# Subscriber Demands and – Network Requirements the Spectrum Capex

```Subscriber Demands and
Network Requirements –
the Spectrum Capex
Hugh Collins
Agenda





Traffic modelling principles
Service modelling
Data: the growth area
Mobile network dimensioning
Spectrum Efficiency Tool: modelling the
relation of spectrum, traffic and network
dimensioning
Traffic Modelling Principles
Traffic modelling principles
 The network must carry the offered traffic!
 … but carrying all traffic is hard to do – traffic peaks
can be very high
– Partly a technical problem – spectrum is limited, so
networks have limited capacity but traffic peaks can be far
above average traffic
– Therefore an economic problem also – if the network is built
to handle the peaks, then it is very under-used for most of
the time
 “Grade of Service” – probability of network busy
– Calls fail, data transmitted slowly or delayed
– Wireless networks usually designed to reject about 2% of
voice calls in the busy hour
For voice use Erlang B; For Data use Erlang C
The Erlang B formula
 Erlang B calculates the probability of blocking
– The probability that a call arriving at a link or switch (with a
defined capacity) finds the link/switch busy
 Erlang B is used for low latency traffic such as voice
or video calls
Pb 
Em
m!
i
m
E
i0 i!
– Pb = Probability of blocking (%)
– m = number of servers/ circuits/ links/ lines
– E = λh = total amount of traffic offered (Erlangs)
(Arrival rate x average holding time)
Traffic (erlangs)
Calculating Erlang B
50
45
40
35
30
25
20
15
10
5
0
5%
2%
1%
0
10
20
30
Number of lines
40
50
The Erlang C formula
 Erlang C calculates the probability of waiting in a
queuing system
– If all servers are busy when a request arrives, the request is
queued
– An unlimited number of requests may be held in the queue
simultaneously
 Erlang C used for data traffic
Em m
m! m  E
Pw 
i
m1 E
Em m
i0 i!  m! m  E
– PW = probability of queuing for a time &gt; 0 secs (%)
– m = number of servers/ circuits/ links/ lines
– E = total amount of traffic offered (Erlangs)
Traffic (erlangs)
Calculating Erlang C
50
45
40
35
30
25
20
15
10
5
0
5%
2%
1%
0
10
20
30
Number of lines
40
50
Service modelling
Categorising subscriber services
 Before we can dimension, we need to understand the
services and their traffic requirements
 Various methods of categorising can be used
 One potential way is presented in ITU-R Rec M.8161:
– Speech: Toll quality voice (64kb/s on a fixed network, much
less than this on a mobile network)
– Simple messaging: User bit rate of 14 kb/s
– Switched data: User bit rate of 64kb/s
– Asymmetrical multimedia services
 Medium multimedia: User bit rate of 64/384 kb/s
 High multimedia: User bit rate of 128/2000 kb/s
– High interactive multimedia: User bit rate of 128/128kb/s
 Faster services represented as multiples of this
 However service speeds have risen in the past decade!
1
ITU-R Recommendation M.816 - Framework for services
supported by International Mobile Telecommunications-2000
Typical service characteristics
 Some typical values are shown below, but local data
should be used where available
Busy Hour Call
Attempts
Call duration (seconds)
Activity factor
Pedestrian
Vehicular
Pedestrian
Vehicular
Pedestrian
Vehicular
Speech
0.8
0.4
120
120
0.5
0.5
Simple
messaging
0.3
0.2
3
3
1
1
Switched data
0.2
0.02
156
156
1
1
Medium
multimedia
0.4
0.008
3000
3000
0.003/
0.015
0.003/
0.015
High
multimedia
0.06
0.008
3000
3000
0.003/
0.015
0.003/
0.015
High
interactive
multimedia
0.07
0.011
120
120
1
1
Source: ITU-R Report M.2023 – Spectrum requirements for IMT-2000
Service demands will also vary by location
 Different areas will provide:
–
–
–
–
Different population densities
Different service mixes
Different service demands
Different service time profiles
 Consider, for example:
Hot spots
– Airports
– Railway or bus stations
– Cafes
Hot routes
– Motorways/ highways
– Railway lines
Service demands vary by time
 Our earlier service characteristics were partly defined
by Busy Hour Call Attempts (BHCA)
– But voice and data busy hours are typically different
– And data typically has similar use across a number of hours
7%
6%
Voice
5%
Data
4%
3%
2%
1%
0%
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Percentage of daily traffic
8%
Time of day
Data: the growth area
Data applications
 E-mail:
– Message 5-10 kbytes
– Attachment 20-1000+ kbytes
– 10 messages in busy hour?
–
 average 1 Mbyte per
user in busy hour
– Symmetrical up and down
 Internet browsing:
hour
– Average 50 kbytes per page
–  average 2 Mbytes per
user in busy hour
– Asymmetrical: more down
than up
 Streamed audio:
– 128 kb/s
– Average say 5 mins in busy
hour
–  average 4.8 Mbytes per
user in busy hour
– Downstream
 Streamed video:
– 512 kb/s
– Average say 5 mins in busy
hour
–  average 19.2 Mbytes
per user in busy hour
– Downstream
Data growth
 Global mobile data traffic is growing very fast:
– Nearly tripled year-on-year, for the past 3 years!
– In March 2010, Ericsson reported that global mobile data
traffic overtook mobile voice traffic
CAGR 92%
Source: Cisco Visual Networking Index 2011
Driven by devices
 The introduction of smarter mobile devices drives
data increases (as well as the applications used!)
Source: Cisco Visual Networking Index 2011
Mobile network dimensioning
Example: A mobile network
BTS
BSC
GMSC
BSC
BTS
MSC
BSC
BSC
BTS
BSC
VLR
O
t
h
e
r
HLR
MSC
GMSC
VLR
BSC
BTS
BSC
MSC
VLR
BSC
BTS
GMSC
BSC
MSC Layer
Transit Layer.
May not exist in all
networks
N
e
t
w
o
r
k
s
Network components to be dimensioned
–
–
–
eNode-B/ Node-B/ BTS
RNC (Radio Network Controller) or BSC (Base Station Controller)
 Core Network:
–
–
–
–
Links: for example STM-1, Gigabit Ethernet, 10GE
Routers, Switches
Databases: for example HLR, VLR
Network operations and management
 Application Platforms:
–
–
–
–
Data/ Internet access
Voicemail
MMS/ SMS
etc
Network component capacities
 In radio networks, the relevant measures of capacity
are:
–
–
–
–
–
connected subscribers
voice minutes
megabytes of traffic
erlangs of traffic
service platform usage
Challenges created by traffic growth
 There are many! And the whole network is affected
 Some examples:
– More sites/ smaller site radii
– Increase in backhaul capacity
 Movement towards high capacity microwave/ fibre
– Need for Evolved Packet Core
 To facilitate improved session, mobility and QoS management
– Improvements in ‘back-office’
 For example, the challenges faced in billing to measure ‘caps’ and
charging
– Improvements in network monitoring and management
 To identify and removing bottlenecks
 To optimising equipment performance and interworking
Spectrum Efficiency Tool:
modelling the relation of spectrum,
traffic and network dimensioning
Main network dimensioning dependencies
QoS
Services
Traffic
Site count /
Network cost
Available
spectrum
RAN site
Capacity
A typical network dimensioning process
1. Set the objectives, for example:
– The technology to be used
– The geographic and population coverage
– The traffic throughput
– The Quality of Service
With the spectrum available, these parameters determine the
network’s capacity
2. Obtain the geographic and population data
– Define/ designate and use types; rural, suburban and urban
3. Compute the number of sites required to meet the
objectives
– Thereby the network design/architecture
– Thereby the network cost
Site / spectrum requirements modelling
 An engineering model to generate dimensioning of
radio network under varying assumptions of:
– Subscriber numbers / market share
– Services provided / traffic offered
– Spectrum available
 Illustrates how changing subscriber demands can
have a significant impact on the network
– The spectrum versus sites trade-off
– The cost versus capacity versus QoS trade-off
 Developed to examine and optimise spectrum
allotment / assignment decisions
Model overview
Subscriber
population
Calculator
engine
Regional
coverage
Spectrum/
technology mix
Graph store
Traffic/
service mix
Required
spectrum,
site count
&amp; costs
Basis for spectrum calculation
 Based on ITU-R Recommendation M.1390 - Methodology for
the Calculation of IMT-2000 Terrestrial Spectrum requirements
 For each service:
Tes
FTerrestria l  β αesFes  β αes
Ses
where:
FTerrestrial= Terrestrial component spectrum requirement
 = Guard band adjustment factor
es = Geographic weighting factor
Tes = Traffic
Ses = Net system capability
(MHz)
(dimensionless)
(dimensionless)
(Mb/ s / cell)
(Mb/ s / MHz / cell)
Net system capability
 Accounts for underlying modulation &amp; multiple access factors ...
– ... as well as radio resource management factors
– Such as power control, discontinuous transmission, frequency
reuse pattern, band splitting/grouping, frequency hopping, adaptive
antennas
Net system capability for different evolutions of systems, Hideaki Takagi and Bernhard H. Walke
(2008), Spectrum Requirement Planning in Wireless Communications, pp56, John Wiley &amp; Sons Ltd
Spectrum requirements calculation overview
Busy hour call
attempts /
Call duration /
Activity factor
Cell area /
Population density /
Penetration rate
Number of
users / cell
Geographic
weighting factor
Service channel bit
rate
Guard band
Offered traffic
/ user
Offered traffic
/ cell
Group size
Offered traffic
/ group
Channels /
cell
Required bit
rate / cell
Blocking probability
(delay critical)
Queuing probability
(non delay critical)
Channels /
group
Required
spectrum
Final
total spectrum
requirement
Setting accessible population data
ID
Name
%
1 Region A
2 Region B
3 Region C
4 Region D
5 Region E
6 Region F
7 Region G
8 Region H
9 Region I
10 Region J
11 Region K
12 Region L
Total
Population
Urban
Rural
Total
#
%
#
#
93.9 2,127,400
6.1
137,700 2,265,100
39.2
107,900
60.8
167,100
275,000
95.1
829,000
4.9
42,600
871,600
82.9
862,800
17.1
178,500 1,041,300
75.8
102,000
24.2
32,500
134,500
62.6
109,900
37.4
65,600
175,500
54.8
60,900
45.2
50,300
111,200
71.2
104,100
28.8
42,200
146,300
85.6
109,200
14.4
18,300
127,500
70.9
58,100
29.1
23,800
81,900
71.8
281,200
28.2
110,700
391,900
34.9
79,600
65.1
148,600
228,200
83 4,832,100
17 1,017,900 5,850,000
 Defining how the population in each region is split between
each geotype ....
 ... and then defining what percentage of this population is
accessible
ID
Name
Terrain
City Type
%
1
2
3
4
5
6
7
8
9
10
11
12
Region A
Region B
Region C
Region D
Region E
Region F
Region G
Region H
Region I
Region J
Region K
Region L
Hilly
Desert
Flat
Flat
Hilly
Hilly
Desert
Hilly
Flat
Hilly
Hilly
Hilly
Total
Large
Small
Large
Large
Small
Small
Small
Small
Small
Small
Small
Small
Landmass
Urban
Rural
Total
2
2
km
%
km
km2
4.0
303
96.0
7,265
7,569
0.6
147
99.4
26,496
26,646
2.5
119
97.5
4,662
4,782
11.9
186
88.0
1,380
1,568
5.0
21
95.0
393
413
6.7
27
93.3
381
409
0.2
58
99.8
32,832
32,894
4.3
40
95.7
893
933
0.7
45
99.3
6,840
6,886
1.3
28
98.7
2,178
2,205
6.2
69
93.8
1,040
1,109
2.0
72
98.0
3,548
3,621
1.3
1,115
98.7
87,908
89,035
 Terrain type, city type and geotype define how signals
Setting target coverage levels
Target Coverage
ID
Urban
Landmass
Population
2
%
km
Factor
#
87.7
266
0.90 1,818,927
66.0
97
0.90
92,255
92.1
110
0.90
708,795
87.9
164
0.90
737,694
94.9
20
0.90
87,210
93.2
26
0.90
93,965
52.0
30
0.90
52,070
87.4
35
0.90
89,006
81.9
37
0.90
93,366
96.3
27
0.90
49,676
93.8
64
0.90
240,426
92.4
66
0.90
68,058
84.4
941
4,131,446
Name
1 Region A
2 Region B
3 Region C
4 Region D
5 Region E
6 Region F
7 Region G
8 Region H
9 Region I
10 Region J
11 Region K
12 Region L
Total
Rural
Landmass
Population
2
%
km
Factor
#
4.0
291
0.50
65,408
0.5
140
0.50
79,373
2.5
116
0.50
20,235
11.9
164
0.50
84,788
5.0
20
0.50
15,438
6.7
26
0.50
31,160
0.2
58
0.50
23,893
4.3
38
0.50
20,045
0.7
45
0.50
8,693
1.2
27
0.50
11,305
6.2
64
0.50
52,583
2.0
70
0.50
70,585
1.2
1,059
483,503
Total
Landmass Population
km2
#
557 1,884,335
237
171,627
226
729,030
328
822,482
40
102,648
51
125,125
88
75,962
73
109,051
82
102,059
54
60,981
128
293,009
137
138,643
2,001 4,614,948
 Define the target coverage

In this example, defined by existing operator coverage levels
 Population factor estimates the ratio of population living in the
coverage area
Calculating cell area
most limiting
service
Urban geometry
Region
topography
COST 231
Propagation
Models
Antenna
geometry
Sectors / site
Cell range
Cell area
 Cell area together with population, geographic and coverage
data enable subscriber densities to be calculated
Setting services and use statistics
16
16
0.5
0.5
0.5
0.5
14
14
1
1
1
1
64
64
1
1
1
1
64 384 0.003 0.015 0.003 0.015
128 2000 0.003 0.015 0.003 0.015
128 128
1
1
1
1
Vehicular
Pedestrian
Service
Net
channel bit
cap
rate* (kb/s) (bit/s/
Activity Factors
73
0.8
0.4 120 120
40
0.3
0.2
3
3
13
0.2 0.002 156 156
15
0.4 0.008 3000 3000
15 0.006 0.008 3000 3000
25 0.007 0.011 120 120
Vehicular
Pedestrian
73
40
13
15
15
25
Vehicular
Pedestrian
Call Duration Net user
(s)
rates (kb/s)
Vehicular
Service
Speech (S)
Simple Message (SM)
Switched Data (SD)
Medium Multimedia (MMM)
High Multimedia (HMM)
High Interactive Multimedia (HIMM)
Busy Hour
Call
Attempts
Pedestrian
Penetration
rate (%)
16
16 0.07
14
14 0.125
64
64 0.125
64 384 0.125
128 2000 0.125
128 128 0.125
 Define what services are used and how they are
used
 The above example relates to 3G
 Traffic metrics based on ITU-R Report M.2023 Spectrum Requirements for IMT 2000, but real observed
traffic figures should be used wherever possible
Calculating spectrum required
 The traffic offered by each service can be calculated
 This can be aggregated and mapped to traffic
channels
– Using Erlang B and Erlang C, as appropriate
 From this, the amount of required spectrum can be
derived (using the ITU-R Rec. M.1390 formula)
– To meet demanded traffic, as driven by the subscriber
numbers
– Based on calculated site numbers
Spectrum requirements planning
 The spectrum calculation is made many times by
0%
Cellular
Site
Minimum
(economic limit)
100%
Maximum
More spectrum required
More sites required
 The results can then be graphed, and interpreted ...
Typical output: spectrum versus site count
Spectrum Site Count (Capacity)
7000
Market
share
6000
10%
Site Count
5000
20%
30%
4000
40%
50%
3000
60%
70%
2000
80%
90%
1000
100%
0
0
5
10
15
Spectrum (MHz)
20
25
30
Interpreting the curves
Sites
Volatile choice (model viewpoint)
Optimum area (purely
technical perspective)
Unsatisfactory
choice
(operator starved)
Greedy choice
(Poor network design,
spectrum hoarding)
Spectrum
Block steps/packaging representing possible choices
Improved capacity of networks, improved economy for operators
Better for re-farming, more competition possible
Simple 2-operator example, 36MHz available
9000
8000
7000
Number of BTS sites
6000
5000
Operator A BTS
Operator B BTS
4000
Total BTS
3000
2000
1000
0
2
4
6
8
10
12
14
16
18
Operator A MHz Allotted
20
22
24
26
28
30
Thank you
Any questions?
```