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Capacity GSM/WCDMA/LTE
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Capacity planning in mobile data networksexperiencing 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.
The mega disruptive challenges …
Mega bits
Mega bucks
Mega Hz
2Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
A typical data traffic day in Europe.
00:00 10:00 12:00 22:0017:00
Illustration
6:00 8:00
voicedata
@Work(2 – 4 Cells)
@Home(2 – 3 Cells)
On theGo
@Home(1 – 2 Cells)
On theGo
Small Cells
3Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
14:00
Today’s bit pipe and the bottlenecks.Network expansion as traffic “management” remedy.
Air/TRX/SiteSpectrum &Floor space
NodeProcessing
capacity
Backhaulbandwidth
Backbonebandwidth
CoreSwitching
WebApps servers
Bandwidth, CPU & Storage.
Off Loading(AP, Femto, …)
PacketCore
Web 2.0
+ Sectorization+ Small cells+ Additional spectral capacity (if available)+ Introduce more efficient technology
LL → MW → Fiber → + Colors
+ Colors+ switching
capacity
+ CPU+ switching
capacity
+CPU (i.e., CE, etc.)(up-to system limit)
RNC GGSNSGSN
traffic pressure pointsdue to aggregation
RNC GGSNSGSN
Node
+CPU
Optimized radio resource management (control plane)
PDP contextRABRRC
4Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Data traffic trend to be considered.Most mobile data traffic is fixed-like in its usage.
50% of all traffic generated in 1 cell1.
80% data traffic carried by 3 cells1.
Remaining 20% carried over 28 cells.
50% of all traffic generated in 1 cell1.
80% data traffic carried by 3 cells1.
Remaining 20% carried over 28 cells.
Traffic off-load via WiFi & small-cell should be pursued more aggressively.
Traffic off-load via WiFi & small-cell should be pursued more aggressively.
31
2 3 4 3 3 2 2 2 1 1 1 1
0
5
10
15
20
25
30
35100% traffic80%+ traffic
Number of sites utilized perusage category.
1 on a per user basis. Note: This empirical law applies to volume as well as packet switched signaling.
“20% mobility”
Illustration
5Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Law of small numbers of large consumption.Usage trend very much Pareto like.
12 month ago
80% subs took 20% of data traffic.
Today
A bit more than 30% of data traffic1
12 month ago
80% subs took 20% of data traffic.
Today
A bit more than 30% of data traffic1
Ca. 5% of active data users consume more than 1GB per month, more than 3 × the average monthly usage.
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.
Customers versusData Volumetric Consumption
Data Volumetric Consumption
Illustration
6Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
3G traffic distribution50% of sites carries 80% of 3G devices and 95% of 3G traffic.
Relative few network resources serves most of the demand.Relative few network resources serves most of the demand.
Illustration
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
3G Devices
3G-Traffic Volume
20% of 3G-cells carries 50% of 3G devices.
50% of 3G-cells carries 80% of 3G devices.
20% of 3G-cells carries 50% of 3G devices.
50% of 3G-cells carries 80% of 3G devices.
20% of 3G-cells carries 60+% of 3G traffic.
50% of 3G-cells carries 95% of 3G traffic.
20% of 3G-cells carries 60+% of 3G traffic.
50% of 3G-cells carries 95% of 3G traffic.
3G-Cells
@ Busy Hour 3G-Devices, 3G-Traffic
7Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Postpaid trends – growth slowing down?
100%
Active PostpaidData users
“Basic phone”
SmartphoneiPhone
180%160%
80%
30%
50%65%
2009 2010 2011
Data growth
Smartphonepenetration
Volume growth
95%
35% 40%65%
120%
275%
2009 2010 2011
Data customer growth
Total
AndroidAndroid
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.
Android27%
Apple44%
Blackberry14%
Other15%
Illustration
8Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Prepaid trends – the next growth wave!
100%
Active PrepaidData users
“Basic phone”
SmartphoneiPhone.
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.
250%
500%
3% 7% 22%
2009 2010 2011
Prepay data growth
Smartphonepenetration
Volume growth
60%
170%
400%
550%
2009 2010 2011
Prepay data customer growth
Total
Android
Android19%
Apple10%
Blackberry55%
Other16%
Illustration
9Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
The difference between post- & pre-paid?
1 ….
Postpaid
Prepaid
00 02 04 06 08 10 12 14 16 18 20 22
Postpaid
Prepaid
Daily volumetric profile Busy Hour usage patterns
15 : 1
4 distinct postpay usage segments with 3 similar for prepay 4 distinct postpay usage segments with 3 similar for prepay
Illustration
10Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
OS …Last 12 month in smartphone heavy MNO.
1 Size of bubbles = share of active devices.
Illustration
Jan-11
+12 Month
“Basic phone”
Symbian
Windows
Volume development per device
PS Signalingdevelopment
per device
Android: from 10% 25% share
Great improvement in iOS & RIM signaling load … Android not so!Great improvement in iOS & RIM signaling load … Android not so!
- 35% signaling
+ 25% volume
Apple iOS
RIM
- 30% Signaling
11Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
PS Signaling … The network challenge?Remains a challenge for network aggregation points.
+140%
-50%+200%
CAGR +95% over period
IntroducingCELL-PCH 1
Introducing3GPP Fast Dormancy
1 NSN based feature.
Illustration
Much have been done on signaling … and “we” have gotten smarter.Much have been done on signaling … and “we” have gotten smarter.
12Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
3G Growth …will continue … for some timeand eventually decline as subs convert to LTE.3G growth …will continue
Illustration of a European Marketwith ca. 50+% prepaid base.
2006 2017
3GContract
3GPrepaid
CAGR 75%@ 2006 - 2011
Total 3G Data Traffic1
3G LTEConversion
CAGR 45% @ 2012 - 2017
2025
GSM 3G Conversion
1 Note: Due to the complex dynamics of technology migration and dependency on operator policy the phase-off of 3G is highly uncertain.
Illustration
13Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Total growth … another leap with LTE.
Illustration of a European MarketLTE introduction 2013 earliest.
2012 2018 2025
Total Data Traffic
LTE CAGR 84%@ 2013 - 2018
CAGR 52% @ 2020 - 2025
3G LTE Conversionby 2025
500+ 2015 traffic@ 100% LTE share
LTE 2 3G Traffic@ 30% LTE share LTE
Illustration
14Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
10 20 40 60 85 120 120 120 120 120 120
0
5
10
15
2010 2012 2014 2016 2018 2020
Total spectrum in use for mobile data
Inc
rea
se
ove
r 20
10
Spectral Efficiency (*)
Spectral demand (limited)
Spectral demand (unlimited) Spectral demand could exceed spectral efficiency
between 2014 - 2016.
Spectral demand could exceed spectral efficiency
between 2014 - 2016.
When data demand exceeds spectral efficiency gains.”Houston we have problem”.
1 Mobile operator with (1) 20MHz @ 800MHz (LTE), (2) 20MHz @ 900MHz (2GHSPA),(3) 50MHz @ 1800MHz (2GLTE), (4) 30MHz @ 2100MHz (HSPA+). Total spectrum position 120 MHz.
Illustration of a European market 1
(*) realWireless report for Ofcom,: 4G Capacity Gains, Final Report, January 2011.
3G LTE LTE-aConversion
Leapfrog network capacity, e.g.,
Small cells topologies
Smart antennas
Early LTE deployment
Price, Control & Policy.
More spectrum.
Leapfrog network capacity, e.g.,
Small cells topologies
Smart antennas
Early LTE deployment
Price, Control & Policy.
More spectrum.
The spectrum crunch.
A lot moreComplexity, Capex and Opex
A lot moreComplexity, Capex and Opex
NOT GOODAT
ALL!
15Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Data Mining – perception versus real experience.Tangible network factors impacting customer perception.
Satisfaction
Dis-satisfaction
Expectations
unfulfilled
Network Experience
Data
Behavioral
Data
Voice SMS
Data
Device
Financial
Data
CDRCSSR
Speed
Data QoE
Etc..
Customer
Service Data
Network State
From cell level up
Signaling Load
Mobility
Segmentation
Data
16Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Expectations
fulfilled
Data Mining – customer perception versus experience.Strength of market survey data with hard network-centric data.
*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.
Dissatisfied Groups Characteristics
< 90% of the time on 3G when using data.
Successful PDP context creations < 80%.
3G Voice Call Setup Duration > 3 seconds.
2G Voice Call Setup Duration > 5 seconds.
Postal code areas (i.e., coverage/capacity)
Handset type(e.g., iPhone 3GS and Blackberry 9700) .
Data usage > 300MB per month.
Number of sites visited > 60.
Voice call duration per month >450 minutes.
A relatively high bill. (i.e., higher bill, higher expectations)
Ca. 30% of active customer.
Re-prioritizing deployment.
Ca. 35+% of smartphones.
Dependency on perceived quality.
3G Coverage & Capacity.
< 5% of active customer.
Network Optimization.
Illustration
17Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Out of Technology Scope
Data Mining – the Big Data picture 1.Capacity planning on the cell level using data mining strategies.
C1ell
C4ell
Cn-2ell
Illustration
n = 20,000 Cells 5 load-functions (output) 16 input cell-level parameters (input) Up-to 100,000 regression models. Planning validity < 4+ month
<Voice calls>, <R99 users>UL, DL
<HS-D/U-PA users>, Max HS-D/U-PA users,
Radio Resource Control Attempts*,
Radio Access Bearer (total, voice, data)
<Soft-HO area>, <DL / UL Speed>
<Voice / Data proportion originating in cell>
<Voice calls>, <R99 users>UL, DL
<HS-D/U-PA users>, Max HS-D/U-PA users,
Radio Resource Control Attempts*,
Radio Access Bearer (total, voice, data)
<Soft-HO area>, <DL / UL Speed>
<Voice / Data proportion originating in cell>
1. RAB release by interference2. Average Noise Raise (ANR)3. R99 specific ANR4. Consumed DL Power5. No Code Available
1. RAB release by interference2. Average Noise Raise (ANR)3. R99 specific ANR4. Consumed DL Power5. No Code Available
1 Paper on “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.
Cell Input Xj (per hour).
Cell Output: Ci=1..5
𝑪𝒊=𝟏 ..𝟓❑ =∑
𝒋=𝟏
𝟏𝟔
𝒂𝒊𝒋 𝑿 𝒋 ∀𝒏
Cn-1ell
Cnell
18Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
The network state-equation .. is there such a thing?Calculating the critical driver limit for capacity demand.
Number of devices per cell.
Rate of concurrent instances of demand per unit time.
Number of devices per cell.
Rate of concurrent instances of demand per unit time.
Critical driver level (e.g., #devices) resulting in capacity upgrade is (simplified)
Critical driver level (e.g., #devices) resulting in capacity upgrade is (simplified)
* k is the number of standard deviation over the mean that is considered.
Illustration
Cell
(v1 ) Voice
(x1 ) R99
(y1 ) Signalling
(z1 ) H
SU
PA
(1 ) H
SD
PA
Effective rate pj
per device
Fundamental load drivers
Average load of cell j:
Solve for ncritical or pcritical
Average load of cell j:
Solve for ncritical or pcritical
nj #active devices
Ci Installed capacity
19Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
The network state-equation ...practical applications.
1 ….
Examples from a 3,000+ Node 3G network
The network (cell-based) state-equation allows reliable long-term 3G radio resource capacity planning.
The network (cell-based) state-equation allows reliable long-term 3G radio resource capacity planning.
0%
20%
40%
60%
80%
100%
0
50
100
150
200
250
300
350
Nod
e-Bs
Number of smart-phones per Node-B
Frequency
Cumulative %
Ca. 1,250 smart-phones per 3-carrier Node-B, carrier expansion should be expected.
2,500 smart-phones per 6-carrier Node-B, carrier expansion should be expected.
0%
20%
40%
60%
80%
100%
0
50
100
150
200
250
300
350
Nod
e-Bs
Number of smart-phones per Node-B
Frequency
Cumulative %
RRL limitfor Ultrasite
RRL limitfor Flex2
CE limit@ 128 CE
CE limit@ 256 CE
CE limit@ 396 CE
20Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
What about FUP? ...Fair? Effective? … or a FAD?
1 ….
Volume per User
FUP Limit
Throttling
Days of“normal” usage
FUP flavors
Hard volume-limit throttling.
BH throttling.
Service based (dpi) throttling.
Traffic de-prioritization … etc…
FUP flavors
Hard volume-limit throttling.
BH throttling.
Service based (dpi) throttling.
Traffic de-prioritization … etc…
Mobile FUP implementations might not be very efficientas a structural traffic management remedy.
Mobile FUP implementations might not be very efficientas a structural traffic management remedy.
Re-active remedy.
Typically capture <2% of users.
Does not address signaling challenge from smartphone Apps.
1 Mbps
64 kbps
21Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Illustration
0 31
Subscription days per month
FUP or FAD?Volume-driven FUP (of today) has little structural impact.
1 Approx. log-norm distribute, 2 in max ¼ of the Top-20% cells.
2.5 Hour
1 Day
10 Days
100 Days
>2.5 Year
>25 Years
>250 Years
>2,500 Years
Days to reach 500MB
Days to reach 2GB
30 Days = Reset
98%< 2%
< 0.5%80% FUP Addressable
Daily usage per active user
Time toFUP limit
Illustration
Example:
2,000 FUP relevant users20,000 Cells in Network
50% of FUP in 20% of Cells1,000 FUP served by 4,000 Cells
BH mean value of users1 per cellis 185 in the Top 20% Cells.
1 FUP relevant customer would compete for resources with at least
185 others in the Busy Hour 2.
22Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Pre- & post-FUP implementation.Marginal traffic reduction achieved with 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
Daily Tail Volume Profile>2 GB usage
Daily Active Customer Profile> 2 GB usage
-15% Drop
Max -10% Drop
Max -0.05% Drop
Post-FUP
Pre-FUP
Post-FUP
Pre-FUP
55% of total trafficMax. 0.25% of total
Active Base
23Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Note: Fair Use Policy with throttling to 64kbps after limit has been reached.
Illustration
What we need to be passionate about.
Deep understanding of data traffic is crucial.Deep understanding of data traffic is crucial.
Customer usage, experience and imposed policies impact. Customer usage, experience and imposed policies impact.
Automation (data mining combined with machine learning) way forward.Automation (data mining combined with machine learning) way forward.
Ensuring best customer experience at all times & at lowest cost.Ensuring best customer experience at all times & at lowest cost.
24Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Detecon is specialized in providing ICT management consulting services with the infrastructure of a global player.
Detecon Branch Offices
Detecon advises on the issues of strategy, organization, and technology design for Telecommunications and IT companies.
Established in 1977, Detecon is experienced, thanks to the successful realization of more than 6,000 projects.
Detecon is international, with worldwide representation, clients in 165 countries, and employees from more than 30 nations.
Detecon has in-depth knowledge of theindustry and a consulting approachoriented towards implementation and cooperation as partners.
Detecon is part ofDeutsche Telekom Group.
DETECON International GmbH
Contact: M.-A. Schultze Phone +49 160 8841957
25Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic.
Thank you for your interest!
Contact: [email protected]: +31 6 2409 5202http://nl.linkedin.com/in/kimklarsenhttp://www.slideshare.net/KimKyllesbechLarsen
Acknowledgement: I am indebted to Veli-Pekka Kroger and Dejan Radosavljevik for greatly enhancing this work with valuable discussions and sharp analytical insights. Last but not least I acknowledge my wife Eva Varadi for her great support and understanding during the creation of this presentation.