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Analyzing the NetworkFriendliness of Mobile Applications
version 1.0
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Analyzing the Network Friendliness
of Mobile Applications
Issue: 1.0
Date: 2012-07-05
Author: Song Jiantao
Email: songjiantao@huawei.com
Summary ..............................................................................................................................1
1 Preface ..............................................................................................................................2
2 Application Network Friendliness Optimization System .....................................................4
3 Method for Analyzing the Network Friendliness of Applications ........................................6
4 Apps Insider ....................................................................................................................10
4.1 Description ...................................................................................................................................................10
4.2 Apps Insider Case Study ................................................................................................................................12
5 Healthy Development of the Application Network Friendliness Optimization System .......14
6 References .......................................................................................................................16
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Summary
In recent years, the development of smartphones has made great progress. Mobile
application development for smartphones has become the next big thing in a post-PC
era. But since most developers are lacking in their understanding of mobile networks
and the behaviors of applications on mobile networks, these applications may be
unfriendly towards mobile networks and cause the following problems: high device
power consumption, frequent signaling storms on mobile networks, low utilization
efciency of network resources, deterioration in user experience, and threats to user
privacy and network security.
To guide the development of network-friendly applications, the GSM Associationhas released Guidelines for Development of Network Friendly Applications, and
hosted a Smarter App Challenge event. To further cultivate an application network
friendliness optimization system, Huawei has provided the Apps Insider, an automatic
tool for analyzing the network friendliness of applications. This white paper introduces
the methodology of analyzing network friendliness, describes the Apps Insider toolkit,
and gives a case study.
The Apps Insider assesses the network friendliness from the perspective of user
experience and network impact respectively, including assessment indexes like user
experience, device power consumption, signaling consumption, trafc consumption,
connection consumption, multi-radio capability (UMTS/LTE/Wi-Fi), privacy and security.
The Apps Insider adopts the client/server model: clients collect the measurementresults and report them to the server; the server calculates the average network
friendliness score of each application based on the score of each assessment index
and its weight; then it ranks applications by network friendliness. By analyzing the key
network friendliness indexes, the Apps Insider provides suggestions on application
development and network optimization to improve network friendliness.
Industry partners including developers and operators are welcome to use the Apps
Insider in Huaweis mLAB to improve the network-friendliness of applications.
Huaweis mLAB will also regularly release the network friendliness ranking of popular
applications and provide suggestions for optimizing their network friendliness.
Through continuous efforts to analyze, assess, optimize, and manage the network
friendliness of mobile applications, win-win outcomes for operators, application
developers, device vendors, network equipment suppliers, and end users will be made
possible.
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1 Preface
In recent years, the development of smartphones has made great progress. According
to Gartner, Inc., worldwide smartphone sales to end users soared to 472 million
units in 2011, up 58% from the previous year. Mobile application development
for smartphones has become the next big thing in a post-PC era. Most mobile
applications are developed based on the experience of fixed network applications.
As a result, these applications may be unfriendly towards mobile networks and cause
the following problems: high device power consumption, frequent signaling storms
on mobile networks, low utilization efficiency of network resources, deterioration
in user experience, and threats to user privacy and network security. To develop
friendly applications for mobile networks, developers must have a comprehensiveunderstanding of mobile networks and the behaviors of applications on mobile
networks.
On UMTS networks, UE (user equipment) has two basic operation modes: idle and
connected. In idle mode, the UE is in standby mode, no service is running, and the UE
is not connected to the Universal Terrestrial Radio Access Network (UTRAN). When
the Radio Resource Control (RRC) connection is set up, the UE is switched to the
connected mode. In connected mode, the UE has the following states: Cell-DCH, Cell-
FACH, and Cell/URA-PCH. In the Cell-DCH state, the UE has a dedicated channel (DCH
or HSPA) and consumes more power to transmit data at a faster rate. In the Cell-FACH
state, the UE consumes less power as data is transmitted at a lower rate. In the Cell/
URA-PCH state, the UE has no data transmitted on either the uplink or the downlink
and needs only the same amount of power as idle mode to retain the connection.
To efficiently utilize wireless resources and reduce power consumption, RRC state
transitions can be performed by using the dynamic channel allocation algorithm based
on the occupied radio link control (RLC) buffer. Signaling consumption varies according
to the state transition path. To avoid a ping-pong state transition, the DCH2FACH
transition is triggered when the deactivated timer T1 (2 to 10 seconds) expires after
data transmission is completed on the DCH/HSPA. Similarly, the FACH2PCH transition
is triggered when the deactivated timer T2 (2 to 10 seconds) expires.
The following is an example of the network-unfriendliness of a mobile application:
A social network application obtains the updates of friends by frequently polling the
server (known as a heartbeat). The heartbeat has little impact on xed networks. On
a wireless network such as the UMTS, however, a complete signaling process must be
performed for each heartbeat, for every upward transition (PCH2FACH, FACH2DCH)
and downward transition (DCH2FACH, FACH2PCH) of the RRC state on the signaling
plane. The user may obtain no valuable information from the heartbeat (his/her friends
may make no updates during heartbeat intervals), and the power and trafc are simply
wasted. Frequent heartbeats cause frequent RRC state transitions, consuming large
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quantities of signaling resources and increasing the CPU load of the interface board on
the control plane.
Currently, network friendliness design and the optimization of mobile applications have
gained extensive attention in both academic and industry elds. The GSM Association
has created the Guidelines for Development of Network Friendly Applications. AT&Ts
research institute has developed the Mobile Application Resource Optimizer (ARO) in
cooperation with University of Michigan. The AT&T ARO consists of a data collector
and data analyzer. The data collector tests applications by using a UE and records TCPsessions, HTTP packets and contents, screen video (3 fps), user input, battery level,
and GPS/camera/Bluetooth usage, and sends the test results to the data analyzer. The
data analyzer analyzes the impacts of mobile applications on wireless networks and
device power consumption based on the wireless network model and device power
consumption model.
Huaweis mLAB provides a wireless network environment that integrates GSM, UMTS,
LTE and Wi-Fi networks with the Apps Insider, an automatic tool for analyzing the
network friendliness of applications. With the wide use of smartphones, malware is
used to intrude on users privacy and attack wireless networks, posing great threats
to mobile users and network security. The Apps Insider assesses user experience,
power consumption, signaling consumption, traffic consumption and connection
consumption of applications in terms of multi-radio (UMTS/LTE/Wi-Fi) capability,privacy and security.
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Analysis
Use the Apps Insider to
analyze the UMTS/LTE/
Wi-Fi throughput rate,
RRC state transition
process, DL/UL signaling
consumption, terminal
power consumption,
connection consumption,
and privacy and s ecurity,
and save the analysis
results into the database.
Dene baselines for assessing
the user experience, terminal
power consumption,
signaling consumption, owconsumption, connection
consumption, multi-radio
capability, and privacy and
security, compare the analysis
results with the baselines,
score the network friendliness
of each application according
to the weight of each index,
and arrange the order of
applications according to the
aggregated score.
Identify key network
friendliness indexes
based on the comparison
result of each network
friendliness index with
its baseline, analyze the
key network friendlinessindexes, and provide
suggestions on application
development and network
optimization.
Establish an open
application network
friendliness assessment
center based on the
mLAB and formulate
regulations for assessing
and testing the network
friendliness of applications
to help developers publish
applications on application
stores, such as App Store
and Google Play.
Assessment
Optimization
Management
2 Application Network FriendlinessOptimization System
Currently, most mobile applications are developed based on the experience of xed
network applications. Unfortunately, developers tend to typically lack an understanding
of mobile networks and the behavior of applications on mobile networks. As a
result, these applications may be unfriendly towards mobile networks and cause the
following problems: high terminal power consumption, frequent signaling storms on
mobile networks, low utilization efciency of network resources, deterioration in user
experience, and threats to user privacy and network security. Huaweis mLAB aims to
build an application network friendliness optimization system that can analyze, assess,
optimize and manage the network friendliness of mobile applications to create win-
win outcomes for operators, application developers, device vendors, network vendors
and end users.
Figure 2-1 Application network friendliness optimization system
Operator
Operator
Terminal
Terminal
Network
Network
User
Fighting alone
Win-Win
User
Application
Application
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Analysis
Analyzes the following data and saves the analysis results into a database: Traffic
Consumption (DL/UL, UMTS/LTE/Wi-Fi), RRC state transition, session number
consumption, device power consumption, privacy and security measures.
Assessment
Dene baselines for assessing user experience(key QoE indexes for different application
categories, e.g. MOS for VoIP, Page Loading Latency for web browsing), device
power consumption, signaling consumption, traffic consumption, session number
consumption, multi-radio capability, as well as privacy and security, and compares the
analysis results with the baselines, scores the network friendliness of each application
according to the weight of each index, and arranges the order of applications
according to their aggregated score.
Optimization
Identies key network friendliness indexes based on comparison results and provide
suggestions on application development and network optimization.
Management
Based on statistical analysis, app platforms like the Apple App Store and GooglePlay would be able to formulate required network friendliness rules and criteria
for published apps. End users would also be able to select apps according to their
friendliness ranking.
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3 Method for Analyzingthe Network Friendliness ofApplications
The method for analyzing the network friendliness of applications is as follows:
Collect data from the client and assistant tools (RNC signaling tracing tool, Agilent
power consumption analyzer and Wireshark packet catcher), assess the applications in
terms of user experience, device power consumption, signaling consumption, trafc
consumption, connection consumption, multi-radio capability (UMTS/LTE/Wi-Fi), as
well as privacy and security, then score the network friendliness of each application
according to the weight of each index, and arrange the order of applications by
aggregated score.
Figure 3-1 Method for analyzing the network friendliness of applications
Application /Classifcation
Weight ofassessment indexes
Data collectionand processing
Assessment andranking
Assessmentindexes
Optimizationsuggestions
Whatsapp/IM Wi-Fi DL/UL throughput rate trafc consumption Weight of trafc consumption
Assess the networkfriendliness of applications
User experienceassessment report
Network impactassessment report
Network optimizationsuggestions
Application developmentsuggestions
iMessage/IM3G/LTE DL/UL throughput Connection consumption Weight of connection
consumption
Rank applications ineach type
viber/VoIPRRC state transition User experience Weight of user experience
Identify key networkfriendliness indexes
Youtube/Video
Duration of each RRC state Multi-radio capability Weight of multi-radio
Facebook/SNS
Signaling quantity Signaling consumption Weight of signalingconsumptionSafari/Web
Power consumption analysismodel
Terminal power consumptionWeight of terminal power
consumptionGmail/MAIL
APP security analysis Privacy and security Weight of privacy and securityiCloud/Cloud
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The procedure for analyzing the network friendliness of applications is as follows:
Step 1 Classify applications and set the typical usage profile. In this step, select
the applications to be tested, record the phone model, operating system version,
name, and version of each application, and classify the applications. Applications can
be classied into the following types and their corresponding usage prole could be
predened.
Step 2 Collect and process raw data. In this step, select various test scenarios
including foreground with no user input, typical usage scenarios(multiple predened
usage proles), and background, collect the raw data with assistant tools (air interface
signaling analyzer, Gi interface IP packet analyzer, and Agilent power consumption
analyzer) and the data reported by devices, then save the data into the database.
Calculate assessment indexes from raw data and save in database, re-generate
baseline for each index.
Application
CategoryExamples Usage Proles in Active State: Key Characteristics
Instant Messaging
(IM)
WhatsApp, MSN, QQ,
iMessage
Send/Receive Messages Per Hour
Average Message Size(MB)
Average Messaging Interval(Seconds)
Social Networking
Services (SNS)
Facebook, Twitter, Sina
Average User Prole Browsing Times Per Hour
Average Number of Posts/Comments Per Hour
Sent/Received with/without Pictures(Y/N)
Video Netix, Youtube, Youku
Video File Formats(MP4/FLV/H.264/)
Average Video File Size(MB)
Average Video Duration(Minutes)
VoIP Skype, Viber Average Call Duration(Seconds)
Web BrowsingChrome, IE, Safari,
Firefox, Opera, UCWeb
Average Page Size(MB)
Average Dwell Time on a Page(Seconds)
Cloud ServiceDropbox, Google Drive,
iCloud, SkyDrive
File Type of Syncing(DOC/PICTURE/CONTACTS/)
Average File Size(MB)
Average File Syncing Interval(Seconds)
Email Hotmail, Gmail
Average Mail Size(MB)
Average Number of Mails Sent/Received Per Hour
Average Mail Transfer Interval(Seconds)
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Figure 3-3 Device power consumption and signaling consumption estimation
based on RRC state transition
260mA
116mA
IDLE2D
F2D
P2F
D2FT1 = 2~10s
T2 = 2~10s
T3
F2PP2IDLE
3mA
RRC State TransitionProcess
SignalingQuantity
P2F;F2P 7
P2F;F2D;D2F;F2P 28
IDLE2D; D2F; F2P; P2IDLE 35
DCH/HSPA
FACH
PCHIDLE
Power consumption
baseline in each RRC state
Signaling consumption
NOTE:
1. User Experience as assessment indexes excludes device power consumption, privacy
and security, includes voice quality for VoIP service, page loading latency for web
browsing, and video quality for video service.
2. Power consumption of a device can be calculated based on the power consumption
baseline of each RRC state and the RRC state transition process instead of using the
power consumption tool which is more efcient but less accurate than using power
consumption reported by the device/test tool.
NOTE:
Power consumption baseline in each RRC state (Y axis) is the default value (based on
mLAB test results) for estimation and could be changed with conguration GUI.
Figure 3-2 Mapping from Raw Data to Assessment Indexes and from Indexes to
Criteria
Collected RawData
AssessmentIndexes
AssessmentCriteria
Wi-Fi DL/UL throughput(bps)
3G/LTE DL/UL throughput
(bps)
RRC state transition
Duration of each RRCstate (ms)
Signaling quantity
User experience*
Trafc consumption
Power consumption
analysis model
Multi-radio capability
Session consumption
MOS measurement
Signaling consumption
Network impact
TCP/UDP connections
Device powerconsumption*
User experience
TLS/SSL/ Encryption
Privacy and security
Privacy and security
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Assess the network friendliness of each application and arrange the order of
applications by network friendliness. In this step, calculate the overall network
friendliness score of each application based on the score by each assessment index
and weight of each assessment index, and arrange the order of applications by
network friendliness in each type.
Set the weight of each assessment index according to the application type.
Step 4 Provide optimization suggestions. In this step, analyze the key network
friendliness indexes, and provide suggestions on application development and network
optimization to improve the network friendliness of applications.
NOTE: More black area means higher weight
IM App1 Scores
indexes baseline score
Video App2 Scores
indexes baseline score
SNS App3 Scores
indexes baseline score
Step 3 Score and Ranking
Compare with baseline to score the network friendliness of each application by
each index. Each index is evaluated by its ranking sorted by the network impact
and user experience, and the score of each index is calculated by its ranking result.
The higher the network impact, the smaller the score. We set the score range from
0 to 5, with a maximum score being 5.
App
Category
Network Impact User ExperiencePrivacy &Security
Trafc Session Signaling Multi-RadioUser
ExperiencePower
ConsumptionPrivacy &Security
IM
VoIP
Web
Video
SNS
*
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4 Apps Insider
To accurately analyze the network friendliness of smartphones and applications,
Huaweis mLAB provides a wireless network environment that integrates GSM, UMTS,
LTE and Wi-Fi networks with the Apps Insider, an automatic tool for analyzing the
network friendliness of applications.
4.1 Description
The Apps Insider adopts the client/server model. The Apps Insider clients collect the
measurement results and report them to the server. The Apps Insider server calculates
the average network friendliness score of each application based on the score of each
assessment index and its weight, and ranks the applications by network friendliness.
By analyzing the key network friendliness indexes, the Apps Insider providessuggestions on application development and network optimization to improve the
network friendliness of applications.
Figure 4-1 Framework of the Apps Insider
User experience
assessment report
Network impact
assessment report
Networkoptimization
suggestions
Application
development
suggestions
Apps Insider
server
Analyze the
networkfriendliness of
applications
Selectassessment
objectsRNC
NodeB
CN
mLAB Apps Insider toolkit
Apps Insider
client
IM Video
SNS
VoIP
Gi
interface
IP packetanalysis
Airinterface
signalinganalysis
Power
consumptionanalysis
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Apps Insider client
The Apps Insider clients include applications on Android, iPhone and Windows
smartphones. Select the application to be assessed, for example: Sina Weibo. Classify
the application, for example: Sina Weibo is a Social Networking Service (SNS). Set test
scenarios such as foreground with no user input, normal use, background, start time
and end time. Select assessment objects, such as the E2E delay, RTT, DNS response
time, DL/UL trafc and power consumption. Report measurement results to the server.
Display nal assessment results.
Apps Insider assistant tools
The Apps Insiders assistant tools include the air interface signaling analyzer, Gi
interface IP packet analyzer, Agilent power consumption analyzer, and MOS analyzer.
Apps Insider server
The Apps Insider server collects information from the device, power consumption
analyzer, and Gi interface IP packet analyzer, and saves the information into a
database. Define baselines for assessing the network friendliness of applications
in terms of user experience, device power consumption, signaling consumption,
traffic consumption, connection consumption, multi-radio capability, and privacy
and security, and provides suggestions on application development and network
optimization.
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Rank applications by network friendliness.
Figure 4-2 Ranking of SNS applications by friendliness
4.2 Apps Insider Case Study
This case analyzes the network friendliness of three popular SNSs (AppA, AppB
andAppC) on the iPhone and Android.
NOTE: User experience indexes vary by type of application. VoIP user experience is
assessed by the MOS analyzer.
Log in to AppA, AppB andAppC, and perform no operations within 20 minutes in
the foreground.
Phone ModelOperating
System
Mobile
Application
Application
Version
Application
TypeTest Scenario
Test Duration
(Minutes)
iPhone4 iOS 5.0 AppA a SNS Foreground 20
iPhone4 iOS 5.0 AppB b SNS Foreground 20
iPhone4 iOS 5.0 AppC c SNS Foreground 20
Huawei Honor Android 2.3.6 AppA d SNS Foreground 20
Huawei Honor Android 2.3.6 AppB e SNS Foreground 20
Huawei Honor Android 2.3.6 AppC f SNS Foreground 20
Assessment Dimension Value RangeWeight (a default value congurable according to
application type)
Connection quantity (0, 5] 0.1
Trafc consumption (0, 5] 0.2
Signaling consumption (0, 5] 0.2
User experience* (0, 5] 0.1
Terminal power consumption (0, 5] 0.2
Multi-radio capability (0, 5] 0.1
Privacy and security (0, 5] 0.1
Dene the weight of each assessment index.
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As shown in Figure 4-2, AppCs iPhone app ranks last. According to the analysis
results of key friendliness network indexes, the device power consumption and
signaling consumption have great impacts on the ranking of applications. AppCs
iPhone app queries the server every 30 seconds (heartbeat) to check whether any
updates have been made. Each heartbeat causes the PCH2FACH state transition. The
data transmission on the FACH lasts for 3 seconds. After the data is transmitted, the
FACH2PCH state transition is triggered when the deactivated timer T2 expires.
The optimization suggestions on AppCs iPhone app are as follows:
The 30-second interval is too short. Increase the heartbeat interval.
Use the push mechanism instead of the poll mechanism. Then the server pushes
the information to the client through the long TCP connection when any update is
made on the server.
The optimization suggestions on wireless networks are as follows:
Adopt the smart state transition scheme and optimize the network conguration
parameters.
Identify heartbeats in self-learning mode.
Dynamically set the deactivated RRC state transition timers (T1, T2 and T3)
according to heartbeats in self adaptation mode.
Analyze key network friendliness indexes.
Provide optimization suggestions.
Figure 4-3 Analysis results of the IP traffic, state transition and power
consumption of AppCs iPhone app
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Figure 5-1 Smartphone Ecosystem Collaboration
5 Healthy Development of the
Application Network FriendlinessOptimization System
With the fast development of smartphones and mobile applications, the network
unfriendliness of smartphones and mobile applications are wasting large quantities
of precious mobile network signaling resources and wireless resources, affecting
user experience and posing a threat to user privacy and network security. To guide
the development of network-friendly applications, the GSM Association released
Guidelines for Development of Network Friendly Applications, and AT&T hasdeveloped the ARO and provided optimal practices. To build an application network
friendliness optimization system, Huawei launched the mLAB, a mobile broadband
(MBB) innovation laboratory. The mLAB provides a wireless network environment that
integrates GSM, UMTS, LTE and Wi-Fi networks and the Apps Insider, an automatic
tool for analyzing the network friendliness of applications.
How to utilize network friendliness evaluation platform in Huaweis mLAB? Three
options including open environment, evaluation service and joint research are
available.
Open Environment
Operators
NetworkVendors
Device VendorsPlatformProviders
CollaborationApp Developers
Evaluation Service Joint Research
Access mLAB network friendliness evaluation
platform through Internet
Install application client on top of mLAB
simulated device OS (Software Device)
Offered Services to
Analysis specic application network
friendliness
Provide valuable network optimization
and application design suggestions
Setting up joint research project between
partners and mLAB
Agreed objective and project time plan/
delivery
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Figure 5-2 Features of Huaweis mLAB Network Friendliness Evaluation Platform
Industry partners including developers, device vendors, platform providers and
operators are welcome to use mLAB resources to analyze the network-friendliness of
applications, enhance the network friendliness of applications in terms of application
development and network optimization, and improve user experience. The mLAB is
also willing to establish an open application network friendliness assessment center
and help developers publish applications for platforms like the Apple App Store
and Google Play. The mLAB will regularly release the network friendliness ranking
of applications and provide suggestions for optimizing the network friendliness of
applications. Users can also refer to this ranking when selecting applications.
Huawei is committed to building a harmonious MBB ecosystem with friends in the
industry and boosting the prosperity and development of the MBB industry, said
Wang Tao, President of Huaweis Wireless Network Product Line.
Assistant Tools
Real WirelessNetwork
Environment
Full Choices ofDevices/OS
NetworkOptimization&Application
DesignSuggestions
Air interface signaling analyzer
Gi interface IP packet analyzer Agilent power consumption analyzer
With different access technology( Wi-Fi/
UMTS/LTE/GSM)
Flexible network features conguration (e.g.
3GPP R8 FD)
Abundant device types, OS versions
Based on mLAB research in popular
applications user behavior and network
impact
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6 References
1. Smartphone Challenge: Guidelines for Development of Network Friendly
Applications, Version 0.11. GSM Association Ofcial Document TS.20, November
2011
2. http://developer.att.com/ARO
3. F. Qian, Z. Wang, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. Profiling
Resource Usage for Mobile Applications: a Cross-layer Approach. In MobiSys,
2011
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A Acronyms
D
DCH Dedicated Channel
DL Downlink
F
FACH Forward Access Channel
G
GPS Global positioning system
GSM Global system for mobile communications
H
HSPA High Speed Packet Access
HTTP Hyper Text Transfer Protocol
I
IM Instant Messaging
IP Internet Protocol
L
LTE Long Time Evolution
M
MOS Mean opinion score
P
PCH Paging Channel
Q
QoE Quality of experience
RRNC Radio Network Controller
S
SNS Social Networking Services
T
TCP Transmission Control Protocol
U
UE User Equipment
UL Uplink
URA UTRAN Registration Area
V
VOIP Voice Over IP
W
Wi-Fi Wireless Fidelity
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Copyright Huawei Technologies Co., Ltd. 2012. All rights reserved.
No part of this document may be reproduced or transmitted in any form or by any means without prior written consent of Huawei Technologies Co., Ltd.
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without limitation, statements regarding the future nancial and operating results,
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that could cause actual results and developments to differ materially from those
expressed or implied in the predictive statements. Therefore, such information
is provided for reference purpose only and constitutes neither an offer nor an
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, HUAWEI, and are trademarks or registered trademarks of Huawei Technologies Co.,
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Tel: +86-755-28780808
Version No.: M3-001034414-20120731-C-2.0