ROI Analysis of WiFi Offloading

1. ROI Analysis of WiFi Offloading

2. …the bad news is… To offload or not to offload… • Operators of mobile cellular data networks (3G and 4G) face an uphill battle against increasing data usage and declining ARPUs • ROI on macro network expansions scrutinized by shareholders • iPhones, iPads, Android smartphones, netbooks are all tapping the mobile network for access anywhere, anytime • Majority of access occurs when the user is stationary in high traffic density areas (home, office, coffee shop etc.)

3. …the good news is WiFi offload already occurs naturally… Comscore found that more than one third (37.2 percent) of U.S. digital traffic coming from mobile phones occurred via a WiFi connection. Phil Marshall head of Tolaga Research. estimates that about 20 percent of iPhone traffic on AT&T Mobility's network is landing on the public WiFi network, and it's likely that another 60 percent is landing on home WiFi networks now that the operator has instituted tiered data plans, he said. “When you have WiFi, you do more, and that effect is pretty hard to measure," Steve Glapa, Rukus. "But nonetheless, I would say network traffic on PCCW's network would be 20 percent higher than if it didn't have WiFi. In some of the dense traffic areas of Hong Kong, some 80 percent of data traffic is traveling over WiFi”, he said by Karl Bode Thursday 27- Oct 2011

4. …a fair assumption according to Light Reading would be… 65% of mobile data traffic already offloaded • Users are in Wi-Fi coverage 63 percent of the time during the day/ • 65 percent of traffic can be offloaded to Wi-Fi under typical usage conditions using on-the-spot offload • Greater offload performance can be achieved if the user is prepared to accept delayed offload (e.g., sync your videos or photos when you get home) • This means that out of the 7GB usage per month, 4.5GB would travel by Wi-Fi and 2.5GB by cellular. Therefore a 2GB-3GB per month cellular data plan is probably enough for most users. Observation: A cellular service provider already benefits from WiFi Offload without making any investment in deploying a WiFi Network of its own.

5. …in-depth analysis required to make informed decision… How do we quantify the offload dilemma? • How do we analyze the impact of a 3G/4G Service Provider’s Own WiFi Offload Deployment on its overall Business Case? • Does it make economic sense to deploy a WiFi Offload Network? • What is the Business Case for MNO WiFi Offload? • What is the ROI of an MNO WiFi Offload? Answer: Develop a WiROI™ Model to answer these questions

6. …the wireless 20|20 process… Using a holistic approach • Study the Impact of WiFi Offload on an Urban LTE Deployment • Deploy an LTE network for Coverage • Deploy a WiFi Network for Capacity • Surgically place the WiFi AP’s in high traffic areas • Calculate the TCO (CapEx and OpEx) for the WiFi Offload Network • Compare to the TCO of providing Capacity by deploying LTE Cells for Capacity • Understand the key deployment parameters which lead to positive economic impact of WiFi off load • Calculate and Compare ROI metrics such as NPV, IRR

7. …WiROI™ capabilities… Real time simulation and analysis • Allow the simulation of public wifi offload to vary the % of the natural offload • Allow the simulation model to vary the % of the Urban Area covered by WiFi • Allow the simulation model to vary the Density of WiFi AP’s per sq km • Vary WiFi Vendor Equipment Performance and Price (Cell Radius of AP’s, cost of AP’s) • Vary the WiFi Backhaul Cost • Vary the operating cost of the WiFi network • Vary the total data capacity a user consumes on a monthly basis Objective: Discover under what conditions WiFi Offload pays off

8. …CAPEX and OPEX drivers… Major Assumptions • One Time Costs (CapEx) • Cost of WiFi Access Point • Cost of WiFi AP installation • Cost of Backhaul Equipment • Cost of Backhaul Equipment Installation and provisioning • WiFi Core Network Equipment (Servers, Portals, etc.) • Recurring Costs (OpEx) • Monthly WiFi and Backhaul Site Rental • Monthly WiFi and Backhaul Maintenance • Monthly Traffic Backhaul Cost Objective: Discover under what conditions WiFi Offload pays off

9. …customized analysis of traffic density… Cover Areas of High Traffic Density

10. …and WiFi AP Density… More AP’s per Sq Km provide better offload

11. …coverage vs. traffic offload… Coverage vs. Traffic Offload • Hotspot coverage of high traffic density areas versus contiguous coverage yields better offload percentage • Customized formulas for different market conditions Coverage

12. WiROI™ Research Analysis Case Study 1 – LTE Deployment on New York City

13. …large dense urban city… Baseline: New York City • Assumption: LTE deployment in NYC • Activity: Simulate TCO impact of implementing MNO WiFi Offload Network • Identify: The scenario for optimal financial return • Analyze: Understand main drivers of the results

14. …WiFi vs. LTE assumptions… Assumptions: CapEx & OpEx

15. …WiFi offload results… Analysis: TCO impact of WiFi Offload • TCO savings of about $123m already with only 20% coverage and density of 24 AP’s per sq km. • Optimal TCO savings of $253m is achieved with 100% coverage and 42 AP’s per sq km. • The number of macro LTE capacity sites reduced by 1,447 and replaced by 33,138 WiFi access points. TCO Savings $123m Optimal Commercial Proposition? TCO Savings $253m Observation: Optimal Financial Return might not be Optimal Commercial Proposition

16. …New York results… Analysis: Optimal TCO Results

17. WiROI™ Research Analysis Case Study 2 – LTE Deployment in San Diego, USA

18. …midsize urban city… Baseline: San Diego • Assumption: LTE deployment in San Diego • Activity: Simulate TCO impact of implementing MNO WiFi Offload Network • Identify: The scenario for optimal financial return • Analyze: Understand main drivers of the results

19. …WiFi offload results… Analysis: TCO impact of WiFi Offload • TCO savings of about $10m already with only 20% coverage and density of 24 AP’s per sq km. • Optimal TCO savings of $16m is achieved with 40% coverage coverage and 24 AP’s per sq km. • At 40% the number of macro LTE capacity sites reduced by 100 and replaced by 2,400 WiFi access points. TCO Savings $16m TCO Savings $10m Observation: Beyond 80% coverage you start to see diminishing returns

20. …San Diego results… Analysis: Optimal TCO Results

21. …large vs. midsize city deployment… Conclusions & Recommendations • In New York, a dense urban environment with high traffic profile, MNO WiFi Offload is optimal at 100% coverage • In San Diego, a urban environment, with high traffic profile, MNO offload is optimal at 40% with diminishing return beyond 80% • MNO WiFi offload makes an compelling business case under the right circumstances • Main driver is OpEx, especially the WiFi site rental and backhaul costs as well as the assumed growth of the traffic demand on the 3G/4G network. • OpEx less than $40 per month a highly attractive solution • OpEx exceed $100-$150 per month, it becomes challenging Recommendation: Create a customized WiROI™ Tool for your market to drive informed decisions. Contact us at www.wireless2020.com

22. …test drive our WiROI™ 4G WiFi Offloading Tool online… Test drive the WiROI™ Tool by register online at www.wireless2020.com to gain access to our online demo or contact us for a WebEx Demo. USA - Eastern Region Haig A. Sarkissian +1-408-884-1561 info@wireless2020.com Peru Office Magnus Johansson +51 988 352 info@wireless2020.com Media Relations Robin L. Bestel +1-610-861-5956 robin@wireless2020.com USA - Western Region Randall C. Schwartz +1-650-490-3090 info@wireless2020.com Armenia Office Levon Mkrtchyan +374 91 421-266 info@wireless2020.com Spain Office Azat Sakanyan +1-408-884-1561 info@wireless2020.com Tonse Telecom #446, 2nd Cross,9th Main, HAL 2nd StageBangalore India 560 038Phone: +91 80 4211 5355 WiROI™ Tool Demo