Mobile Broadband Review 2014H1

Mobile Broadband Review 2014H1

Contents 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 i

Contents 5.2 Data Sources... 18 5.3 Contact Information... 18 ii

Figures 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 iii

Tables 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 iv

1 Introduction 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%). 1

2 Network Insights 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 Intra SGSN/MME RAU/TAU per attached subscriber @ Busy Hour Inter SGSN/MME RAU/TAU per attached subscriber @ Busy Hour Paging times per attached subscriber @ Busy Hour (PS) (124 enodebs for each TA list) Service Request times per attached subscriber @ Busy Hour Intra MME /SGSN HO times per attached subscriber @ Busy Hour Inter MME /SGSN HO times per attached subscriber @ Busy Hour 2014Q1 2G 3G 4G 6.38 2.42 1.79 0.71 1.06 0.12 1.84 2.44 11.64 NA 11.35 30.67 0.02 0.10 8.02 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 2

2 Network Insights 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. 3

2 Network Insights 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. 4

2 Network Insights 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-Averag e 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 5

2 Network Insights Scenario UE inactive Timer (s) DL average user experience rate (Mbit/s) UL average user experience rate (Mbit/s) Peak-to-Averag e 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. 6

2 Network Insights 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% B 3 58.206 22,670 3.08% C 2 47.472 36,560 1.56% Value added service: a free movie ticket every Wednesday Top 1 U+HDTV app with 2 million users; 1.6 million U + Navi daily users; contracted packages for traffic tariff 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 11 th 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. 7

2 Network Insights 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. 8

3 Experience Insights 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. 9

3 Experience Insights 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. 10

3 Experience Insights 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) 3.3.2 Practice 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. 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 11

3 Experience Insights 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 12

4 User Behavior Insights 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. 13

4 User Behavior Insights 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) 4000 3500 3000 2500 2000 1500 1000 500 0 695 Average Number Of Microblogs posted 31808 Average Number Of Followed Users Average Number Of Fans 4038 Individual Users Expert Users Individual VIP Users 7521 Institutional VIP 35000 30000 25000 20000 15000 10000 5000 0 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. 14

4 User Behavior Insights 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. 15

4 User Behavior Insights 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. 16

4 User Behavior Insights 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. 17

5 Appendix 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: pengzhenyu@huawei.com mlab: MBBlab@huawei.com 18

Terms and Definitions Terms and Definitions Terms 3G 4G enodeb LTE MME RAU RTT SGSN Spectrum Efficiency TAU Definitions 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. The Fourth Generation of mobile telecommunications system. There are two standards for LTE networks: LTE TDD and LTE FDD. 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. 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. The mobility management entity is an EPC entity that performs the logic functions related with signaling. 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. The round trip time is the elapsed time for the data to be sent and received between the transmitter and the receiver. 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. 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. 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. 19

Terms and Definitions Terms TCP UMTS Definitions 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. The Universal Mobile Telecommunications System is the third generation mobile telecommunications standard released by 3GPP. 20

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