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Content

Overview: Principle Planning Steps & GSM/UMTS Differences

Input Requirements: Traffic, Quality of Service, Capacity, Coverage

Propagation Models

Dimension

Radio Link Budget

UL & DL Link Budget

Eb/No & Processing Gain

Power Control Headroom

Soft Handover Gain

Processing Gain

Effective Noise & Interference

Cell Range Calculation

Link Budget & Cel l Range Calculat ion

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Overview: Principle Planning Steps

A

Iu(PS) Gi

PS Core Network Planning

Iu(CS)

Gn

IurUE

(USIM)

Uu

GMSC

MSC

VLR

AuCHLREIRCSE

GGSNSGSN

IWF/TC

RNC

RNC

Iu

Node B

Iub

Node

BIub

Node B

CS Core Network Planning

Transmission Planning

Radio Network

Planning

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Source: ITU

General planning objectives:

To realize service(s) with

at minimum costs

maximum coveragemaximum capacity

maximum Quality of Service (QoS)

minimal interference

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Definition of system requirements:

Coverage requirement

Capacity requirement

Quality of Service requirement

Radio propagation

Output for first dimensioning:

Rough number of base stations

Rough number of sites

Node B configurations

Transmission needs

First

Dimensioning

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Input for detailed planning:

Coverage requirement

Capacity requirement

Quality of Service requirement

Radio propagation

Output for detailed planning:

Selection of sites

Node B configurations

Coverage analysis

Capacity analysis

Quality of Service analysis

RR parameters for cells+

First Dimensioning

Detailed

Coverage &

Capacity Planning

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Input for optimization

Performance Measurements

Drive Tests

Customer Complains

Output for optimization

Physical parameter adjustment

Data base (e.g. RR) parameter

adjustment

Network Optimization

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First Dimensioning

Detailed Coverage andCapacity Planning

Optimization

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TECHCOMConsult ing

Notice and additional remarks

Principle planning steps

1) Basic planning data acquisition (data about:expected traffic load & planned service area) f i rs t d im ension ing

2) Terrain data acquisition& installation of a

digital terrain database(including topographical &

morphological data) into a planning tool

3) Coarse coverage and capacity prediction andinitial site determination for a first site selection

processusing the digital terrain data, standard

propagation models & predicted service usage

4) Site survey & s ite select ion

5) Survey measurements(to fine tune the

propagation models)

6) Detailed network design(to determine final

network structure: Number and configuration of

Node Bs and RNCs; needed antennas and

transmission lines; frequency plan; future evolution

strategy)

7) Transmission planning

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General difference between

GSM andUMTS (WCDMA)

Planning Steps:

In GSMcoverage andcapacitycan be

planned independently:

1.) Coverage planning

2.) Capacity planning

In GSM frequency re-use distance

neighbor cells use different frequencies

In UMTScoverage & capacityare coupled:

Increasing load can decrease coverage

Coverage and capacity must be

planned simultaneously !!

In UMTS frequency re-use = 1

neighbor cells are interference coupled

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Input Requirements:

Traffic & Coverage

Quality of Service & Erlang Theory(Erlang B & C)

Capacity

Traffic, Quality of Service, Capacity, Coverage

Propagation Models Dimension

Radio Link Budget


Input Requirements

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Input Requirements

Traffic

Traffic forecast

Number of subscribers

Service types

Quality of service

Distribution of traffic

Capacity

Available frequency spectrum

Forecast of subscriber

penetration rateInformation about traffic density

Coverage (Path Loss)

Coverage regions

Information about area typePropagation conditions

Quality of Service

Area location probability(coverage probability)

Blocking probability

End user throughput

UE classes

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TECHCOMConsult ing


Traff ic Forecasting:

An important aspect in dimensioning a

telecommunication network is the expected trafficin the future. Therefore, an analysis of the

expected traffic is of great interest. Even in case

that the penetration (number of traffic sources)

saturates, the amount of traffic does not

necessarily saturates, too. Traffic forecasts are

not easy and may be influenced by many aspects:

e.g. price politics, offered services,

The more the important dependencies are

realized and taken into account, the more precise

the forecasts will be.

For a detailed analys isit is useful to:

Split the total PLMN into sub-areas

Categorize the subscribers: e.g. into business,residential,

Analyse: e.g. the number of subscribers per area,

the development of the penetration depth, the

expected penetration depth

Analyse also economic dependencies like e.g.

any correlation between the demand of telephone

service and e.g. the economic activities in a special

region, the economic situation in general (measurede.g. by the economic growth), the income of the

people,

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TECHCOMConsult ing


Traff ic Measurements:

It is of great interest for the network operator to

measure the real traffic situationin his network.To perform such measurements, in former

telecommunication systems special traffic

measurement equipment (e.g. the so called

electromechanical meter) was needed. Since in

the meantime most telecommunication systems

are digital, this kind of equipment is not needed

any more: The call and device concerning data

are stored in the memory of the systemprocessor. It is only a question of software to read

them out.

The traffic measurements are usually part of the

so called Performance Data Measurements.

Performance Data Measurementscan be run

continuously, periodically or sporadically, for a long

time or a short time, observing smaller or greaterparts of the network.

Concerning the traffic measurements, either special

events are counted (e.g. the number of successful

calls, the number of lost calls, ...) or special time

intervals are recorded (e.g. holding times, waiting

times,...).

The corresponding counters could in principle be

actualised continuously during the observationperiod, but mostly a scanning method is used.

Scanning methodmeans that the system counts the

number of events not continuously but only at

particular times. This leads to some uncertainty for

the measurement results. Nevertheless, the error

performed can be estimated using statistical

methods. In general, the smaller the scanning

interval the higher the precision of the measurement.Typical scanning intervals are 100 ms or 500 ms.

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Input Requirements: Traffic & Coverage

Traffic: No. of Subscriber & Service Types

Number of subscribers for each service type

Forecasts of new applications and which service type they will use

Availabilityof service types / quality of service in different network areas

Voice: 12kbps

in whole network

Data: 64kbps

in suburban areas

Data: 144kbps

in urban areas

Data: 384kbps

in business areas

Data: 2048kbps

In-door, buildings

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Input Requirements: Traffic

Traffic

busy hour traffic per subscriber for different bit rates

Voice:

- Bit Rate

- Voice activity: Erlang /

subscriberduring busy hour

Real time (RT) data:

- Bit rates for services

- Erlang /subscriber

during busy hour

Non real time (NRT) data:

- Target bit rates

- Mean Throughput in kbps /

subscriberduring busy hour

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Input Requirements: Traffic & Coverage

Traffic Distribution:

Time Dependency

0 12 24 hours0 %

100 %

50 %

Districts:polygons

Cluttertypes

Distribution of Traffic: predicted using clutter & districtsDistribution of traffic depends on:

Districts: polygons with statistics on population, business,...

Clutter typesfor traffic distribution within districts

(DU, U, SU, Rural, dense forest, open area, water)

Traffic Distribution:

Location Dependency

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TECHCOMConsult ing


Traff ic Distr ibution: Time Dependency

The traffic in a telecommunication network as a

function of time will not be constant but will showsignificant fluctuations. Variations of the traffic

during a single day, from day to day, for different

weekdays, or even for different seasons can be

observed. Also on a long time scale the averaged

traffic will not remain constant but will increase in

most telecommunication networks.

Traff ic Distr ibution : Location Dependency

The traffic in a telecommunication network

will not be location independent but will showsignificant location dependencies.

For example, in rural areas there will be less traffic

compared to city areas.

Distr ibution of Traff ic

Distribution of traffic depends on:

Distr icts: polygons with statistics on population,

business,...Clutter types for traffic distribution within districts

(e.g. dense urban DU, urban U, suburban SU, rural

R, dense forest, open area, water)Traffic per cell is predicted using clutter anddistricts

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Input Requirements: Traffic

Traffic

Distribution of traffic, not only planning of traffic in cell

Low interference High interference for neighbor cells

Increase of capacity needs dueto soft handover

Calculation of Eb/No in map

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Input Requirements: Quality of Service QoS

Quality of Service:

Service typesdepend on

Throughputrate and delay

Traffic classes(depending on sensitivity to delay):

Conversational Class

Streaming Class

Interactive ClassBackground Class

Blockingsystem (blocking probability)

Queuingsystem (user throughput)

Coverage for different service typescan be calculated by

- Margins

- Cell probabilities

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Quality of Service: Margins

Load Factor of cell:

Required Eb/No

User bit rate

Other / own cell interference i (soft blocking)

Orthogonality of codes (DL)

Coverage probability:

Cell edgeprobability

Cell areaprobabilityLog-normal fading margin(based on 1 measurement &

required probabilities)

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Quality of Service: Cell edge /cell area probabilities

The propagation conditions of electromagnetic waves in real environments are not stable, but

location (& time) dependent fluctuationsappear.

The radio network planner has to take this into account by working with probabilities, e.g. with the

coverage probability:

Cell edge probabilityCell area probability

Typical cell edge probabilitiesfor:

Very good coverage: 95%

Good coverage: 90%

Acceptable coverage: 75%

As will be discussed later, these values correspond to the following cell area probabilities:

Very good coverage: 99%

Good coverage: 97%

Acceptable coverage: 91%

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Quality of Service:Area location probability (coverage probability)

Outdoor coverage,

Indoor coverage,

In car coverage

95 % Indoor for low rate

90 % Indoor for high rate

90 % in car

Location probability has big influence on amount of sites

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Quality of Service:

Blocking probabilityfor real time services (circuit switched)

End user throughput(packet switched)

Dependent on

supported data rates

propagation conditions

T ffi Th E l Th

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Traffic Theory: Erlang Theory

Traffic Offered Traffic Carried

Traffic Lost

Telephone system:::KK

KK

KKKK

KK

KK

KK

L

JJJJ

JJJJ

JJJJ

KKKKKK

JJ

JJ

JJ

pure Loss System

pure Queuing System

(combined) Loss & Queuing System

Blocking probabilityB

A n

n= number of

Trunks

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TECHCOMConsult ing


Traff ic f low units :

In honour of A. K. Erlang (1878-1929),a Danishmathematician who was the founder of traffic

theory, the unit of the traffic flow (or traffic

intensity) is called Erlang (Erl).

The traffic flow is a measure of the size of the

traffic. Although the traffic flow is a dimensionless

quantity, the Erlang was assigned as unit of the

traffic flow in traffic theory.

By definition:

1 trunk occu pied for a duration t of a

consid ered period T carries t / T Erlang.

From this definition it follows already that the

traffic carried in Erlang cannot exceed the number

of trunks.

Several definitions can be given for the traffic flow:

Especially for traffic measurements it is useful toconsider the traffic flow as averaged number of

trunks which are occupied (busy) during a specified

time period:

Traff ic intensity= Mean number of busy trunks in a

time period

If this is a long time period, ongoing calls at thebeginning and at the end of this period can be

neglected. The traffic flow then can be considered as

call intensity (number of trunk occupations per time

unit) times the mean holding time (which is the

average holding time per trunk occupation):

Traff ic intensity= Call intensity x Mean holding

time

T ffi Th E l B F l

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Traffic Theory: Erlang B Formula

Assumptions:

Pure loss system

Infinite number of traffic sources

Finite number of devices (trunks) n

Full availability of all trunks

Exponentially distributed holding times

Constant call intensity, independent of the number of occupations

Time and call congestion are equal:

This formula is called Erlang`s formula of the first kind(or also Erlang loss formulaor Erlang

B formula).

n

i

i

n

n

i

A

n

A

AEBE

0

,1

!

!)(n: number of trunks

E = B = Blocking rate (%)

A: Attempt / offered traffic

N ti d dditi l k

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TECHCOMConsult ing


The Erlang B form ula

describes the congestion as function of the Traffic

Offered and the number of available trunks A.In real life the situation is mostly different. People

often want to calculate the number of needed

trunks nfor a certain amount of traffic offered and

a maximum defined congestion / blocking rate B.

That means the Erlang B formula must be

rearranged:

n = function o f (B andA)

This rearrangement cannot be done analytically

but only numerically and will be performed most

easily with the help of a computer. Another

possibility is the usage of special tables, namely

so called Erlang B look-up tables.

On the following page an example of such an Erlang

B look-up table is presented.

Traffic Theory: Erlang B Look up Table

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Traffic Theory: Erlang-B Look-up Table

Number of

trunks n

Offered Traffic A

forB=E=0.01

(1 % blocking)

Offered Traffic A

forB=E=0.03

(3 % blocking)

Offered Traffic A

forB=E=0.05

5 % blocking)

Offered Traffic A

forB=E=0.07

7 % blocking)

1

2

3

4

5

6

7

89

10

11

12

13

14

15

16

17

18

19

2021

22

23

24

25

0.01

0.15

0.46

0.87

1.36

1.91

2.50

3.133.78

4.46

5.16

5.88

6.61

7.35

8.11

8.88

9.65

10.44

11.23

12.0312.84

13.65

14.47

15.29

16.13

0.03

0.28

0.72

1.26

1.88

2.54

3.25

3.994.75

5.53

6.33

7.14

7.97

8.80

9.65

10.51

11.37

12.24

13.11

14.0014.89

15.78

16.68

17.58

18.48

0.05

0.38

0.90

1.53

2.22

2.96

3.74

4.545.37

6.22

7.08

7.95

8.84

9.37

10.63

11.54

12.46

13.39

14.31

15.2516.19

17.13

18.08

19.03

19.99

0.08

0.47

1.06

1.75

2.50

3.30

4.14

5.005.88

6.78

7.69

8.61

9.54

10.48

11.43

12.39

13.35

14.32

15.29

16.2717.25

18.24

19.23

20.22

21.21

Erlang B look-up table for an infinite number of traffic sources and full availability:

Traffic Theory: Erlang B Look up Table

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Traffic Theory: Erlang-B Look-up Table

Number of

trunks n

Offered Traffic A

for

B=E=0.01

(1 % blocking)

Offered Traffic A

for

B=E=0.03

(3 % blocking)

Offered Traffic A

for

B=E=0.05

5 % blocking)

Offered Traffic A

for

B=E=0.07

7 % blocking)

26

27

28

29

30

31

32

33

34

3536

37

38

39

40

41

42

43

44

45

4647

48

49

50

16.96

17.80

18.64

19.49

20.34

21.19

22.05

22.91

23.77

24.6425.51

26.38

27.25

28.13

29.01

29.89

30.77

31.66

32.54

33.43

34.3235.22

36.11

37.00

37.90

19.39

20.31

21.22

22.14

23.06

23.99

24.91

25.84

26.78

27.7128.65

29.59

30.53

31.47

32.41

33.36

34.30

35.25

36.20

37.17

38.1139.06

40.02

40.98

41.93

20.94

21.90

22.87

23.83

24.80

25.77

26.75

27.72

28.70

29.6830.66

31.64

32.62

33.61

34.60

35.58

36.57

37.57

38.56

39.55

40.5441.54

42.54

43.53

44.53

22.21

23.21

24.22

25.22

26.23

27.24

28.25

29.26

30.28

31.2932.31

33.33

34.35

35.37

36.40

37.42

38.45

39.47

40.50

41.53

42.5643.59

44.62

45.65

46.69

Exercise: Trunking Gain

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Exercise: Trunking Gain

Exercise: Use the Erlang Blook-up table to find out the meaning of trunking gain:

a)Which traffic offered can be handled by an Erlang B system assuming 32 trunks

and 1 % blocking?

b)Which traffic offered can be handled by 2 Erlang B systems for each assuming

16 trunksand 1 % blocking?

c)Which traffic offered can be handled by 4 Erlang B systems for each of them

assuming 8 trunksand 1 % blocking?

A = f(B,n)

a)A = 1 x f(1%, 32) = 22.05

b)A = 2 x f(1%, 16) = 17.76c)A = 4 x f(1%, 8) = 12.52

22.05

n: number of trunks

E = B= Blocking rate (%)

A: Attempt / offered traffic

Input Requirements: QoS

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Quality of Service: UE classes


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Input Requirements: Capacity

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Input Requirements: Capacity

Capacity:

Forecast of subscriber penetration rate

Maps about traffic density

Available frequency spectrum

Frequency [MHz]

1900 1920 1980 2010 2025 2110 2170

UMTS FDD (UL) UMTS FDD (DL)UMTS

TDD

UMTS

TDD

Licensed frequencies out of defined UMTS frequency band:

2 x 60 MHz paired band (FDD)

35 MHz unpaired (TDD)

Bandwidth: 5 MHz

UMTS Forum: min. 2x15 MHz + 1x5 MHz / operator

Propagation Models

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Propagation Models

Dimension

Radio Link Budget

UL & DL Link Budget

Eb/No & Processing Gain Power Control Headroom

Soft Handover Gain

Processing Gain



Propagation Models

Propagation Models

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Propagation Models

Coverage:

Coverage regions:

coverage areas may differ for different roll-out phase

Information about Area Types:

different clutter types, e.g.: dense urban, urban, suburban, rural,

dense forest, open area, water

Propagation conditions:

Path loss calculationusing standard Propagation Models

Correction factorsfor propagation models

Fading margins

....


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TECHCOMConsult ing


Radio wave propagation:

The radio wave propagation is described by

solutions of the Maxwell equations.

Exact solutions of the Maxwell equations are not

accessible for real space environment with

obstacles which give rise to reflections and

diffractions.

However, the full information provided by an exact

solution (e.g. exact polarization and phase of thefield strength) is mostly not needed.

What is needed is the the received power level.

What a Propagation Modelshould provide is the

attenuation of the power leveldue to the fact that the

signal propagates from the transmitter to thereceiver.

Radio Wave Propagation Models

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p g

Empirical models

Log distance path loss

COST Hata

Semi empirical models

COST Hata & knife edge

COST Walfish Ikegami

Deterministic models

Ray launching, ray tracing


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TECHCOMConsult ing


Empiric al & determinist ic models:

Empiric al mod elsare based on measurements.

Some empirical models (like the ITU model) are

curves derived from measurements. Others

summarize the measurements in formulas (like

the Okumura-Hata model) which fit the measured

data.

Such models are very simple to handle but also

usually rather imprecise. They are limited to

environments similar to the one where the

measurements were performed.

Determin ist ic modelsare based on simplifying

assumption for the general problem. This can be

a mathematical approximation of the original

problem (like the finite difference model). Or it can

be a simple model for a special situation of the

general problem (like the knife edge model).

Deterministic model can reach a very high precision,

but they suffer from a very high complexity.

Semi empirical modelsare a combination of

empirical models with deterministic models for

special situations (like knife edge models; Walfish-

Ikegami).

Propagation Models

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g

Received power:

PT

: Transmitted power

PR: Received power

nTRd

cPP

)lg()lg()lg(lg dAdncLP

P

T

R

101010Path loss L:

d: distance

n

T

R

dcP

P

0

0.2

0.4

0.6

0.8

1.0

2.5 5.0 7.5 10.0

n = Path Loss Exponent

c: constant

d: Distance [km]

Propagation Models

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0.0001

0.001

0.01

0.1

1

1 2 5 10

n=4

n=3

n=2

0

0.2

0.4

0.6

0.8

1.0

2.5 5.0 7.5 10.0

n=4

n=3

n=2

Received power level

as function of distance d

on linear scale.

nR d

P 1

Received power level

as function of distance d

on log scale.

nR d

P 1

Propagation Models

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2

4

d

PR

Example: Free space propagation (n = 2)

: wavelength in vacuum; , speed of light in vacuum

f : frequency in MHz

d : distance in km

The influence of the surface is neglected completely!

f

c

smc

81099792 .

dfL lglg. 20204432

Propagation Models

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n

R

d

dP

0

d0: reference distance 1kmfor macro cells or in the range of 1m - 100mfor micro cells;

should be always in the far field of the antennaL d0: reference path loss; to be measured at the reference distance.

Environment Exponent n

Free space 2

Urban area 2.7-3.5

Shadowed urban area 3-5

Obstructed in building 4-6Obstructed in factories 2-3

Log-distancepath loss model:

0

100

d

dnLL

d lg

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Propagation Models

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COST Hatamodel: clutter correction term c

suburban areas

rural areas

city center

The major difference between the Okumura Hata model is a modified dependence onfrequency and additional correction term for inner city areas

Both models, the Okumura Hata model and the COST Hata model can lead locally to substantial

deviation from the measured attenuation since these models are isotropic. Local properties of the

surface (big buildings, hills etc.) are not taken into account.

94.40)lg(33.18lg78.4

4.528

lg2

3

2

2

ffc

fc

c

Propagation Models

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Example:

For f = 1950MHz, hBS = 30m, hMS = 1,5mthe correction term for the dependence on hMS

can again be neglected. For the other terms of COST Hata model the insertion of the values

serves:

)lg(22.354.137 dcL

urban

COST Hata model:

Propagation Models

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COST Walfisch Ikegami model:

For a better accuracy in urban areasbuilding height and street width have to be taken into

account, at least as statistical parameters. Based on the Walfisch Bertoni propagation model forBS antennas place above the roof tops, the semi empirical COST Walfisch Ikegami model is a

generalisation including BS antennas placed below the roof tops.

Parameter range for this model:

Frequency f = 800 2000MHz

Height base station hBS= 4 50m

Height Mobile station hMS = 1 3m

Distance d = 0.02 5km

Further parameter:

Mean building height: hin m

Mean street width: win m

Mean building spacing: bin m

Mean angle between propagation path and street: in

Propagation Models

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b w

dBS

UE

hhBS

hMS


BS

UE

w: Mean street width: [m]

b: Mean building spacing [m]

h: Mean building height [m]

: Mean angle between propagation path & street [

Propagation Models

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With LOSbetween BS and UE:

)lg()lg(. dfLLOS

2620642

With non LOS:

,

,

0

0

L

LLL

L

msdrts

NLOS

0

0

msdrts

msdrts

LL

LL

free space propagation:

rtsL roof top to street diffraction and scatter loss:

00

00

0

9055

5535

350

OL

)lg()lg(. dfLO 20204432

LOS: Line-Of-Sight

,..

,..

,.

)lg()lg()lg(.

114004

075052

354010

201010916MSrts

hhfwL

Propagation Models

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msdL multiscreen diffraction loss:

)lg()lg()lg( bfkdkkLL fdamsdmsd 91

hhBS

,.

,.

,

,

,.

)(.

),(.

,

,

),lg(

1925

704

1925704

1518

18

508054

8054

54

0

1181

f

f

k

h

hhk

dhh

hhk

hhL

f

BSd

BS

BSa

BS

msd

hhBS

hhBS

hhBS

hhBS

hhBS

hhBS 50.d

and

and

50.d

Medium sized cities and suburban centres

Metropolitan centres


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TECHCOMConsult ingCOST Walf isch Ikegami model:

Although also designed for BS antennas placed

below the mean building height the COSTWalfisch Ikegami model show often considerable

inaccuracies.

This is especially true in cities with an irregular

building pattern like in historical grown cities. Also

the model was designed for cities on a flat

ground. Thus for cities in a hilly environment the

model is not applicable.

Propagation Models

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Diffraction knife edge model:

Diffraction models apply for configurations where a large obstacle is in the propagation pathand

the obstacle is far away from the transmitter and the receiver, i.e.: and 21 ddh ,h

The obstacleis represented as an ideal conducting half plane (knife edge)

hMShBSd1

h

d2

Huygens secondary source

Propagation Models

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Huygens principle: all points of a wave front can be considered as a source for a

secondary waveletsum up the contributions of all wavelets starting in the half plane above the obstacle

Phase differences have to be taken into account (constructive and destructive interferences)

Difference between the direct path and the diffracted path,

the excess path length

Phase difference: with Fresnel Kirchhoff diffraction parameter.

Note: this derivation is also valid for

21

212

2 dd

ddh

2

2

2

21

212

dd

ddh

0h

Propagation Models

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Diffraction loss:

du

uii

E

EL

D

D

22

1

2020

2

0explglg)(

0E

DE

field strength obtained by free field propagation without diffraction (and ground effects).

diffracted field strength

Shadow border region:

)lg(.)(

20513

0D

L,

,

0

0

LOS region,

shadowed region

0h

The following mathematical approximations exist:

600

)(DL

LD: additional pathloss (diffraction loss)

Propagation Models

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Semi empirical models:

Semi empirical modelscombinedeterministic modelslike knife edgemodels with empirical

modelslike COST Hata.

The mentioned empirical models are only valid for a quasi flat surface. In combination with knife

edge models they can be extended to hilly surface or a mountain area.

The combination of empirical and deterministic models requires usually additional correction terms.

For the specific combination of models and their correction terms most user develop their own

solution which they calibrate with their measurements. .

Propagation Models

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Deterministic models:

Ray tracing and ray launching:

With the methods of geometrical optics all possible propagation paths from the transmitter

to the receiver are determined and summed up, i.e. there is a free space propagation from the

antenna to the first obstacle or from obstacle to obstacle and at the obstacle the ray is reflected or

diffracted until it reaches the antenna. The algorithm takes only rays with an adjustable maximum

number of reflections and diffractions.

With this method a very high precisionfor the prediction of the path loss can be obtained.

For this method a digital map with high accuracyis required.

For the reflection and diffraction attenuation factorshave to be specified which depend on the

building surface (e.g. glass or brick wall).

The algorithms are very complexand computer power consuming.

However, there are continuous improvements for hardware, software and algorithms.

Propagation Models

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Propagation model rural urban in-house

Log-distance path loss + 0 +

COST Hata + 0 -

COST Hata & knife edge + 0 -

COST Walfisch Ikegami - + -

Ray launching / Ray tracing 0 + +

Summary of the application areas of the different models:

Suitable prediction models for

Macro-, Micro-, and Pico- cells

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Dimensioning

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Input Requirements: Traffic, Quality of Service, Capacity, Coverage Propagation Models

Dimension

Radio Link Budget

UL & DL Link Budget



Soft Handover Gain

Processing Gain



Dimensioning

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Initial Cell Count

PopulationVoice

PenetrationTarget

Data

PenetrationTarget

Voice Traffic

/Subscriber

Data Traffic

/Subscriber

Average speech rate Average data rate

Offered Traffic

RF Capacity

EstimationCapacity / CellUL budget Cell Range

Initial Cell Count

Dimensioning

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Cell Range Calculation: Evaluation of cell range:- Maximum loadof system

- Link budget

for subscriber at cell edge

Cell loading>,< or =

max. allowed

system load

=

Cell Range

Calculation of cell loadingusing

traffic profileand cell range

Using coverage limited cell range

Add carrier or

decrease cell radius

> < Decreasemaximum

system loadCapacity

limitation

Coverage

limitation

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Link Budget

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Input Requirements: Traffic, Quality of Service, Capacity, Coverage Propagation Models

Dimension

Radio Link Budget

UL & DL Link Budget



Soft Handover Gain

Processing Gain



Link Budgets

Before dimensioning the radio network the link budget for different environments (indoor outdoor

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Before dimensioning the radio network, the link budget for different environments (indoor, outdoor,

in-car) must be considered.

From the link budget, the maximum allowable path losscan be derived.

Body Loss

Building (indoor)

penetration loss

Path Loss L

Losses/Margins: e.g.

(Fading) Margins

Gains: e.g.Soft Handover Gain,

Antenna Gain

Cable Losses Node B

Noise

figure

Tx

PowerRx Noise

Power

Tx Power+ Gains

Losses/Margins

Path Loss

Rx Noise Power

max. Path Loss L

Link Budgets

Power Level

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UE

Transmit

Power

UE Antenna gain

Feeder Losses

Body Loss +

Building Penetration Loss

Soft HOV gain

PC Headroom +Interference Margin +

Fading margin

Path Loss

L

Processing

Gain

BTS antenna

gain

Feeder Losses +

Combiner Losses...

Required Eb/No

Power Level

Receiver

Noise

Power

Link Budgets

Terms which enter the link budget:

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Transmitter

Maximum output power [dBm]

Feeder loss [dB]

Antenna gain [dBi]

EIRP [dBm]

DL Peak to Average Ratio [dB]

Receiver

Thermal Noise Density [dBm/Hz]

Receiver Noise Figure [dB]

Receiver Noise Density [dBm/Hz]

Receiver Noise Power [dBm]

Required Eb/No [dB]

Required Ec/Io [dB]

Antenna Gain [dBi]

Feeder Losses [dB]

Required Signal Power [dBm]

Isotropic Power [dBm]

Environment/Service

Processing Gain [dB]

Soft Handover Gain

Power Control Headroom [dB]

Interference Margin [dB]

Log-normal Fading Margin [dB]

Body Loss [dB]

Building (indoor) Penetration Loss [dB]

Path Loss [dB]

g

EIRP: Effective Isotropic Radiated Power

Link Budgets

Example of an UL link budget(UMTS): speech 12.2 kbps, slow moving (3 km/h)

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UE Maximum Output Power 21 dBm (1)

Feeder Losses 0 dB (2)

Antenna Gain 0 dBi (3)

Body Loss 3 dB (4)

EIRP 18 dBm (5) = (1)-(2)+(3)-(4)

Environment/ Soft Handover Gain + MDC Gain 2 + 0 dB (6a)+(6b) (urban, 90% cell edge probability)

Service Power Control Headroom 3 dB (7)

Processing Gain 25 dB (8)

Interference Margin [dB] 3 dB (9) (50% UL load )

Log-normal Fading Margin 10 dB (10) (urban = 8, 90% cell edge probability 97% cell area probability)

Building (indoor) Penetration Loss 0 dB (11)

Required Eb/No [dB] 4 dB (12)

Required Ec/Io -18 dB (13) = (12) - ( 6b) - (8) + (9)

Node B Thermal Noise Density -174 dBm/Hz (14)

Receiver Noise Figure 6 dB (15)

Receiver Noise Density -168 dBm/Hz (16) = (14) + (15)

Receiver Noise Power -102 dBm (17) = (16) + 10 x log10(3.84x106)

Feeder Losses 3 dB (18)Antenna Gain 18 dBi (19)

Required Signal Power -120 dBm (20) = (13)+(17)

Isotropic Power -124 dBm (21) = (20)+(18)-(19)-(6a)+(7)+(10)+(11)

Path Loss L 142 dB (5)-(21)

Link Budgets

Example of an DL link budget(UMTS): speech 12.2 kbps, slow moving (3 km/h)

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Node B neccessaryOutput Power 20 dBm (1)



EIRP 35 dBm (5) = (1)-(2)+(3)Environment/ Soft Handover Gain + MDC Gain 2 + 1 dB (6a)+(6b) (urban, 90% cell edge probability)

Power Control Headroom 0 dB (7)

Processing Gain 25 dB (8)

Interference Margin [dB] 6 dB (9) (75% DL load )

Log-normal Fading Margin 10 dB (10) (urban = 8, 90% cell edge probability 97% cell area probability)

Building (indoor) Penetration Loss 0 dB (11)

Required Eb/No [dB] 7 dB (12)

Required Ec/Io -13 dB (13) = (12) - ( 6b) - (8) + (9)

UE Thermal Noise Density -174 dBm/Hz (14)

Receiver Noise Figure 8 dB (15)

Receiver Noise Density -166 dBm/Hz (16) = (14) + (15)

Receiver Noise Power -100 dBm (17) = (16) + 10 x log10(3.84x106)


Body Loss 3 dB (4)


Required Signal Power -113 dBm (20) = (13)+(17)

Isotropic Power -105 dBm (21) = (20)+(18)-(19)-(6a)+(7)+(10)+(11)

DL Peak to Average Ratio 5 dB (22)

Path Loss L 142 dB (5)-(21)+(22) Balanced Link max. DL Path Loss max. UL Path Loss

Link Budget: Required Eb/No

Eb/No definition:

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Eb/No definition:

Eb: Energy per bit, No: total noise/interference of the cell

Eb/No is required to guarantee a minimum link quality.

UL Eb/No:

NoiseII

P

R

W

N

E

otherown

rb

0

W: bandwidth,i.e. chip rate

R: bit rate

Pr: received PowerIown: Interference from own cell

(excluding own signal)

Iother: Interference from other cells

Noise: total noise

: Orthogonality factor

DL Eb/No:

NoiseIIP

R

W

N

E

otherown

rb

10

Processing Gain

Link Budget: Required Eb/No

The higher the spreading factor i e the lower the bit rate the higher is the required Eb/No

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Service Required Eb/No [dB]

DL UL

Speech 12.2 kbps 7 4

Data 64 kbps (RT) 7 2

Data 64 kbps (NRT) 6 2

Data 144 kbps (NRT) 5.5 1.5

Data 384 kbps (NRT) 5 1

The higher the spreading factor,i.e. the lower the bit rate, the higher is the required Eb/No.

In DL interference of own cell reduced due to synchronized orthogonal codes.

Required Eb/No(DL) >required Eb/No(UL)

Eb/Nohas to be calculated for different servicesand concerning the speedof the mobile.

To keep a certain link quality for higher mobile speedthe carrier to interference ratio has to be

increased and therefore also Eb/No has to be increased.

Example:for slow moving mobile (3 km/h):

Source: ITU

Link Budget: Required Ec/Io

E /I i i

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Ec/Io is given as:

Ec/Io= Energy per chip / total power spectral density

UL Ec/Io:

NoiseII

P

N

E

W

R

I

E

otherown

r

o

bc

0

DL Ec/Io:

NoiseII

P

I

E

otherown

rc

0

Ec/Iocan be seen as link performance indicatorfor signals, which contain

no information bits (e.g. CPICH).


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TECHCOMConsult ingRequired Ec/Io can also be w rit ten as:

Required Ec/Io = required Eb/No - processing

gain + interference margin - soft handover gainfrom macro diversity (MDC)

Soft handover gain from macro diversity (MDC) is

only important for DL Ec/Io

The required Ec/Io is needed to give the minimum

carrier to interference ratio for the RF signal

based on the required Eb/No.

Link Budget: WCDMA/UMTS specific terms

Compared to GSM link budget there are some WCDMA specific parameters in the

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Compared to GSM link budget there are some WCDMA specific parametersin the

UMTS Link Budget:

Power Contro l headroom

Soft handover gain

Processing gain

Interference Margin

Soft Handover gain, Interference Margin, Power Control Headroom and Required

Eb/No are parameters, which have to be inserted by the planner.

For UMTS link budgets an isotropic path loss is assumed for calculation.

The link budget must be balanced between UL and DL.

The link budget calculation has to be done for each service / data rate (probably

asymmetric) separately.

The maximum load needs to be defined for dimensioning and calculating link budgets.

Link Budget: fast Power Control & PC Headroom

Gain of fast Power Control

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Gain of fast Power Control

Fast power control compensatesvery effectively fast fading(Rayleigh fading), because of the

quick adjustment of power control settings.

Example (ITU):

Simulation: service 8 kbps speech, FER = 1 %, 10 ms interleaving, PC step size = 1 dB,

ITU Pedestrian A: two-path channel, second tap is very weak

ITU Vehicular A: five-tap channel with WCDMA resolution,

Required Eb/No Slow power control Fast power control

(1.5 kHz)

Gain from fast power

control

ITU Pedestrian A 3 km/h 11.3 dB 5.5 dB 5.8 dB

ITU Vehicular A 3 km/h 8.5 dB 6.7 dB 1.8 dB

ITU Vehicular A 50 km/h 6.8 dB 7.3 dB - 0.5 dB

Link Budget: fast Power Control & PC Headroom

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Limits of fast power control:

PC Headroomor Fast Fading Margin Remark:Slow power control =

no power control in simulations =

correct average power

Gain of fast power control:

less Eb/No necessary (compared to Slow PC)

higher for slow moving mobiles

larger for less multipath diversity (pedestrian)

But:

Link Budget: PC Headroom / Fast Fading Margin

Margin against

Fast Fading

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20

UE moving tocell edge [sec]

0 1 2 3

dBm

-10

0

10

UE Tx

power

If maximum poweris reached increase of frame errors

(quality decrease) Eb/No target increases.

10

0 1 2 3

dB

5

15

Eb/No target

UE moving to

cell edge [sec]

UE Tx

power

Fast Fadingtyp. Value: 25 dB

TECHCOM C lt i


P C t l H d / F t F di M i

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TECHCOMConsult ingPower Control Headroom / Fast Fading Margin

Parameter in link budget to set a margin against

fast fading.Whereas in the cell area the closed loop fast

power control gives a gain especially for slow

moving mobiles, at the edge of the cell the

mobiles cannot achieve this gain because their

maximum output power is not high enough to

follow the fading dips.

Therefore a power control headroom (fastfading margin) is needed for slow moving mobiles.

Slow moving mobile can be the limiting factor of

coverage dimensioning.

Typical values are between 2 dB - 5 dB.

Link Budget: Fading & Fading Margins

Rice fading:dominant path exists

Fast Fading

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p

(usually LOS path)

Rayleigh fading:no dominant path

i.e. a non LOS situation.

Fast Fadingdue tomultipath propagation

compensationby Fast Power Contro

margin due to Fast Fading: PCHeadroom (or Fast Fading Margin).

Slow Fadingdue toshadowingcauses Slowor

Log-Normal Fading Margin

compensation SHO Margin

TECHCOM Consult ing


Fading occurs on different scales due to different b th b bilit f ti f th b l t l f

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TECHCOMConsult ingFading occurs on different scales due to different

causes. Fading appears statistically but different

fading types (Fast Fading & Slow Fading) obey

different probability distributions.Propagation models predict only the average value

of the receive level.

An extra marginhas to be added due the fading

effect.

The common question for all fading effects is: how

big to chose the margin such that the receive level

drops not below a given limit with a specified

probability?

Fast fadingappears due to multipath propagation.

The Rx level is affected by interferences due to

different path lengths in the multipath propagation.

The field strength at the receiver is the vector sum

of the fields corresponding to the different

propagation paths. Usually the fading is described

by the probability function for the absolute value of

the field strength. Fast Fading com pensationis

performed by Fast Power Contro l. Nevertheless, a

margin is needed due to Fast Fading: PC Headroom(or Fast Fading Margin).

Slow fadingdenote the variation of the local mean

signal strength on a longer time scale.

The most important reason for this effect is the

shadowingwhen a mobile moves around (e.g. in a

city).

Measurements have shown that the variation of thereceive level is a normal distribution on a log scalelog normal fading.The fading can be parameterized by adding a zero

mean Gaussian distributed random variable

The has to be determined by measurements.

XdLdL )()(

2

2

22

1

PPPX exp)(

X

Link Budget: Slow Fading

To compute the probability that the receive level exceeds a certain marginthe Gaussian

distribution has to be integrated This leads to the Q function:

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distribution has to be integrated. This leads to the Q function:

)(1)(

2

12

1

2exp

2

1)(

2

zQzQ

zerfdx

xzQ

z

z Q(z) z Q(z) z Q(z) z Q(z)

0.0 0.50000 1.0 0.15866 2.0 0.02275 3.0 0.00135

0.1 0.46017 1.1 0.13567 2.1 0.01786 3.1 0.00097

0.2 0.42074 1.2 0.11507 2.2 0.01390 3.2 0.00069

0.3 0.38209 1.3 0.09680 2.3 0.01072 3.3 0.00048

0.4 0.34458 1.4 0.08076 2.4 0.00820 3.4 0.00034

0.5 0.30854 1.5 0.06681 2.5 0.00621 3.5 0.00023

0.6 0.27425 1.6 0.05480 2.6 0.00466 3.6 0.00016

0.7 0.24196 1.7 0.04457 2.7 0.00347 3.7 0.00011

0.8 0.21186 1.8 0.03593 2.8 0.00256 3.8 0.00007

0.9 0.18406 1.9 0.02872 2.9 0.00187 3.9 0.00005

Q(z): Outage Areaz:Factor for calculation of

lognormal fading margin

Tabulationof the Q function

Link Budget: Slow or Log-Normal Fading

In a shadowing environment, the probability of a certain level as function of the level value follows

G i di t ib ti l ith i l

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a Gaussian distribution on a logarithmic scale.

In general, a Gaussian distributionis described by a mean valueand the standard deviation.

Level [dBm]

Probability

90%

m

Link Budget: Log-Normal Fading

From measurementsthe standard deviation1 sigma (LNF)in a certain environment.

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Typical measurementvalues (outdoor, indoor) are given in the following table:

Environment

LNF(o) LNF(i)

Dense urban

UrbanRural

10 dB

8 dB6 dB

9 dB

9 dB8 dB

Log-Normal Fading & Cell Edge Probability

To achieve a certain cell edge probability,

LNFmust be multiplied with a factor (z)given in the

following table:

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following table:

(Cell edge probability means the probability to have coverage at the border of the cell)

Cell edge probabilityin % Factorzfor calculation of

lognormal fading margin

50

55

60

65

7075

80

85

90

95

96

97

98

99

0.000

0.126

0.253

0.385

0.5240.674

0.842

1.036

1.282

1.645

1.751

1.881

2.054

2.326

Link Budget: Cell Edge & Cell Area Probability

Jakes formulagives a relation for the probability that a certain value Pmat the cell boundaryat

radius R is exceeded and the corresponding probability for the whole cell It is based on)(Pr P

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radius R is exceeded and the corresponding probability for the whole cell. It is based on

the log distance path loss model:

0

0 lg10)()(

d

dndLPdP TR

22

11

21exp)(1

2

1)(Pr

b

aberf

b

abaerfPmcell

)(Pr mcell P

2

)(RPPa Rm

2

)lg(10 enb

Integrating the

Gaussian distributionfunction over the

whole cell area

Delivers

cell area probabilities.

Some examples

are given in

the following table:

Cell edge probabilityin % Cell area probabilityin %

50

75

90

95

77

91

97

99

Link Budget: SHO & MDC Gain

SHO & MDC Gaingain againstSlow Fading

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R

NC

Node B Node BSofter handover

Combining(maximum ratio

combining)

Soft handover

Combining(selection combining)

required Eb/Noisreduced

Soft & Softer Handover gain

cannot be separated

SHO values:

UL: 05 dB; typically: 2 dB.

DL: 2 dB - 5 dB; typically: 2 dB

SHO MDC values:

UL: typically 0 dB(Frame Selection by RNC)

DL: typically 1 dB(MRC Combining by UE)

TECHCOMConsult ing


Soft Handover and Macro Diversity Combin ing UL: Typical values are 0 dB to 5 dB. Typical average

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MDC gain:

Soft or softer handover give a gain against slow

fading / log-normal fadingbecause

the mobile can select a better cell based on

minimal transmit power of UE.

Hard handover algorithm is based on geometrical

distance.

Additionally it gives a macro diversity gain in DL

against fast fading because by

using macro diversity combining the required

Eb/No is reduced.

Measurement of soft handover gain:

Gain in required Eb/No is measured relative to

single link.

Averaging is done over all radio links in the soft

handover area.

yp yp g

value 2 dB.

DL: Typical average value 2 dB - 5 dB.

Link Budget: SHO Gain

Soft handover gain dependencies:

UE I d / d ll f h d i l

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4.76.7?10 (dense urban)75 %

3.75.4?8 (urban)75 %

2.84.0?6 (rural)75 %

Soft handover gain

(50 % correlation)

Soft handover gain

(0 % correlation)

Log-normal

fading margin

Standard deviation

LNF(o)

Cell edge

probability

UE Indoor / outdoor smaller soft handover gain values

On area location probability (cell edge probability)

Standard deviation of the signal for environment (in log-normal fading)

Correlation between diversity paths

Example- Exercise:

Link Budget: Processing Gain

Processing gain (or spreading gain) is a CDMA specific gain because it is achieved

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from spreading the signal over the bandwidth , i.e. the chip rate, respectively.

The processing gain is calculated from:

Bitrate

ChipratePGain 10log10

Due to the chip rate is fixed in one system depending on the bandwidth

the processing gain is dependent on the given bit rates. In UMTS the chip rate 3.84 Mchip/s

Exercise:

Speech 12.2 kbps PGain= ?

Data 144 kbps PGain= ?

Data 384 kbps PGain= ?

PGain= 25 dB

PGain= 14.25 dB

PGain= 10 dB

Link Budget: Processing Gain

Required Eb/No= 4 dB(12.2 kbps)

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Signal

Power

time

Noise

Speech 12.2 kbps

Data 144 kbps

Data 384 kbps

Pgain = 25 dB

Required Eb/No= 1.5 dB(144 kbps)

Pgain = 14.25 dB

Required Eb/No= 1.0 dB(384 kbps)

Pgain = 10 dB

Ec/Io

Link Budget: Interference Margin

The Interference Marginis calculated from the UL and DL load factors:

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110 10LogIMargin

20

10

6

1.25

3

25% 50% 75% 99%

IMargin[dB]

Load factor

typically 25 % -75 %load

can be used in practice.

TECHCOMConsult ing


Interference margin:

This parameter in the link budget considers theThe Interference Margin is calculated from the UL

d DL l d f t

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This parameter in the link budget considers the

load in the cell which influences the coverage.

The more load is in a cell the higher has the

interference margin to be set because every useris an interferer to the others in a CDMA system.

More load leads and therefore higher interference

margin causes a smaller coverage area.

With the interference margin the load dependency

of the Node B sensitivity considered.

In coverage limited scenarios smaller values

(typically 1-3 dB for 20%- 50% loading) are

assumed for the interference margin because thelimitation of the cell size is determined by the

maximum path loss in link budget instead of the

capacity on air interface.

and DL load factors:

Interference Margin = - 10 x log ( 1- Load).

Link Budget: Noise Figurecalculations

Thermal Noise:

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At finite temperatures (T > 0K) every object is moving. E.g. the electrons in a

resistor move and create therefore a noise with a certain power, that can be shown

to be

Pn= kB* T * B, with kB=1.38*10-23J/K, B is the Bandwidth in Hz

Thermal Noise Density:

The thermal noise in a spectrum interval is the thermal noise density:

Pn/ B = kB* T, with kB=1.38*10-23J/K, B is the Bandwidth in Hz

Example:

Pn/ B = kB* T 4.14*10-21J -174 dBm/Hz , with T = 300 K

Pn 1.6*10-16W -108 dBm, with T = 300 K and B = 3.84 MHz

Link Budget: Noise Figure calculations

Receiver Noise Figure

The requirements for the receiver noise figure are set in the specifications for Node B and UE

Fehler

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The requirements for the receiver noise figure are set in the specifications for Node B and UE.

e.g. 6 dB/8 dB

Receiver Noise Density:

The receiver noise density is defined as the sum of thermal noise density and the receiver noise

figure

Receiver Noise Density = Thermal Noise density + Receiver Noise Figure

e.g. Receiver Noise Density = -174 dBm/Hz + 6 dB= -168 dBm/Hz

Rx Noise Power: Receiver Noise Spectral Density or Thermal Noise Floor:

Receiver noise spectral density is the sum of the thermal noise density over the used

bandwidth,i.e. chip rate, and the receiver noise figure

e.g. Receiver Noise Spectral Density = -174 dBm/Hz "* Bandwidth" + 6 dB== -174 dBm + 10 * log10(3.84*10

6) + 6 dB= -174 dBm + 66 dB+ 6 dB= -102 dBm

Link Budget: DL Peak to Average Ratio

DL Peak to Average Ratioor Isotropic Path Loss IPL CorrectionFactor: The correction

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factor is needed because not all mobiles are at the center or at the edge of the cell. It is defined

as ratio between the maximum path loss and the average path loss. A maximum path loss will

occur if the mobile is at the cell edge and the the Node B transmits to this UE. This ratio is

calculated using a simulation for typical UE distributionsthroughout the cell depending on the

used service.

highest ratio smaller ratio



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Propagation Models

Dimension

Radio Link Budget

UL & DL Link Budget


Power Control Headroom Soft Handover Gain

Processing Gain




Definition for WCDMA systems coverage efficiency:

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Coverage efficiency = coverage Area/site [km2/site]

depending on

propagation environment

allowed traffic density (maximum allowable path loss)

propagation environment:

Cell range calculation:

using standard propagation models (e.g. COST-Hata, Walfish-Ikegami):

Maximum allowable path loss maximum cell range

Cell Range & Coverage Area Calculation

d d

If cell range is known coverage area CAcan be

calculated

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d

d

d

dor

CA= Sd2

Sis a constant depending on the site configuration:

Omni or 6 sector cell: 2.6

2 sector cell : 1.6

3 sector cell : 1.95

The more sectors the more soft handover overhead has

to be regarded for estimation.

Best coverage efficiency does not mean also bestcapacity efficiency!

Cell Range Calculation: Exercise

Exercise:

Given:d

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f = 1950 MHz,

hBS = 30 m,

hMS = 1.5 m

Calculate themaximum cell range dfor a dense urban environmentand

the following two services:

a) Speech (12.2 kbps)

b) Data (144 kbps)

Hint:

- For non specified valuestake the values from the link budget given above.

- Use COST Hata (simplified).

Solution - Cell Range Calculation

5569448213933346 )lg()lg(..)()lg(.)lg(.. dhchdhfLBSMSBSurban

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805617011 .)lg(..)lg(.)( fhfhdMSMS

)lg(22.354.137 dcLurban

1. Speech (12.2 kbps)

Path Loss (Example) L = 142 dB

lg (d) = (142137.43) / 35.22 = 3.6 / 35.22 = 0.1022

d = 1.11 km

2. Data (144 kbps) L = 136.76 dB

no Body Loss; GP=14.25; Eb/No = 1.5; with SHO Gain:

lg (d) = (136.76137.43) / 35.22 d = 0.79 km

Parameter:

f = 1950 MHz,

hBS = 30m,

hMS = 1.5m

Eb/No (144 kbps) 1.5 dB

Dense Urban c = -3

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04_3560_6220_Link Budget_E06

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