Capacity planning in mobile data networks experiencing exponential growth in demand

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Had the pleasure to deliver the key note presentation at Informa's 3G, HSPA & LTE Optimization conference in Prague. Great event with many very important presentations.

Had the pleasure to deliver the key note presentation at Informa's 3G, HSPA & LTE Optimization conference in Prague. Great event with many very important presentations.

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  • 1. Capacity planning in mobile data networks experiencing exponential growth in demand. Informa’s 3G, HSPA & LTE Optimization Conference, 17th April 2012, Prague, Czech Republic. .Dr. Kim Kyllesbech Larsen,Technology, Deutsche Telekom AG.
  • 2. The mega disruptive challenges … Mega Hz Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 2
  • 3. A typical data traffic day in Europe. Illustration data voice 00:00 6:00 8:00 10:00 12:00 14:00 17:00 22:00 Small Cells @Home On the @Work On the @Home (1 – 2 Cells) Go (2 – 4 Cells) Go (2 – 3 Cells) Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 3
  • 4. Today’s bit pipe and the bottlenecks.Network expansion as traffic “management” remedy. Air/TRX/Site Node Backhaul Backbone Core Web Spectrum & Processing bandwidth bandwidth Switching Apps servers Floor space capacity Bandwidth, CPU & Storage. traffic pressure points + Sectorization + Small cells due to aggregation + Additional spectral capacity (if available) Off Loading + Introduce more(AP, Femto, …) efficient technology RNC RNC SGSN SGSN GGSN GGSN LL → MW → Fiber → + Colors Packet Web 2.0 Core Node +CPU +CPU (i.e., CE, etc.) (up-to system limit) + Colors + CPU + switching + switching capacity capacity RRC RAB PDP context Optimized radio resource management (control plane) Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 4
  • 5. Data traffic trend to be considered.Most mobile data traffic is fixed-like in its usage. Illustration Number of sites utilized per usage category. 35 31 100% traffic 30 80%+ traffic 25  50% of all traffic generated in 1 cell1. 20 “20% mobility”  80% data traffic carried by 3 cells1. 15 10  Remaining 20% carried over 28 cells. 5 2 3 4 3 3 2 2 2 1 1 1 1 0 Traffic off-load via WiFi & small-cell should be pursued more aggressively. 1 on a per user basis. Note: This empirical law applies to volume as well as packet switched signaling. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 5
  • 6. Law of small numbers of large consumption.Usage trend very much Pareto like. Illustration Customers versus Data Volumetric Consumption 12 month ago  80% subs took 20% of data traffic. Today  A bit more than 30% of data traffic1 Data Volumetric Consumption Ca. 5% of active data users consume more than 1GB per month, more than 3 × the average monthly usage. 1 Some of the diffusion over the 12 month might also be impacted by FUP cutting off the extreme usage. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 6
  • 7. 3G traffic distribution50% of sites carries 80% of 3G devices and 95% of 3G traffic. @ Busy Hour 3G-Devices, 3G-Traffic Illustration 100% 80%  20% of 3G-cells carries 50% of 3G devices. 60%  50% of 3G-cells carries 80% of 3G devices. 40%  20% of 3G-cells carries 60+% of 3G traffic. 20% 3G Devices  50% of 3G-cells carries 95% of 3G traffic. 3G-Traffic Volume 0% 0% 20% 40% 60% 80% 100% 3G-Cells Relative few network resources serves most of the demand. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 7
  • 8. Postpaid trends – growth slowing down? Data growth 100% iPhone Volume growth 180% Smartphone 160% Smartphone penetration 80% 65% “Basic phone” 50% 30% Active Postpaid 2009 2010 2011 Data users Other 15% Data customer growth 275% Android 27% Total Blackberry Android Android 14% 95% 120% 35% 40% 65% Apple Illustration 44% 2009 2010 2011 Note: >90% of all smartphones are active data users. 65% of all postpaid have a smartphone, iPhone has a 40% share of all postpaid smartphones. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 8
  • 9. Prepaid trends – the next growth wave! 100% Prepay data growth 500% Volume growth Smartphone penetration Smartphone 250% iPhone. 3% 7% 22% “Basic phone” 2009 2010 2011 Active Prepaid Data users Prepay data customer growth Other 16% Android 550% 19% Total Android 400% Apple 10% 170% Blackberry 55% 60% Illustration 2009 2010 2011 Note: 61% of all prepaid smartphones are active data users. Ca. 20% of all prepaid have a smartphone, iPhone share is 10% of all prepaid smartphones. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 9
  • 10. The difference between post- & pre-paid? Illustration Daily volumetric profile Busy Hour usage patterns Postpaid Prepaid 15 : 1 Postpaid Prepaid 00 02 04 06 08 10 12 14 16 18 20 22 4 distinct postpay usage segments with 3 similar for prepay 1 …. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 10
  • 11. OS …Last 12 month in smartphone heavy MNO. Illustration PS Signaling development Jan-11 per device RIM +12 Month - 30% Signaling Android: from 10% 25% share + 25% volume Windows - 35% signaling Apple iOS Symbian “Basic phone” Volume development per device Great improvement in iOS & RIM signaling load … Android not so! 1 Size of bubbles = share of active devices. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 11
  • 12. PS Signaling … The network challenge?Remains a challenge for network aggregation points. Illustration CAGR +95% over period Introducing 3GPP Fast Dormancy Introducing CELL-PCH 1 +140% +200% -50% Much have been done on signaling … and “we” have gotten smarter. 1 NSN based feature. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 12
  • 13. 3G Growth …will continue … for some time growth …will continueand eventually decline as subs convert to LTE. Illustration Illustration of a European Market with ca. 50+% prepaid base. Total 3G Data Traffic1 CAGR 45% @ 2012 - 2017 GSM 3G Conversion 3G Prepaid 3G LTE CAGR 75% Conversion @ 2006 - 2011 3G Contract 2006 2017 2025 1 Note: Due to the complex dynamics of technology migration and dependency on operator policy the phase-off of 3G is highly uncertain. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 13
  • 14. Total growth … another leap with LTE. Illustration Illustration of a European Market LTE introduction 2013 earliest. CAGR 52% @ 2020 - 2025 Total Data Traffic by 2025 3G LTE Conversion 500+ 2015 traffic @ 100% LTE share LTE CAGR 84% @ 2013 - 2018 LTE 2 3G Traffic @ 30% LTE share LTE 2012 2018 2025 Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 14
  • 15. When data demand exceeds spectral efficiency gains.”Houston we have problem”. Illustration of a European market 1 The spectrum crunch. Total spectrum in use for mobile data 10 20 40 60 85 120 120 120 120 120 120 15 Leapfrog network capacity, e.g., Spectral Efficiency (*) Spectral demand (limited) Small cells topologies Spectral demand (unlimited) Spectral demand couldIncrease over 2010 10 Smart antennas exceed spectral efficiency 3G LTE LTE-a Early LTE deployment between 2014 - 2016. Conversion Price, Control & Policy. 5 NOT GOOD AT More spectrum. ALL! 0 2010 2012 2014 2016 2018 2020 1 Mobile operator with (1) 20MHz @ 800MHz (LTE), (2) 20MHz @ A lot more 900MHz (2G HSPA),(3) 50MHz @ 1800MHz (2G LTE), (4) 30MHz @ 2100MHz (HSPA+). Total spectrum position 120 MHz. Complexity, Capex and Opex (*) realWireless report for Ofcom,: 4G Capacity Gains, Final Report, January 2011. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 15
  • 16. Data Mining – perception versus real experience.Tangible network factors impacting customer perception. Etc.. Financial Data Network Data Experience QoE Satisfaction Data Speed Segmentation CSSR Data CDR Expectations Expectations unfulfilled fulfilled Customer Behavioral Service Data Data Dis-satisfaction Device Network State Mobility From cell level up Data Voice SMS Signaling Load Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 16
  • 17. Data Mining – customer perception versus experience.Strength of market survey data with hard network-centric data. Illustration Dissatisfied Groups Characteristics  < 90% of the time on 3G when using data.  3G Coverage & Capacity.  Successful PDP context creations < 80%.  3G Voice Call Setup Duration > 3 seconds.  Network Optimization.  2G Voice Call Setup Duration > 5 seconds.  Postal code areas (i.e., coverage/capacity)  Re-prioritizing deployment.  Handset type (e.g., iPhone 3GS and Blackberry 9700) .  Ca. 35+% of smartphones.  Data usage > 300MB per month.  Ca. 30% of active customer.  Number of sites visited > 60.  < 5% of active customer.  Voice call duration per month >450 minutes. Out of Technology Scope  A relatively high bill. (i.e., higher bill, higher expectations)  Dependency on perceived quality. *Participants in the survey are informed and agreed (i.e., opt in policy applied) that their data will be used for research. No DPI applied. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 17
  • 18. Data Mining – the Big Data picture 1.Capacity planning on the cell level using data mining strategies. Illustration Cell Input Xj (per hour). <Voice calls>, <R99 users>UL, DL C4ell <HS-D/U-PA users>, Max HS-D/U-PA users, Radio Resource Control Attempts*, C1ell Radio Access Bearer (total, voice, data) <Soft-HO area>, < DL / UL Speed> Cn-2ell Cnell <Voice / Data proportion originating in cell> Cn-1ell Cell Output: Ci=1..5  n = 20,000 Cells 1. RAB release by interference  5 load-functions (output) 2. Average Noise Raise (ANR)  16 input cell-level parameters (input) 3. R99 specific ANR  Up-to 100,000 regression models. 4. Consumed DL Power  Planning validity < 4+ month 5. No Code Available 1 Paperon “Mass Scale Modeling for Prediction and Simulation of the Air-Interface Load in 3G Radio Access Networks”, by Radosavljevik, v.d. Putten & K. Kyllesbech Larsen submitted to The 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining’12, *One 1 RRC per active device. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 18
  • 19. The network state-equation .. is there such a thing?Calculating the critical driver limit for capacity demand. nj #active devices Illustration Fundamental load drivers  Number of devices per cell.  Rate of concurrent instances of demand per unit time. Effective rate pj per device Cell Ci Installed capacity *k is the number of standard deviation over the mean that is considered. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 19
  • 20. The network state-equation ...practical applications. Examples from a 3,000+ Node 3G network RRL limit RRL limit for Ultrasite for Flex2 350 100% 350 100% CE limit @ 128 CE Ca. 1,250 smart- 300 300 80% phones per 3-carrier 80% Node-B, carrier 250 250 expansion should be expected. 60% 60% Node-Bs 200 Node-Bs 200 2,500 smart-phones CE limit per 6-carrier Node-B, @ 256 CE 150 150 carrier expansion 40% 40% CE limit should be expected. 100 @ 396 CE 100 Frequency Frequency 20% 20% 50 Cumulative % 50 Cumulative % 0 0% 0 0% Number of smart-phones per Node-B Number of smart-phones per Node-B The network (cell-based) state-equation allows reliable long-term 3G radio resource capacity planning. 1 …. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 20
  • 21. What about FUP? ...Fair? Effective? … or a FAD? Illustration FUP flavors Volume per User  Hard volume-limit throttling. Throttling  BH throttling.  Service based (dpi) throttling. FUP Limit 64 kbps  Traffic de-prioritization … etc… Days of “normal” usage 1 Mbps Re-active remedy. 0 31 Typically capture <2% of users. Subscription days per month Does not address signaling challenge from smartphone Apps. Mobile FUP implementations might not be very efficient as a structural traffic management remedy. 1 …. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 21
  • 22. FUP or FAD?Volume-driven FUP (of today) has little structural impact. Time to 80% 98% < 0.5% FUP Addressable FUP limit < 2% >2,500 Years Example: >250 Years 2,000 FUP relevant users >25 Years 20,000 Cells in Network >2.5 Year 50% of FUP in 20% of Cells 1,000 FUP served by 4,000 Cells 100 Days 30 Days = Reset 10 Days BH mean value of users1 per cell Days to reach 500MB is 185 in the Top 20% Cells. 1 Day Days to reach 2GB 1 FUP relevant customer would 2.5 Hour compete for resources with at least 185 others in the Busy Hour 2.Illustration Daily usage per active user 1 Approx. log-norm distribute, 2 in max ¼ of the Top-20% cells. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 22
  • 23. Pre- & post-FUP implementation.Marginal traffic reduction achieved with FUP. Daily Tail Volume Profile Daily Active Customer Profile >2 GB usage > 2 GB usage Max. 0.25% of total 55% of total traffic Active Base -15% Drop Max -0.05% Drop Pre-FUP Max -10% Drop Pre-FUP Post-FUP Post-FUP 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Illustration Note: Fair Use Policy with throttling to 64kbps after limit has been reached. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 23
  • 24. What we need to be passionate about. Customer usage, experience and imposed policies impact. Deep understanding of data traffic is crucial. Automation (data mining combined with machine learning) way forward. Ensuring best customer experience at all times & at lowest cost. Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 24
  • 25. Detecon is specialized in providing ICT managementconsulting services with the infrastructure of a global player. DETECON International GmbHDetecon advises on the issues of strategy, organization, andtechnology design for Telecommunications and IT companies. Contact: M.-A. Schultze Phone +49 160 8841957Established in 1977, Detecon is experienced, thanks to thesuccessful realization of more than 6,000 projects. www.detecon.com info@detecon.comDetecon is international, with worldwide representation, clients in165 countries, and employees from more than 30 nations.Detecon has in-depth knowledge of theindustry and a consulting approachoriented towards implementationand cooperation as partners.Detecon is part ofDeutsche Telekom Group. Detecon Branch Offices Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 25
  • 26. Acknowledgement: I am indebted to Veli-Pekka Kroger and Dejan Contact: kim.larsen@telekom.deRadosavljevik for greatly enhancing this work with valuable discussions andsharp analytical insights. Last but not least I acknowledge my wife Eva Mobile: +31 6 2409 5202Varadi for her great support and understanding during the creation of this http://nl.linkedin.com/in/kimklarsenpresentation. http://www.slideshare.net/KimKyllesbechLarsen