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White Paper Intelligent Network Awareness
System (iNAS)
MANAGING CONNECTIONS SMARTER
UZBEKISTAN
Uzbekistan Tashkent City,
Mirabad District, 14 Oybek ko'chasi
Tel: +998 93 001 2248
Email: [email protected]
WUHAN
Fl 4, Building E2, 4 Middle Software Park
Road Optics Valley Software Park, East Lake
High-tech Development Zone, Wuhan, China
Tel: +86 27 8780 9610
Email: [email protected]
HONG KONG
Unit 1001, Mira Place Tower A,
132 Nathan Road,
TST, Hong Kong
Tel: +852 2824 8753
Email: [email protected]
BEIJING
603, CLP Information Building,
6 Zhongguancun South Street,
Haidian District, Beijing, China
Tel: +86 10 6870 9986
Email: [email protected]
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
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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.
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White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System
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
White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System
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;
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White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System White paper Intelligent Network Awareness System (iNAS) —— Intelligent Network Awareness System
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
Overview view example -2
Overview view example -3
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
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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
93.38%Excellent rate of high
volume network
93.15%Excellent rate of high
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
Excellent rateof messaging
Excellent rateof gaming
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
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White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
Ordinary Web browsing
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
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
High speed rail awareness Excellent rateof browsing
Excellent rateof video playing
Excellent rateof messaging
Excellent rateof gaming
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
White paper Intelligent Network Awareness System (iNAS) —— Solution Features White paper Intelligent Network Awareness System (iNAS) —— Solution Features
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
Poor quality% of the total
Poor quality% of the total
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