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Page 1: Mobile Broadband Review 2014H1 - huawei · Mobile Broadband Review 2014H1 Contents i Contents 1 Introduction..... 1

Mobile Broadband Review 2014H1

Page 2: Mobile Broadband Review 2014H1 - huawei · Mobile Broadband Review 2014H1 Contents i Contents 1 Introduction..... 1

Mobile Broadband Review 2014H1 Contents

i

Contents

1 Introduction.................................................................................................................................... 1

2 Network Insights........................................................................................................................... 2

2.1 PS Traffic Models in Different Networks ..................................................................................................................... 2

2.1.1 PS Signaling Increasing Dramatically in 4G Networks ............................................................................................. 2

2.1.2 Network Architecture Changes Contributing to Signaling Increases ......................................................................... 3

2.2 RAN Traffic Models in Different RATs ........................................................................................................................ 4

2.2.1 Status for UMTS and LTE Network Rates ................................................................................................................ 4

2.2.2 Reasonable Number of Subscribers Helping Increase LTE Spectrum Efficiency...................................................... 5

2.3 Traffic Distribution of Typical LTE Networks .............................................................................................................. 6

2.3.1 Significant Difference in Traffic Distribution of LTE Networks ............................................................................... 6

2.3.2 10% Video Consumption in an LTE Network Higher Than That of UMTS .............................................................. 8

3 Experience Insights ....................................................................................................................... 9

3.1 Status for Live Network Experience ............................................................................................................................. 9

3.1.1 Network Experience Improvements Lower Than Air Interface Capability Enhancement ......................................... 9

3.2 Influencing Factors ..................................................................................................................................................... 10

3.2.1 Air Interface Bandwidth and Network Architecture Determining User Experience ................................................ 10

3.3 Progress in the Acceptance Test Criteria of Experience Coverage ............................................................................. 11

3.3.1 Operative and Available Quota Commitment .......................................................................................................... 11

3.3.2 Practice .................................................................................................................................................................... 11

4 User Behavior Insights ............................................................................................................... 13

4.1 Time Distribution of Video Playing ............................................................................................................................ 13

4.1.1 More Smooth Time Distribution of Video Playing in 4G Network Than in 3G and Wi-Fi ..................................... 13

4.2 User Behaviors in Video Playing and Microblog ....................................................................................................... 14

4.2.1 More Video Consumptions in 4G than in 3G ........................................................................................................... 14

4.2.2 VIP's Influence Higher than Other Users in Microblog ........................................................................................... 14

4.3 Microblog Users' Behavior Trend ............................................................................................................................... 15

4.3.1 Number of Chinese Characters ................................................................................................................................ 15

4.3.2 Proportion of Microblogs Containing Images ......................................................................................................... 16

4.3.3 Individual Users Forwarding More Than VIP Users ............................................................................................... 17

5 Appendix ...................................................................................................................................... 18

5.1 Overview .................................................................................................................................................................... 18

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Mobile Broadband Review 2014H1 Contents

ii

5.2 Data Sources ............................................................................................................................................................... 18

5.3 Contact Information .................................................................................................................................................... 18

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Mobile Broadband Review 2014H1 Figures

iii

Figures

Figure 2-1 Comparison of UMTS and LTE network architecture ......................................................................... 3

Figure 2-2 Comparison of UMTS and LTE network rates (2013Q2) .................................................................... 4

Figure 2-3 Relationship between LTE DL spectrum efficiency and the number of online subscribers ................. 5

Figure 2-4 Traffic distribution in typical LTE networks (2014Q1) ....................................................................... 6

Figure 2-5 UMTS and LTE traffic distribution comparison in the same carrier's network (2014Q1) ................... 8

Figure 3-1 Experience testing result for the global commercial networks (2014Q1- Q2) ..................................... 9

Figure 3-2 Influencing factors for user experience and their relationships ......................................................... 10

Figure 3-3 Acceptance principles for carrier O's xMbps network (2014Q1 - Q2) .............................................. 11

Figure 3-4 Overview of xMbps Anytime Anywhere ........................................................................................... 12

Figure 4-1 Time distribution of video playing on Sohu Video APP (2014Q2) .................................................... 13

Figure 4-2 Percentage of playback with different quality videos on various networks (2014Q2) ...................... 14

Figure 4-3 Comparison of influence of different users (2014Q2) ....................................................................... 14

Figure 4-4 Number of Chinese characters per post for each type of users (2014Q2) .......................................... 15

Figure 4-5 Proportion of Microblogs containing images for each type of users (2014Q2) ................................. 16

Figure 4-6 Proportion of Microblogs forwarded by each type of users (2014Q2) .............................................. 17

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Mobile Broadband Review 2014H1 Tables

iv

Tables

Table 2-1 PS traffic models in typical networks globally (2014Q1) ...................................................................... 2

Table 2-2 LTE traffic models comparison in typical scenarios .............................................................................. 5

Table 2-3 Traffic distribution of videos in different resolutions in typical LTE networks ..................................... 7

Table 2-4 Monthly traffic tariff comparison in typical LTE networks ................................................................... 7

Table 3-1 Acceptance solutions of carrier O's xMbps network............................................................................ 11

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Mobile Broadband Review 2014H1 1 Introduction

1

1 Introduction

This report consists of three parts: Network Insights, Experience Insights, and User Behavior

Insights. The Network Insights describes the traffic models and traffic distribution of 4G and

3G networks, the differences between 4G and 3G networks, and the causes for the differences.

The Experience Insights explores the main factors that affect users' experience and the

progress of xMbps network deployment. The User Behavior Insights analyzes the video

consumption in different networks as well as the microblog users' behavior, characteristics,

and development trend.

The major findings are as follows:

The traffic model in the PS (Packet Switched) network from 2G/3G evolving to 4G: The

increased signaling load brought by paging and handover, the flattened network architecture

and changed talking modes are the root causes.

A reasonable number of online subscribers is helpful to enhancing the spectrum efficiency of

LTE networks.

The share of video services on the LTE network is about 10% higher than that of the UMTS

network as far as a certain mobile carrier is concerned. Even among the relatively developed

LTE networks, the share of HD videos varies a lot. The data traffic package quota and tariff,

as well as carriers' business orientation have significant impact on the consumption of HD

videos.

New progress was made in the acceptance test criteria of Experience Coverage (for example

xMbps anytime anywhere): the number of xMbps requests, the fill rate of xMbps and

transmitted carrier power (TCP) utility should be combined to decide the criteria for

optimization/expansion, and accept by comparison of the performance counters before or after

the optimization/expansion.

Testing results of the live networks show that the improvement in the quality of user

experience is disproportionate to that in the air interface data rate, and only a coordinated

optimization of the air interface and network architecture can offer the best user experience.

The statistics of Sohu Video show that the video consumption per user is more active in the

4G network than in 2G/3G network. The percentage of the 4G users choosing HD or higher

definition format videos is much higher than that of 2G/3G users (more than 20%).

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Mobile Broadband Review 2014H1 2 Network Insights

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2 Network Insights

2.1 PS Traffic Models in Different Networks

2.1.1 PS Signaling Increasing Dramatically in 4G Networks

Table 2-1 PS traffic models in typical networks globally (2014Q1)

PS Traffic Model

2014Q1

2G 3G 4G

Intra SGSN/MME RAU/TAU per attached subscriber @

Busy Hour

6.38 2.42 1.79

Inter SGSN/MME RAU/TAU per attached subscriber @

Busy Hour

0.71 1.06 0.12

Paging times per attached subscriber @ Busy Hour (PS)

(124 eNodeBs for each TA list)

1.84 2.44 11.64

Service Request times per attached subscriber @ Busy

Hour

NA 11.35 30.67

Intra MME /SGSN HO times per attached subscriber @

Busy Hour

0.02 0.10 8.02

Inter MME /SGSN HO times per attached subscriber @

Busy Hour

0.00 0.01 0.22

Average packet size @ Busy Hour (Bytes) 374.00 556.00 735.00

Average traffic per active bearer @ Busy Hour (Kbps) 0.81 25.00 110.00

Average online time per active bearer @ Busy Hour

(min)

NA 78.97 121.84

Data source: PS LMT, Huawei

As can be seen from Table 2-1, Paging is a signaling killer. Paging times (paging times for Broadcast Services excluded) for each attached 4G user is 11.6, 4.8 times larger than that of

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Mobile Broadband Review 2014H1 2 Network Insights

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3G (if 124 eNodeBs are deployed in a tracking area (TA) list, the paging request load for

MME brought by each user is 595 times bigger than that of SGSN in 3G networks). In

addition, handover times for each attached 4G user are larger than that of 3G. The changes in

network architecture (the entity that performs paging and handovers move from RNC in 3G

network to MME in the 4G network) and in voice calling modes account for these.

The number of 4G service requests is 2.7 larger than that of 3G. The paging channel (PCH)

deployment in 3G networks reduces the number of signaling messages, while the Dynamic

Discontinuous Reception (DRX) is not deployed in the 4G network so far.

The average packet size in 4G is 1.3 times that of 3G; the traffic volume per user during busy

hours is 4.4 times that of 3G.

The dynamic DRX feature is to reduce the signaling overhead and save UE power consumption when

UEs perform instant messaging and presence class services. It dynamically configures the UE Inactive

Timer and Uplink Synchronization Timer and uses the DRX algorithm in the out-of-synchronization

state to enable the UE online and save the UE power consumption.

2.1.2 Network Architecture Changes Contributing to Signaling Increases

Figure 2-1 Comparison of UMTS and LTE network architecture

Data source: PS LMT, Huawei

The impact of LTE network architecture being flat lies in two sides: on the one hand, the

end-to-end round trip time (E2E RTT) is reduced significantly (> 20 ms); on the other hand,

MME interacts with eNodeB directly, so that one MME will process the signaling messages

from multiple eNodeBs (for example, a paging occurs in different TAs, which involve

hundreds of eNodeBs).

At the same time, in the LTE network, all the handovers between eNodeBs should be

processed by MME, dramatically increasing the signaling messages; while in the UMTS

network, most of the handovers are processed in the same RNC, and only the signaling

messages in the scenario where the UEs migrate between different RNCs are processed by

SGSN.

Finally, with the expansion of the scale of LTE deployment, macro sites and micro sites will

coordinate more, sites will become denser; a TA list may include more sites, thus behaviors,

such as paging, handover, etc will create greater requirements on signaling capacity of MME.

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Mobile Broadband Review 2014H1 2 Network Insights

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2.2 RAN Traffic Models in Different RATs

2.2.1 Status for UMTS and LTE Network Rates

Figure 2-2 Comparison of UMTS and LTE network rates (2013Q2)

Data source: Huawei Wireless Network

The samples from the Korean carrier B are few, and they performed not too ideally in average. Therefore,

the average network rate of carrier B is low.

The data of LTE networks for West Europe and North America is absent.

As to 3G downlink rate, Norway is 5 times of the global average rate, performing far better

than China, Southeast Asia, and the Middle East. As to 3G uplink rate, most countries

fluctuate around the global average rate, among which Thailand tops by 2.65 Mbit/s.

As to 4G downlink rate, carrier A of United Arab Emirates performs better than any other

carrier. As to 4G uplink rate, carrier A of Malaysia performs better than any other carrier.

Carrier B has advantages over carriers A and C in respect of 3G network in China. However,

it performs worse than the latter two in respect of LTE network.

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Mobile Broadband Review 2014H1 2 Network Insights

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2.2.2 Reasonable Number of Subscribers Helping Increase LTE Spectrum Efficiency

Figure 2-3 Relationship between LTE DL spectrum efficiency and the number of online

subscribers

Source: Huawei Wireless Network

As shown in Figure 2-3, a reasonable number of online subscribers helps increase LTE

spectrum efficiency. On one hand, if the number of online subscribers is small, the number of

subscribers fluctuates more intensely and there is a large probability that the service

requirements are small, causing low spectrum efficiency. On the other hand, if the number of

online subscribers is big, the peak-to-average (PAR) ratio of online subscribers is smaller. In

this case, many resources will be consumed by signaling, and few of them are used for data

transmission; therefore, the DL spectrum efficiency is low.

Table 2-2 LTE traffic models comparison in typical scenarios

Scenario UE inactive Timer (s)

DL average user experience rate (Mbit/s)

UL average user experience rate (Mbit/s)

Peak-to-Average Ratio of online users

DL/UL traffic ratio

Scenario 1 20 8.26 0.69 2.58 10.04

Scenario 2 10 8.09 1.27 1.74 9.15

Scenario 3 5 10.73 1.54 3.57 8.74

Scenario 4 10 11.88 1.64 2.38 7.44

Scenario 5 20 13.33 2.30 1.95 7.5

Scenario 6 15 9.25 1.25 1.56 7.74

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Scenario UE inactive Timer (s)

DL average user experience rate (Mbit/s)

UL average user experience rate (Mbit/s)

Peak-to-Average Ratio of online users

DL/UL traffic ratio

Scenario 7 20 9.53 1.33 2.44 9.36

Source: Huawei Wireless Network

DL user experience rate = data volume that is successfully transmitted in a statistical period / time for

data transmission

As shown in Table 2-2 shows, the DL average user experience rate during busy hours in

advanced LTE networks is stable (standard deviation/mean value = 19%). However, the UL

average experience rate fluctuates a lot (standard deviation/mean value = 34%). The

fluctuation of PAR of online subscribers (1.5–3.6) and the UL/DL traffic ratio (7–10) in

different LTE networks is significant.

2.3 Traffic Distribution of Typical LTE Networks

2.3.1 Significant Difference in Traffic Distribution of LTE Networks

Figure 2-4 Traffic distribution in typical LTE networks (2014Q1)

Data source: Huawei Wireless Network

Generally, SNS consumes 8% of total daily traffic that users spend on smart phones, though this figure may vary in different carriers and regions.

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Table 2-3 Traffic distribution of videos in different resolutions in typical LTE networks

Carrier 240P Video Share

360P Video Share

480P Video Share

720P Video Share

1080P Video Share

A 37% 39% 18% 6% 0%

B 5% 26% 25% 32% 12%

C 21% 44% 16% 19% 0%

D 12% 35% 26% 27% 0%

Table 2-4 Monthly traffic tariff comparison in typical LTE networks

Carrier

Average Monthly Traffic Consumption Per User (Gigabytes)

Average Monthly Expenditure (Dollars)

Average Annual Income in 2012 (Dollars)

Percentage of Expenditure in Monthly Income

Description

A 2 41.958 38,250 1.32%

Value added service: a

free movie ticket every

Wednesday

B 3 58.206 22,670 3.08%

Top 1 U+HDTV app with

2 million users; 1.6

million U + Navi daily

users; contracted

packages for traffic tariff

C 2 47.472 36,560 1.56%

Value-added service: the

music app Newsic Daily

for free, and Now TV

England Premier League

Channel for free

Data Source: The data for users' expenditure was retrieved on 11th August, 2014 from the

corresponding carrier's website. The users' monthly traffic consumption was a mean value

from the industry consulting report, and the monthly expenditure (with local currency unit)

was from the most suitable data traffic package quota and tariff. The numbers in the preceding

table were calculated based on the daily currency by the currency calculator provided by

Hexun.com. The data for average annual income comes from the statistics published by World

Bank in 2012.

In advanced LTE networks, the percentage of HD and higher resolution videos varies a lot.

The data traffic package quota and tariff, as well as carriers' business orientation have

significant impact on the consumption of HD videos.

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Mobile Broadband Review 2014H1 2 Network Insights

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2.3.2 10% Video Consumption in an LTE Network Higher Than That of UMTS

Figure 2-5 UMTS and LTE traffic distribution comparison in the same carrier's network (2014Q1)

Data source: Huawei Wireless Network

In the relatively advanced LTE networks, the video consumption is about 10% higher than

that of UMTS, indicating that the higher network rate can stimulate the video consumption.

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Mobile Broadband Review 2014H1 3 Experience Insights

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3 Experience Insights

3.1 Status for Live Network Experience

3.1.1 Network Experience Improvements Lower Than Air Interface Capability Enhancement

Figure 3-1 Experience testing result for the global commercial networks (2014Q1- Q2)

Data source: Huawei mLAB

From 3G to LTE, the improvements in Page-loading Speed and user experience (i.e.

Page-loading Duration) is disproportionate to those of the air interface rate (i.e. DL Speed in

Speedtest). Therefore, to improve the web experience is still a long way off.

As the LTE air interface rate improves and the video content delivery networks are optimized,

the video Initial Buffering Downloading Speed is accelerated dramatically and the video

experience is improved a lot. However, due to the downloading speed limits from video

websites when playing the video and the less popularity of higher definition videos, the

Average Downloading Speed is only improved a little.

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Mobile Broadband Review 2014H1 3 Experience Insights

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3.2 Influencing Factors

The findings of mLAB's analysis on OTT transmission mechanism are as follows:

User experience = Size of the content / Actual speed

Actual speed = MIN (Air interface rate, TCP throughput)

3.2.1 Air Interface Bandwidth and Network Architecture Determining User Experience

Figure 3-2 Influencing factors for user experience and their relationships

Data source: Huawei mLAB

User experience depends not only on the data rate over the air interface (i.e. Experience

Coverage: xMbps Anytime Anywhere), but also on RTT determined by network architecture.

If the air interface resources are not limited, user experience is mainly affected by the RTT.

Therefore, attention should be paid to network architecture optimization to decrease RTT,

which includes the optimization of RTT in the wireless network as well as that caused by the

OTT services network architecture (like CDN deployment). If the bandwidth is not a

bottle-neck, the shorter the RTT, the faster the speed, and the greater the demand for the air

interface bandwidth.

A coordinated optimization of air interface and RTT will improve user experience at the

lowest costs.

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Mobile Broadband Review 2014H1 3 Experience Insights

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3.3 Progress in the Acceptance Test Criteria of Experience Coverage

3.3.1 Operative and Available Quota Commitment

Figure 3-3 Acceptance principles for carrier O's xMbps network (2014Q1 - Q2)

Data source: Huawei Radio Inventory Solutions

New progress was made in the acceptance test criteria of Experience Coverage (Brand:

xMbps anytime anywhere): the number of xMbps requests, the fill rate of xMbps and TCP

utility should be combined to decide the criteria for optimization/expansion, and accept by

comparison of the performance counters before or after the optimization/expansion.

3.3.2 Practice

Table 3-1 Acceptance solutions of carrier O's xMbps network

Scenario Definition Solution Proposal

Scenario 1 xMbps requirements > 300, xMbps

fill rate < 30%, TCP >60%

Capacity Expansion based on

experience (Sector splitting/Small cell)

Scenario 2 xMbps fill rate < 30%, TCP <

50%, xMbps requirement > 100

Network Optimization (RAN

Feature/ACP - Auto Cell Planning

Solution)

Scenario 3 HSDPA user > 20, TCP > 70% Capacity Expansion based on

traditional resource(s) utility

Scenario 4 xMbps fill rate < 30%, TCP >

50%, xMbps requirement < 100

Optimization or analyses of the

(x/2)Mbps fill rate

Other excluding the above scenarios None

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Mobile Broadband Review 2014H1 3 Experience Insights

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Data source: Huawei Radio Inventory Solutions

Figure 3-3 corresponds with scenario 1 listed in Table 3-1.

Figure 3-4 shows vividly the idea for Experience Coverage (Brand: xMbps anytime

anywhere).

Figure 3-4 Overview of xMbps Anytime Anywhere

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Mobile Broadband Review 2014H1 4 User Behavior Insights

13

4 User Behavior Insights

4.1 Time Distribution of Video Playing

4.1.1 More Smooth Time Distribution of Video Playing in 4G Network Than in 3G and Wi-Fi

Figure 4-1 Time distribution of video playing on Sohu Video APP (2014Q2)

Data source: Sohu Video APP

The peak hours for video playing range from 12:00 to 13:00 and from 20:00 to 24:00.

Compared with the 2G / 3G / Wi-Fi curves in the chart, the time distribution curve of video

playing in the 4G network is much smoother.

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4.2 User Behaviors in Video Playing and Microblog

4.2.1 More Video Consumptions in 4G than in 3G

Figure 4-2 Percentage of playback with different quality videos on various networks (2014Q2)

Data source: Sohu Video APP

The percentage of the 4G users choosing HD or higher definition format videos is much

higher than that of 2G/3G users (more than 20%).

4.2.2 VIP's Influence Higher than Other Users in Microblog

Figure 4-3 Comparison of influence of different users (2014Q2)

Data source: Huawei mLAB

Among the four types of microblog users, individual VIP users take the lowest proportion.

However, they publish more microblogs, have more fans, and are followed more than other

types of users, and therefore have greater influence.

695

4038

31808

7521

0

5000

10000

15000

20000

25000

30000

35000

0

500

1000

1500

2000

2500

3000

3500

4000

Individual Users Expert Users Individual VIP Users

Institutional VIP

Average Number Of Microblogs posted

Average Number Of Followed Users

Average Number Of Fans

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Mobile Broadband Review 2014H1 4 User Behavior Insights

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4.3 Microblog Users' Behavior Trend

4.3.1 Number of Chinese Characters

Figure 4-4 Number of Chinese characters per post for each type of users (2014Q2)

Data source: Huawei mLAB

The average number of Chinese characters per microblog is increasing. The average number

of Chinese characters per microblog from individual VIP users is greater than that from

individual users.

The average number of Chinese characters per microblog now is 71 based on the user

proportions and through weight calculation.

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Mobile Broadband Review 2014H1 4 User Behavior Insights

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4.3.2 Proportion of Microblogs Containing Images

Figure 4-5 Proportion of Microblogs containing images for each type of users (2014Q2)

Data source: Huawei mLAB

The proportion of microblogs containing images has been slightly increasing, showing a

roughly stable trend on the whole. Individual VIP users publish more microblogs containing

images than other types of individual users.

The proportion of microblogs containing images now is 72.45% based on the user proportions

and through weight calculation.

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Mobile Broadband Review 2014H1 4 User Behavior Insights

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4.3.3 Individual Users Forwarding More Than VIP Users

Figure 4-6 Proportion of Microblogs forwarded by each type of users (2014Q2)

Data source: Huawei mLAB

The proportion of common users who forward other users' microblogs is slightly higher than

that of individual VIP users who forward other users' microblogs. Nearly half of the current

microblogs are forwarded ones on the whole.

The proportion of forwarded microblogs now is 56.78% based on the user proportions and

through weight calculation.

The data for analyzing the Microblog users’ behavior was retrieved by MBB Robot, with 15,000

samples so far.

Rules for defining the types of users:

Individual users: most of them are common people, including the users who are not authenticated as

VIP and have attracted a large number of fans, accounting for 91.43% of the total users.

Active users: the users who are very active among the individual users. They have tags for being

active and a larger number of microblogs and fans than the common users, accounting for 7.18% of

the total users.

Individual VIP: identified Microblog users who are often famous in their fields and have a lot fans,

accounting for 0.71% of the total users.

Institutional VIP: the users include government department, companies, and websites, accounting for

0.68% of the total users.

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Mobile Broadband Review 2014H1 5 Appendix

18

5 Appendix

5.1 Overview

This report was written by Huawei Wireless Traffic Model Analysis Team. Based on the data

from global typical commercial mobile networks, the results of live mobile networks' speed

tests, web browsing experience tests, and streaming service experience tests, the statistics of

OTT services characterics, and the statistics of Sohu Video APP. This report tries to

objectively reflect the status and trend for mobile broadband, terminals, services, and user

experience/behavior. However, this report does not present the accuracy and integrity of the

information.

In respect of privacy, all the names of carriers are anonymous in this report. Limited by the

number of samples and the rapid development of mobile broadband, Huawei retains the rights

to modify the later versions of this report and will not be responsible for the results caused by

these modifications.

5.2 Data Sources

The original data from the global commercial mobile networks that cooperate with Huawei;

The test results from the tests by MBB Explorer APP in the typical commercial networks;

The statistical results by using MBB Robot to collect the data of OTT services characterics;

Statistical results by analyzing the users and the video playing in the Sohu Video APP.

5.3 Contact Information

Author: Peng Zhenyu/00068822

Email: [email protected]

mLAB: [email protected]

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Mobile Broadband Review 2014H1 Terms and Definitions

19

Terms and Definitions

Terms Definitions

3G The Third Generation of mobile telecommunications technology, which

supports high speed data transmission. There are three standards branded with

3G: CDMA2000, WCDMA, and TD-SCDMA.

4G The Fourth Generation of mobile telecommunications system. There are two

standards for LTE networks: LTE TDD and LTE FDD.

eNodeB Evolved Node B is a type of base station specifically for LTE networks.

Compared with the NodeB in 3G network, eNodeB integrates the functions of

RNC, allowing lower response times.

LTE The Long Term Evolution is the fourth generation of mobile

telecommunications standard, released by 3GPP. It uses OFDM and MIMO to

greatly increase the data transmission capacity and speed of radio access

network.

MME The mobility management entity is an EPC entity that performs the logic

functions related with signaling.

RAU A routing area (RA) is applied in the packet switched (PS) network of UMTS.

The routing area update is an important part of the mobility management in

the GPRS network, to help identify the locations of UE and enable UE paging.

RTT The round trip time is the elapsed time for the data to be sent and received

between the transmitter and the receiver.

SGSN The serving GPRS support node is a functional entity in the PS network of the

GPRS/WCDMA, providing functions such as packet data routing and

forwarding, mobility and session management, logical link management,

authentication and encryption, and charging data record (CDR) generation and

output.

Spectrum

Efficiency

The spectrum efficiency is a measure of the performance of encoding methods

that code information as variations in an analog signal. Spectrum efficiency =

Traffic rate/Bandwidth. The unit for spectrum efficiency is bits/Hz.

TAU Tracking area (TA) is applied in the EPS. The UEs both in idle and connected

modes are registered in a TA and managed by EPC. If the TA of the UEs is

changed, the registration information will be changed accordingly. A tracking

area update (TAU) informs EPC whether the UEs are available. If the

handover is performed or the tracking area identity (TAI) is not included in the

TA list, TAU must be performed.

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Mobile Broadband Review 2014H1 Terms and Definitions

20

Terms Definitions

TCP The transmitted carrier power is used to monitor DL transmission. It is limited

by the maximum transmit power of the base station's power amplifier.

UMTS The Universal Mobile Telecommunications System is the third generation

mobile telecommunications standard released by 3GPP.

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Mobile Broadband Review 2014H1 Reference Documents

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Reference Documents

1. Liu Pingping, Research on the Technologies to Reduce the Peak-to-Average Ratio in

OFDM, Northwest, 2010.6

2. "eRAN8.0 Feature Documentation – DRX and Signaling Control Feature Parameter

Description", Huawei Technologies, Co., Ltd., 2014.5

3. Differences between 3G and 4G Network Architecture, accessed from

http://network.chinabyte.com/414/12557414.shtml, 2013.3

4. Zhu Min, "South Korea: Advantages for LTE Development", Huaxin Consulting Co.,

Ltd., accessed from http://www.srrc.org.cn/NewsShow9177.aspx, 2014.2

5. Guo Luqing, "A Comparison of 4G Charges between China and other Countries",

accessed from http://biz.21cbh.com/2014/kuaibao_319/1102834.html, 2014.3

6. Guo Xiaofeng, "South Korean 4G Network Experience: 300RMB for 6 gigabytes traffic

volume"

7. Don Mac Vittie, "Myths of Bandwidth Optimization", F5 Networks, Inc., 2012

8. Li Wenbin and Gen Bo, "Theoretical Analysis on TCP Throughput 0.2", 2009.6

9. J. Padhye, V. Firoiu, D. Towsley and J. Kurose, "Modeling TCP Reno performance: A

Simple Model and Its Empirical Validation", IEEE/ACM Trans. on Networking, vol. 8,

no. 2, pp. 133-145, April 2000.

10. Yang Chengming, "Empirical Analysis of Microblog Users' Behavioral Characteristics",

Business School of East China Normal University, 2011.6