What is going on in Mobile Broadband Networks?

Nokia Networks What is going on in Mobile Broadband Networks? Smartphone Traffic Analysis and Solutions White Paper Nokia Networks white paper What is going on in Mobile Broadband Networks?

Contents Executive Summary 3 Data volumes and asymmetry 4 Traffic related signaling 6 Mobility related signaling 8 User connectivity 9 VoLTE impact 11 RF quality 12 Conclusion 14 Further reading 14 Abbreviations 15 Page 2

Executive Summary Smartphones are the dominant source of data traffic in mobile broadband networks. This white paper analyzes typical traffic patterns, shows the impact of that traffic on mobile networks and illustrates solutions for enhancing network performance and efficiency. The lessons learned from measurements on a large number of leading mobile broadband networks can be summarized as follows: Average downlink data volumes range from 1-3 GB per subscriber per month in smartphone dominated networks and more than 10 GB in USB dongle-dominated networks. The networks usually have limited downlink capacity. Smart Scheduler is one solution for improving efficiency. Data asymmetry between downlink and uplink on average can be more than 10: 1 in smartphone networks due to downlink streaming traffic. The traffic from USB dongles is more symmetric with asymmetry typically being 4.5:1 Mass events tend to have limited uplink because the traffic is more symmetric when many people want to share pictures and videos from the event. Uplink interference management solutions, like Coordinated Multipoint (CoMP) in LTE and Mass Event Handler (MEH) in HSPA, are important for these mass events. Smartphones create frequent transmission of small packets due to background applications. The frequency of packet call setups or channel allocations is typically 400 per day per subscriber. The average data volume per allocation is small, just 100-200 kb. The heavy signaling can also create uplink interference due to control channels and may require advanced interference management. The amount of mobility (handover) signaling is substantially lower than call setup (erab setup) signaling, even in an LTE network with flat architecture. 10-15% of LTE subscribers are simultaneously RRC connected during the busy hour, requiring high base station capacity for these simultaneous users. The number of RRC connected devices is much higher in HSPA due to Cell_PCH state, which requires high RNC capacity. On the other hand, the number of Cell_DCH users is lower than the share of RRC connected users in LTE. Radio frequency (RF) quality can be estimated from Channel Quality Indicator (CQI). The values indicate more than 2 bps/hz efficiency in LTE and more than 1 bps/hz efficiency in HSPA, even in loaded cells in the downlink. Page 3

Data volume RF quality from CQI Data asymmetry Signaling frequency User connectivity Downlink efficiency solutions like Smart Scheduler Spectral efficiency estimates Downlink limited except mass events that are uplink limited High BTS signaling capacity High BTS user connectivity Figure 1. Summary of smartphone related traffic challenges and proposed solutions Data volumes and asymmetry Data volumes are increasing rapidly in mobile broadband networks due to the growing number of users and because each user consumes more data. This growth in data consumption is driven by new applications such as high definition streaming, enabled by attractive smartphones. The data volumes created by these advanced smartphones are typically 1-3 GB/month in advanced networks, for example in Japan, Korea and North America. USB modems with laptops can create a lot of traffic, even greater than 10 GB/month. The typical data volumes are illustrated in Figure 2. USB dongle dominated networks are typically found in the early phase of LTE deployment when smartphone penetration is low, or in those markets where fixed broadband availability is low and USB modems are used for home connectivity. Data volumes also naturally depend on the pricing and data packages. GB/sub/month 12 10 8 6 4 2 0 Smartphones network Both types of traffic USB dongle network Figure 2. Typical data volumes per subscriber per month in advanced networks Page 4

Most of today s mobile broadband traffic is in the downlink. The dominant traffic type is streaming, which is practically downlink only traffic. In fact, the volume of downlink data can be up to ten times greater than the uplink data volume. Therefore, solutions for improving downlink efficiency are required, for example, Smart Scheduler for interference management, Multi-band load balancing, Carrier Aggregation and Quality of Service differentiation. The typical asymmetry is shown in Figure 3. The asymmetry is different in mass events, where the uplink traffic is relatively higher from a smartphone network, see Figure 4. The reason is that many people want to share pictures and videos from these mass events. Therefore, mass events have turned out to be uplink limited and solutions for managing uplink interference are required, for example, uplink multi-cell reception, also known as Coordinated Multipoint (CoMP) in LTE, Continuous Packet Connectivity (CPC) and Mass Event Handler (MEH) in HSPA. For more details, see the Nokia white paper High Capacity Mobile Broadband for Mass Events. Traffic asymmetry can also be taken into account in the time domain capacity allocation between uplink and downlink in TD-LTE by selecting suitable configurations to match the required capacity split. Configuration 1 provides a fairly symmetric split between downlink and uplink, while configuration 2 is better suited for downlink dominated traffic, using nearly 80% of the time for the downlink direction. The same split needs to be used in the whole coverage area. Downlink vs uplink asymmetry 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 Smartphone network Both types of traffic USB dongle network Figure 3. Typical asymmetry between downlink and uplink Page 5

12 10 8 6 4 2 0 Downlink vs uplink asymmetry Average in the network Peak hour in the mass event Figure 4. Asymmetry between downlink and uplink in the smartphone network Traffic related signaling Smartphone applications create frequent transmission of small packets due to the background activity of the operating system related network services and the applications. Even if a person does not actively use the phone, packet transmissions still happen in the background. Many mobile broadband networks experience an average of 30 packet calls or channel allocations per subscriber per busy hour with the average data volume per allocation as low as 100 kbytes. That corresponds to one allocation per subscriber every two minutes during the busy hour. Compare this to the typical voice related activity of just one voice call during the busy hour, which shows that packet data related signaling is far higher than voice related signaling. During mass events, signaling volumes can be very high, going beyond 250 signaling actions per base station per second. Therefore, radio network products need to support a high signaling capability, in addition to high throughput and high user capacity. Figure 5 shows an example of LTE packet call setup volumes per subscriber per busy hour from the network point of view. Nokia Smart Labs has analyzed smartphone behavior with one example being illustrated in Figure 6. An Android based smartphone creates two packet calls per hour simply due to the operating system, without any applications. When some of the popular social media applications (App A, App B and App C) are running in the background, the signaling activity increases to 17 calls per hour. Page 6

The analysis of HSPA networks indicates a similar number of channel allocations, which can result in substantial signaling in the RNC and also increase the uplink interference due to the increased traffic on the control channels. Therefore, there is a clear need for enhanced features including Nokia Mass Event Handler (MEH), Discontinuous Transmission (DTX), High Speed Random Access Channel (HS-RACH), Parallel Interference Cancellation (PIC) and four antenna reception (4RX). The frequent signaling in LTE and in HSPA also creates challenges for terminal power consumption. The same features that minimize signaling can also help to minimize power consumption and make the battery last longer. 45 40 35 30 25 20 15 10 5 0 erabs/sub/hour Smartphone network Both types of traffic USB dongle network Figure 5. Number of LTE packet calls (erabs) per subscriber per busy hour network analysis Page 7

18 16 14 12 10 8 6 4 2 0 Average number of background data transactions per hour OS baseline App A activated App B activated App C activated Figure 6. Number of LTE packet calls (erabs) per hour Android based smartphone analysis Mobility related signaling The amount of mobility signaling was a concern in the early days of LTE deployment because its flat architecture also makes mobility visible to the core network. The practical network statistics show that the number of call setups is typically 10 times higher than handovers. There are two main reasons for this behavior: the number of packet calls is very high and the packet calls are very short. Handover is unlikely to occur during a short packet call. The relative number of packet calls compared to handovers in three example LTE networks is shown in Figure 7. We can conclude that mobility signaling is no problem in current networks, compared to the call setup. If the cell size becomes considerably smaller in future heterogeneous networks, mobility signaling may increase. Page 8

20 18 16 14 12 10 8 6 4 2 0 Number of erabs vs Handovers 1 2 3 Figure 7. Relative number of packet calls compared to number of handovers User connectivity The number of RRC connected users is a relevant factor in radio network dimensioning. The share of LTE subscribers that are RRC connected can provide useful information to estimate the radio network requirements based on the number of LTE subscribers. An example relationship between RRC connected users and all LTE subscribers is shown in Figure 8. The share goes up to 10% in this example network during the busy hour in the evening. The average is 7-8% over a 24 hour period. The share of connected users is heavily dependent on the RRC inactivity timer, which was 10 seconds in this particular network. The share can be simply explained by the fact that smartphones create a packet call every two minutes (every 120 seconds) during the busy hour. If the actual packet transmission is short, the typical length of the RRC connection is just slightly more than the inactivity timer, so 10-15 seconds, which explains why 10% of subscribers are RRC connected. In general, a short inactivity timer, around 5 to 10 seconds, is preferred to minimize smartphone power consumption as well as the number of connected users. The share of RRC connected users is clearly higher in an HSPA network since inactive users can stay in Cell_PCH for a long time, around 30 minutes. In advanced HSPA networks, Cell_PCH users consume no base station resources and a very large number of Cell_PCH users can be supported by the RNC. Page 9

The Cell_PCH state in HSPA is actually similar to the idle state in LTE. This means that the number of packet calls (RABs/eRABs) in HSPA is typically low since setups only take place between the terminal and RNC, while in LTE, setups take place between the terminal and packet core network. Cell_DCH users in HSPA are similar to connected users in LTE. The number of Cell_DCH users in HSPA is normally less than 10% because the inactivity timer on Cell_DCH is usually shorter than the LTE RRC inactivity timer. The RRC states in HSPA and in LTE networks are illustrated in Figure 9. Optimizing the use of RRC states is critical to optimize smartphone performance. 12% Share of RRC Connected Users 10% 8% 6% 4% 2% 0% Figure 8. Share of RRC connected users out of all subscribers Page 10

Inactivity HSPA Idle LTE Idle 30 min 10 s 5 s RAB setup Cell_PCH (Connected) Connected Cell_DCH (Connected) Data transmission erab setup Figure 9. Illustration of RRC states in HSPA and in LTE network VoLTE impact Voice over LTE (VoLTE) is running commercially in a few networks today and more launches are expected in 2014. How should operators dimension their networks in preparation for a VoLTE launch? The number of packet calls and simultaneous packet users is so high that VoLTE will have only a minor impact on the throughput, the number of connected users or signaling. This is true even if VoLTE requires an additional dedicated bearer with QCI1 (Quality of Service Class Identifier) to carry high quality voice and even if VoLTE calls are typically much longer than packet calls. The share of VoLTE calls (erabs) of all calls is currently below 1%. Although this share will increase when VoLTE penetration grows, it is still expected to be just a few percentage points. We have seen that voice calls during mass events tend to be shorter than on average in the network. This can be explained by the fact that people are unlikely to make very long calls during those events. Also, packet calls tend to be slightly shorter during mass events than on average in the network. The packet call length is still mostly dominated by the inactivity timer and not the actual data transmission time. Page 11

RF quality The network RF quality provides information about the achievable spectral efficiency, which is again important for dimensioning the maximum network capacity. All LTE and HSPA terminals provide Channel Quality Indicator (CQI) measurements to the base station. CQI indicates the maximum possible data rate that the terminal is able to receive in the current signal-to-noise ratio conditions. By taking the average values of CQI reports, the network can estimate the average achievable cell capacity by mapping the CQI reports to the corresponding throughput with the link adaptation tables. The LTE mapping table is shown in Table 1. The typical average CQI in LTE networks is between 10 and 11, corresponding to the efficiency of 2.5 bps/hz/cell, and even more when dual stream MIMO is considered. The CQI value depends on the network loading and interference levels. An example correlation between loading and CQI is shown in Figure 10. In this particular case with low traffic, CQI is 11 and with high traffic, CQI is 9.5, corresponding to an efficiency of more than 2 bps/hz. The high load here corresponds to the total data volume of 1000 Megabytes in 15 minutes, corresponding to the average throughput of 9 Mbps. Similar analysis in HSPA networks typically also shows an efficiency of more than 1 bps/hz in the loaded cases. Table 1. 3GPP LTE CQI mapping table with 10% error rate CQI index Modulation Efficiency 0 out of range 1 QPSK 0.14 2 QPSK 0.21 3 QPSK 0.34 4 QPSK 0.54 5 QPSK 0.79 6 QPSK 1.06 7 16QAM 1.33 8 16QAM 1.72 9 16QAM 2.17 10 64QAM 2.46 11 64QAM 2.99 12 64QAM 3.51 13 64QAM 4.07 14 64QAM 4.60 Page 12

12.0 12.0 11.5 11.5 11.0 11.0 10.5 10.5 10.0 10.0 9.5 9.0 8.5 8.0 9.5 9.0 8.5 8.0 Average CQI Average CQI Linear (Average CQI) Linear (Average CQI) 0 200 400 600 800 1,000 0 200 400 600 800 1,000 Average data volume per enodeb [MB/15 min] Average data volume per enodeb [MB/15 min] Figure 10. Correlation between cell loading (data volume) and Channel Quality Indicator (CQI) in an LTE network Page 13

Conclusion With more than 200 HSPA and more than 140 LTE operator customers, Nokia Networks is in the best position to understand smartphone traffic in mobile broadband networks. Although data volumes are important to consider, other factors such as signaling, mobility, packet sizes, data asymmetry and mass event capacity require advanced solutions to enhance network capacity and end-user performance. Nokia Smart Labs works closely with smartphone vendors and application developers to better understand the network requirements. This paper presents the typical traffic patterns and the main network solutions. The paper is based on measurements from a large number of leading mobile broadband networks and from measurements in Nokia Smart Labs. Further reading White papers High Capacity Mobile Broadband for Mass Events Nokia Smart Scheduler HSPA+ Boosters for Multifold Performance Web pages Nokia Smart Labs Page 14

Abbreviations Cell_DCH Cell_PCH CoMP CPC CQI DTX erab HSPA HS-RACH LTE MEH PIC RAB RRC VoLTE Cell Dedicated Channel Cell Paging Channel Coordinated Multipoint Continuous Packet Connectivity Channel Quality Indicator Discontinuous Transmission E-UTRAN Radio Access Bearer High Speed Packet Access High Speed Random Access Channel Long Term Evolution Mass Event Handler Parallel Interference Cancellation Radio Access Bearer Radio Resource Controller Voice over LTE Page 15

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