

proportion/streaming media)

under 5G network.

Effects of TCP out-of-order

/TCP retransmission /RTT

on network performance.

EPC Health Index, Network Quality, Data Quality

Root Cause analysis,chase back on user complaints

Signaling payback,bill enquiry

QualityAssessment

FaultAssessment

ProcessPayback

Network Health Check

Wireless AccessNetwork Core Network Business

Platform

TCP 2/3 H

andshaking Rate

Collateral R

ate

TAU R

ate

PDN

Rate

Voice Rate

TCP R

ate

TCP R

etransmission R

ate

TCP R

andom R

ate

DN

S Rate

HTTP R

ate

25 26


List of abnormal signaling events:

Time Event eNodeBID Base stationname

Number ofrequests

Number offailures

Successrate

Number ofdrops

Average timedelay

Querycell KPI

Querycell KQI

Querycell KQI

Query cellawareness

Query cellawareness2018-09-05 Query cell

KPIDrop 060

Export

The weight of health factor index is shown in the following table:

By setting the area and time granularity to analyze the indicators related to wireless network, access

network, core network and business platform, we can understand the overall quality of the network.

The main analysis of wireless network is TCP 2/3 handshake success rate/delay.

Success rate of access network is mainly aimed at attachment/TAU success rate trends, network elements,

failure cause analysis, adherent segment delimitation, root cause analysis, TAU failure period and bound,

etc.

The core network mainly carries out the trend, network elements and the success rate under scenarios for

the two indicators of success rate of PDN and success rate of session, It supports the analysis of failure and

segmentation-bound analysis.

Business platform analysis mainly includes indicator of trend analysis, TCP trend analysis, DNS/HTTP

request the time of the date and success rate analysis, business indicator analysis, failure cause analysis

and failure segment and boundary analysis.

Example of web health overview:

Five high network health degree example:

Examples of five high Internet health trends:4.7.2 COMPREHENSIVE QUALITY ANALYSIS

This function can be used to query and diagnose network problems or business system problems.

4.7.3 FAULT LOCATION

Reason Index Index Rate

Wireless

Access Network

Core Network

Business Platform

TCP 2/3 Handshaking Rate

Collerated Rate

TAU Rate

PDN Rate

Voice Rate

TCP Rate

TCP Retransmission Rate

TCP Random Rate

DNS Rate

HTTP Rate

100%

60%

40%

20%

80%

20%

10%

10%

20%

40%

27 28


Network health overview

Region setting Time granularityAll All 09-03Day Month Analysis

94.43%Networkhealth

Network health

5025 75

0 100

5025 75

0 100

5025 75

0 100

5025 75

0 100

100 Points 96.81 Points 98.17 Points 91.45 Points

Wireless network Access network Core Network Service platform

94.89“4H1C1M”

health

Campus Network

Scenario network health analysis

5025 75

0 100

92.07 Points

High Volume Network

5025 75

0 100

93.47 Points

High Speed Rail Network

5025 75

0 100

94.34 Points

High Density Network

5025 75

0 100

95.5 Points

Highway Network

5025 75

0 100

95.96 Points

MRT

5025 75

0 100

92.67 Points

“4H1C1M” network health trend

Network health Wireless network Access network Core Network Service platform

40

60

80

100

20

000 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Fault location

Service process

Region setting

Time granularity

Time Interface Signaling process Description of failure reason Number of failures % of total Delimitation

Scenario setting Scenario type

Interface type

Analysis

Network

All

Hour

2018-09-05 14 S1-MME Attach EPS services not allowed in this PLMN 2720 0.02 User

All All All

S1-MME Signaling process Attach Dimension setting Network element All

Day 2018-09-05 14:00 2018-09-05 14:00

This function can be used to query the detailed list of signaling/business information. Signaling and business

results include: all success, failure, timeout, etc. Support for drill-down from signaling backtrace field to

signaling process backtrace page.

4.7.4 DETAILED LIST QUERY FUNCTION

Support multiple user number input query, support 4G, VoLTE, Internet of things signaling interface’s

selection query. the user's signaling process message and signaling backtrace sequence diagram, support

multiple formats including: PCAP, Excel, picture, HTML, analysis of signaling process problems.

4.7.5 SIGNALING PROCESS BACKTRACKING

Trace the user's basic information, network indicators, detailed list analysis, failure cause analysis, cell

analysis, terminal analysis and other information data through business process, time granularity and

conditional input of complaint number.

4.7.6 CUSTOMER COMPLAINT TRACING

A user (18******* ****47) complained to 100xx customer service centre on September 5, 2018 about slow

intenet browsing speed and lagging during video streaming. Through the mobile Internet business

4.8.1 A CUSTOMER SERVICE SUPPORT USE CASE

Marketing support subsystem is used to view the data from the marketng team viewpoint. The marketing

team can determine different usage trends amongst different demongraphics and drill down into user

behaviour based on location, handset type, application usage and so on. This capability is critical in allowing

the marketing department to design new service plans and value added services to users to increase

revenues and user satisfaction.

4.7.7 MARKETING SUPPORT SUBSYSTEM

SharedHotspot

Dual SIMTerminals

Popular businessservice

Business user

Travel user

High SppedRail User

Campus user

ForeignUsers

Home returnrush time

4.8 TYPICAL CASES

29 30


Detailed list query function

Detailed list of Internet awareness services Detailed list of online awareness signaling Detailed list of log retention

Accessarea

Basestation ID

Base stationname cellID Source

IPSource

portDestination

IPDestination

portSGW

IPPGW

IPDuration(in ms)

Upstreamvolume

Downstreamvolume

Upstreampackets

Downstreampackets

800Midentification

Reasonfor ending

51897

51889

51891

106.11.250.66

106.11.250.66

106.11.250.66

80

80

80

80

69

77

1683

1734

1750

792

1136

887

7

7

7

6

6

6

No

No

No

Service application

endsService

application ends

Service application

ends

Start time

User

2018-09-05 17:38:53 End time 2018-09-05 18:38:53

InterfaceMobile phone no S1-U Application type HTTP Analysis

Customer complaint tracing

Service process

Signaling analysis

Interface Signaling process Number of requests Number of failures Time delay

S1-MME

S1-MME

S1-MME

UE Context Release

Initial Context Setup

Sevice Request

47

36

36

24

12

12

402

328

192

UserNetwork User no Start time 2018-09-05 17:38:53 End time 2018-09-05 18:38:53 Analysis

A network operator was receiving complaints that network performance was below the expected quality in a

certain region and a notice of poor quality was issued to engineering to investigate. To resolve the problem

engineering need to efficently locate the cause of poor quality in the district investigating which ENodeBID:

30xxxx, cellID: xx, etc that is causing the problem. Using the mobile Internet business perception analysis

system, engineering was quickly able to idenitfy and confirm that the perception of experience by the users

was in fact very poor. They were able to determine the network was only performing at 63.2% of expected

performance and the preliminary analysis also showed video quality was especically poor.

The cell was further analyzed and investigated. Through systematic analysis, it was found that the terminal,

wireless side signal strength showed good signal quality, IPRAN and core network were all excellent, which

was not the reason for the poor quality of the cell, but the business side showed poor.

Through systematic analysis and judgment using the service analyis system, network indicators confirmed

that users experience was being impacted and that overall perception of quality for the affected cell was in

fact poor. The details of the overloaded business support systems was sent to the relevant departments and

the problem was quickly solved restoring network performance back to the expected level.

In this white paper, we analyse the broadband and mobile data market requirement and the importance of

network visibility for impoving customer experience as well as introduce GreeNet’s iNAS solution, it’s

technical implementation, product architecture, major functions, and application scenarios.

After further analysis, it is found that in the business systems supporting this cell, are overloaded wth high

flow rate causing poor network quality.

Poor quality service download rate is lower, caton good rate is higher.4.8.2 NETWORK PERFORMING BELOW EXPECTED QUALITY

perception analysis system platform, the customer service centre was able to determine that due to the weak

wireless signal at the user's position when using the network, the user’s quality of experience was affected.

The customer service centre was able to provide the user with an explanation of the poor network

performance and also send the fault details to engineering team for investigation and resolution.

Complaint user model and analysis conclusion:

31 32


Segment determination, delinking, positioning of cell

Condition setting eNodeBID cellID Time granularity YesterdayDay Month Analysis

Service sideTerminal Side

Poor rate of terminals

0.00

Terminal Side delimits the terminal side by determining the terminal type within the cell and the percentage of poor-quality terminals

Poor-qualityservice / type



All services / type

Total volume / GB

Poor-qualityvolume / GB

8032.79%

75.26%

244

1.43

1.9

Wireless side

Wireless coverage quality

EXCELLENT

Wireless Side delimits the Wireless Side by analyzing the cell wireless coverage quality, interference, warning and other information

IPRAN_A

Excellent rate of IPRAN

0

IPRAN_A delimits the IPRAN Side by analyzing the quality and alarm of the IPRAN equipment associated with the community

Core network

Core network elementIP quality

0

Core Network delimits the Core Network side by analyzing the IP quality and alarms of core network elements connected to the community.

Customer service front desk complaint handling

Unavailable network

User profile

Poor Internet awareness

2018-09-03 2018-09-03 Analysis

Basic user information: City; Service location; Network availability: poor

User terminal analysis: Terminal brand; terminal model; terminal type: 4g

Users’ TOP5 services: AutoNavi

User cell analysis: Cell served: 1; including 1 cell of poor quality

User sharing hotspot: No volume sharing occurred

User fallback analysis: No fallback to 3G

Network timeliness: Network stability: Good

Excellent rate of user experience awareness: cross

Number of users of same terminals: 3227

Excellent rate of overall terminal awareness: 91.51

Is It a terminal of poor quality or not: No

TOP 3 poor-quality services: AutoNavi

Poor-quality service location: 1, Poor--quality cell: 1

Factor identification

Factor no Factor item Factor value

Conclusion & suggestion

Factor combination

Factor combination: Poor-quality cell & weak-coverage cell

Main factor: Poor-quality cells caused by suspected signal problems

After analysis, it is determined that the poor Internet awareness of the complaining user is mainly caused by the poor-quality cell with weak coverage. It is suggested to adjust the cell signal coverage strength.

1

3

Poor-quality alarm cell

Weak-coverage cell

Yes

Yes

Cell awareness details

Region setting

Time granularity

Details of excellent rate of cells

Time City District/county eNodeBID cellID Cell nameNumber of

users (in person)Volume(in MB)

Excellent rateof services (%)

Excellent rateof browsing (%)

Excellent rateof video playing (%)

Excellent rateof messaging (%)

Excellent rateof gaming (%)

Scenariotype

Scenarioinformation

Scenario setting Scenario type

Query Export

All

Optional additional conditions Users

Day

2018-09-05 8 681.51 63.20 76.36 17.11 98.92 100.00

All “4H1C1M”

Today

All AllScenario information

Month

Comparison of poor-quality services

0

5

Volume (in MB) Excellent rate

10

15

20

25

30

0%

20%

40%

60%

100%

80%

Volume % of the total

Browsing Messaging GamingVideo playing

Service class Service sub-class Poor quality or not Number of users Volume (in MB) Excellent rateof video playing

Excellent rate ofdownload speed

Excellent rateof lagging

Video playing

Video playing

Video playing

Yes

Yes

Yes

11

8

8

13.6737

72.9981

88.3748

0

12.94

19.66

0

0

8.62

0

64.71

63.79

White paper Intelligent Network Awareness System (iNAS)-尺寸 ...

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