Capacity planning in mobile data networks experiencing exponential growth in demand

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.

The mega disruptive challenges …

Mega Hz

Dr. Kim Kyllesbech Larsen, 3G, HSPA+ & LTE Optimization, April 17th 2012, Prague, Czech Republic. 2

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)


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)


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.

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.

3G traffic distribution
50% 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.


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.


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.


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 ….


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.

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.

3G Growth …will continue … for some time
growth …will continue
and 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.

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


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 could
Increase 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.

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


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.

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.

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.

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 ….


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 ….


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.

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.

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.


Detecon is specialized in providing ICT management
consulting services with the infrastructure of a global player.
DETECON International GmbH
Detecon advises on the issues of strategy, organization, and
technology design for Telecommunications and IT companies.
Contact: M.-A. Schultze
Phone +49 160 8841957
Established in 1977, Detecon is experienced, thanks to the
successful realization of more than 6,000 projects. www.detecon.com
info@detecon.com

Detecon is international, with worldwide representation, clients in
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Detecon has in-depth knowledge of the
industry and a consulting approach
oriented towards implementation
and cooperation as partners.

Detecon is part of
Deutsche Telekom Group.

Detecon Branch Offices


Acknowledgement: I am indebted to Veli-Pekka Kroger and Dejan Contact: kim.larsen@telekom.de
Radosavljevik for greatly enhancing this work with valuable discussions and
sharp analytical insights. Last but not least I acknowledge my wife Eva
Mobile: +31 6 2409 5202
Varadi for her great support and understanding during the creation of this http://nl.linkedin.com/in/kimklarsen
presentation. http://www.slideshare.net/KimKyllesbechLarsen

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