At a time where network congestion leads to dropped calls and interruptions in Internet access, this study presents the mobile traffic projections and capacity evaluation of IDATE experts’ based on several case studies. The aim of our in-depth analysis is, to determine the realities and to provide different scenarios for network saturation up to 2015.

Traffic Model & Scenarios
Conclusions of the study are based on the results of an in-house model

In this study, mobile broadband is considered as a service that provides user access to mobile services, via any mobile device and over any mobile technology at speeds higher than 2.75G.

Segmentation by network speeds
• GPRS roughly equates to 114 Kbps
• EDGE to 474 Kbps
• UMTS to 2 Mbps (stationary)
• HSPA to 7.2 Mbps
• HSPA+ to 28 Mbps
• LTE to 100 Mbps

Modelling methodology
All results below will be based on a simulation of an imaginary mobile operator already running a UMTS/HSPA network and later a LTE network in an imaginary territory.

1 step
We evaluated the capacity of a mobile network based on the following characteristics:
• 50 million population in a developed country
• Area: 400,000 – 500,000 km²
• Mobile operator has 33% market share.
• Spectrum availability: frequency band, number of channels, availability date… (see table below for detailed
values)
• Spectrum efficiency, maximum data rate for each technology
• Evaluation of the capacity of a cell
• Total monthly and yearly capacity of the network

2 step
We estimated the mobile traffic (voice and data), first on a worldwide basis and then for a network operator in a medium-sized country. This evaluation takes into account the following assumptions:
• Number of mobile subscriber and projections for 2010-2015
• % of broadband subscribers
• Voice and data traffic per user
• Total voice and data traffic


Report Highlights

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1. Executive Summary
• Mobile data traffic is pushed by several technological and marketing enablers
• The long awaited take-off of mobile data traffic is leading to congestion
• Congestion is a threat to profitability
• There are 4 main bottlenecks
• Many options are available

2. Methodology

3. The Sharp Growth in Mobile Data Traffic
3.1. Key drivers
3.1.1. Improved network infrastructures
3.1.2. Improved mobile data-hungry devices
3.1.3. Strong mobile broadband subscribers' growth
3.1.4. Strong data demand
3.1.5. Mobile data plans
3.2. Mobile data traffic is to turn into congestion
3.2.1. Data-consumption driven by smartphones and portables
3.2.2. Data-consumption driven by video
3.3. Consequences of network congestion for MNOs
• Higher churn rates
• Higher direct loss of revenues
• Higher reacquisition costs and the need to increase CRM efforts
• Mobile data traffic is increasing faster than related revenues
• Network costs become a threat to MNOs' profitability

4. Where Does Saturation Occur in a Mobile Network?
4.1. Various types of network saturation: identified bottlenecks
4.2. Congestion at the radio interface
4.3. Congestion in the backhauling network
4.4. Congestion in the RNC
4.5. Congestion in the core network

5. What Options Available to MNOs?
5.1. Adapt the backhauling network
5.1.1. Mobile operators invest in backhauling upgrades
5.1.2. Backhauling technical solutions
5.2. Improve the radio access network
5.2.1. Add new cell sites
5.2.2. Add small cells and picocells
5.2.3. Add urban femtocells
5.2.4. Cell splitting and sectorisation
5.2.5. Antennas enhancements
5.2.6. Switch to HSPA+
5.2.7. Accelerate LTE deployment
5.2.8. Buy additional spectrum / use refarming
5.2.9. Use emerging technologies
5.3. Offload mobile data traffic
5.3.1. Wi-Fi offload
5.3.2. Use femtocell capabilities for offloading
5.3.3. Consider the use of broadcast/multicast networks and capabilities
5.4. Improve the data traffic management
5.4.1. Adapt the core network
5.4.2. Manage the type of data traffic in the network
5.4.3. Optimise the applications running on smartphones

6. Traffic Model and Scenarios
6.1. Presentation
6.2. Main hypothesis
6.2.1. Frequency bands, data rates & capacity
6.2.2. Worldwide mobile traffic
6.3. Conservative scenario
6.4. Strong growth scenario
6.5. Large city
6.6. Conclusions on modeling

7. Conclusions for MNOs
7.1. Conclusions on traffic and capacity model
• Wireless mobile data traffic is expected to increase sharply over the 2010-2015 period
• No theoretical congestion problems
• Dense urban areas are the biggest area of concern
7.2. Conclusions from options for MNOs
• Carrier strategies will depend on their particular situation on the market place
• Rising data demand can be met without exponential increase in CAPEX
• What alternatives are favoured?
7.3. Conclusions MNOs global strategies
7.3.1. Speed up 4G deployment
7.3.2. Improve Wi-Fi coverage
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Table 1: 3G and 3G+ coverage in major countries 19
Table 2: Mobile broadband overview in selected European countries (stated data are for June 2009) 24
Table 3: Unlimited data plans in EU5 and the USA 30
Table 4: Customer perceptions 42
Table 5: Backhauling needs 51
Table 6: Backhaul technologies alternatives 51
Table 7: Femtocell basics 55
Table 8: Femtocell characteristics 56
Table 9: Main frequency bands for UMTS/HSPA deployment – TDD mode 59
Table 10: FDD spectrum assets for selected operators (end-2007) 61
Table 11: FDD mobile spectrum availability in Western Europe 62
Table 12: Frequency bands for 4G technologies 63
Table 13: New 4G spectrum 63
Table 14: Date of availability of LTE spectrum 64
Table 15: Terrestrial mobile broadcasting solutions 72
Table 16: Traffic and signalling load 76
Table 17: Network capacity units 85
Table 18: Data rates and capacity for mobile radio technologies 85
Table 19: Worldwide mobile traffic 86
Table 20: Conservative scenario – traffic forecasts 86
Table 21: Conservative scenario – total capacity of the network 87
Table 22: Strong growth scenario – traffic forecasts 88
Table 23: Strong growth scenario - total capacity of the network 88
Table 24: 1 km² in a big city – traffic forecasts – strong data traffic 89
Table 25: 1 km² in a big city – traffic forecasts – very heavy data traffic 89
Table 26: 1 km² in a big city – total capacity 90
Table 27: Comparison of traffic and capacity for the various scenarios 91
Table 28: Benefits and drawbacks by considered options 95
Table 29: LTE adoption: advantages and drawbacks for various operator types 97
Table 30: Value perceived by different type of players 99
Figure 1: Bottlenecks in the mobile network 10
Figure 2: Traffic and capacity for a MNO with 17 M subscribers 13
Figure 3 : Mobile internet bandwidth consumption 20
Figure 4: 3G subscribers uptake in major mobile markets 23
Figure 5: Penetration of broadband in the Orange mobile customer base 24
Figure 6: Mobile/fixed broadband subscribers in Sweden 25
Figure 7: Mobile/fixed broadband technologies in Sweden 25
Figure 8: Mobile vs total broadband ratio in selected European countries 26
Figure 9: Mobile/fixed broadband subscribers in Austria 27
Figure 10: Mobile/fixed broadband technologies in Austria 27
Figure 11: Mobile data growth in advanced markets (%) 28
Figure 12: Data growth vs mobile voice growth in advanced and emerging markets (%) 28
Figure 13: Share of mobile data and mobile voice revenues (million EUR, %) in advanced markets 29
Figure 14: Mobile data traffic in Sweden 31
Figure 15: Mobile data traffic per user and month in Sweden 31
Figure 16: Mobile broadband traffic in Denmark and Norway 32
Figure 17: Mobile data traffic in Orange's main markets 33
Figure 18: Mobile data traffic in Verizon Wireless network 33
Figure 19: Europe traffic growth at Vodafone Group 33
Figure 20: The Cisco VNI Global Mobile data traffic forecast (2008-2013) 34
Figure 21: Mobile data traffic growth by device type 35
Figure 22: Mobile data traffic share by device type in 2008 and 2013 36
Figure 23: Mobile broadband traffic breakdown by telephony subscriptions type in Norway 37
Figure 24: Data AUPU depending on device 38
Figure 25: High-end phones traffic growth 38
Figure 26: Portables traffic growth 39
Figure 27: Mobile data traffic share by application in 2008 and 2013 40
Figure 28: Mobile data traffic growth by application 40
Figure 29: Data growth versus revenue growth 43
Figure 30: Selection of yearly CAPEX levels 44
Figure 31: Mobile CAPEX evolution over 4 quarters 45
Figure 32: Mobile data traffic explosion 46
Figure 33: Various types of network saturation 47
Figure 34: Congestion in the RNC 48
Figure 35: Mobile data offload solution 52
Figure 36: Femtocell Architecture 55
Figure 37: Beamforming principle 57
Figure 38: Relation between 3G range and peak data rates (downlink) 58
Figure 39: Theoretical and typical throughput speeds for various mobile data technologies 59
Figure 40: GSM 900 spectrum in selected European countries 61
Figure 41: Cumulative spectrum in Western Europe 62
Figure 42: Availability of new spectrum in Western Europe 64
Figure 43: Example of Wi-Fi off-load solution 66
Figure 44: Wi-Fi offloading solution from Kineto 68
Figure 45: Broadband use places in the UK 69
Figure 46: Offloading solutions for TV and video traffic 71
Figure 47: DPI network bandwidth management system 75
Figure 48: UMTS network and potential sharing 79
Figure 49: Influence of the Vodafone, China Mobile and Verizon Wireless partnership over LTE ecosystem 82
Figure 50: Partnerships between vendors targeting LTE Japanese market 83
Figure 51: Vendors selected by Verizon Wireless for LTE deployment 83
Figure 52: Evaluation of the capacity of mobile networks 84
Figure 53: Traffic and capacity for a MNO with 17 M subscribers 91
Figure 54: Traffic and capacity for a 1 km² area 92
Figure 55: Network improvements timeline 94
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