July, 19984 - 1RF100 (c) 1998 Scott Baxter Physical Principles of Propagation Chapter 4 Section A

July, 1998 4 - 1RF100 (c) 1998 Scott Baxter

Physical Principles of Propagation

Physical Principles of Propagation

Chapter 4 Section A


Introduction to Propagation

Propagation is the heart of every radio link. During propagation, many processes act on the radio signal.

• attenuation– the signal amplitude is reduced by various natural mechanisms. If there

is too much attenuation, the signal will fall below the reliable detection threshold at the receiver. Attenuation is the most important single factor in propagation.

• multipath and group delay distortions– the signal diffracts and reflects off irregularly shaped objects, producing a

host of components which arrive in random timings and random RF phases at the receiver. This blurs pulses and also produces intermittent signal cancellation and reinforcement. These effects are overcome through a variety of special techniques

• time variability - signal strength and quality varies with time, often dramatically• space variability - signal strength and quality varies with location and distance• frequency variability - signal strength and quality differs on different

frequencies To master propagation and effectively design wireless systems, you must know:

• Physics: understand the basic propagation processes • Measurement: obtain data on propagation behavior in area of interest• Statistics: analyze known data, extrapolate to predict the unknown• Modelmaking: formalize all the above into useful models


Frequency and Wavelength: Implications

Radio signals in the atmosphere propagate at almost speed of light

= wavelength

C = distance propagated in 1 second

F = frequency, Hertz The wavelength of a radio signal

determines many of its propagation characteristics

• Antenna elements size are typically in the order of 1/4 to 1/2 wavelength

• Objects bigger than a wavelength can reflect or obstruct RF energy

• RF energy can penetrate into a building or vehicle if they have apertures a wavelength in size, or larger

/2

C / F

for AMPS: F= 870 MHz

0.345 m = 13.6 inches

for PCS-1900: F = 1960 MHz

0.153 m = 6.0 inches


Propagation Effects of Earth’s Atmosphere

Earth’s unique atmosphere supports life (ours included) and also introduces many propagation effects -- some useful, some troublesome

Skywave Propagation: reflection from Ionized Layers

• LF and HF frequencies (below roughly 50 MHz.) are routinely reflected off layers of the upper atmosphere which become ionized by the sun

• this phenomena produces intermittent world-wide propagation and occasional total outages

• this phenomena is strongly correlated with frequency, day/night cycles, variations in earth’s magnetic field, 11-year sunspot cycle

• these effects are negligible for wireless systems at their much-higher frequencies


More Atmospheric Propagation Effects

Attenuation at Microwave Frequencies

• rain droplets can substantially attenuate RF signals whose wavelengths are comparable to, or smaller than, droplet size

• rain attenuations of 20 dB. or more per km. are possible

• troublesome mainly above 10 GHz., and in tropical areas

• must be considered in reliability calculations during path design

• not major factor in wireless systems propagation Diffraction, Wave Bending, Ducting

• signals 50-2000 MHz. can be bent or reflected at boundaries of different air density or humidity

• phenomena: very sporadic unexpected long-distance propagation beyond the horizon. May last minutes or hours

• can occur in wireless systems

Refraction by air layers

Ducting by air layers

>100 mi.

“Rain Fades” onMIcrowave Links


Dominant Mechanisms of Mobile Propagation

Most propagation in the mobile environment is dominated by these three mechanisms:

Free space

• No reflections, no obstructions

– first Fresnel Zone clear

• Signal spreading is only mechanism• Signal decays 20 dB/decade

Reflection

• Reflected wave 180out of phase

• Reflected wave not attenuated much

• Signal decays 30-40 dB/decade Knife-edge diffraction

• Direct path is blocked by obstruction

• Additional loss is introduced

• Formulae available for simple cases We’ll explore each of these further...

Knife-edge Diffraction

Reflection with partial cancellation

B

A

d

D

Free Space


Free-Space Propagation

The simplest propagation mode• Antenna radiates energy which spreads in space• Path Loss, db (between two isotropic antennas)

= 36.58 +20*Log10(FMHZ)+20Log10(DistMILES )• Path Loss, db (between two dipole antennas)

= 32.26 +20*Log10(FMHZ)+20Log10(DistMILES )

• Notice the rate of signal decay:• 6 db per octave of distance change, which is

20 db per decade of distance change Free-Space propagation is applicable if:

• there is only one signal path (no reflections)• the path is unobstructed (i.e., first Fresnel zone is

not penetrated by obstacles)

First Fresnel Zone ={Points P where AP + PB - AB < }Fresnel Zone radius d = 1/2 (D)^(1/2)

1st Fresnel Zone

B

A

d

D

Free Space “Spreading” Loss energy intercepted by receiving antenna is proportional to 1/r2

r


Reflection With Partial Cancellation

Mobile environment characteristics:

• Small angles of incidence and reflection

• Reflection is unattenuated (reflection coefficient =1)

• Reflection causes phase shift of 180 degrees Analysis

• Physics of the reflection cancellation predicts signal decay of 40 dB per decade of distance

Heights Exaggerated for Clarity

HTFT HTFT

DMILES

Comparison of Free-Space and Reflection Propagation ModesAssumptions: Flat earth, TX ERP = 50 dBm, @ 1950 MHz. Base Ht = 200 ft, Mobile Ht = 5 ft.

Received Signal in Free Space, DBM

Received Signal inReflection Mode

DistanceMILES

-52.4

-69.0

1

-58.4

-79.2

2

-64.4

-89.5

4

-67.9

-95.4

6

-70.4

-99.7

8

-72.4

-103.0

10

-75.9

-109.0

15

-78.4

-113.2

20

Path Loss [dB ]= 172 + 34 x Log (DMiles )- 20 x Log (Base Ant. HtFeet)

- 10 x Log (Mobile Ant. HtFeet)

SCALE PERSPECTIVE


Signal Decay Rates in Various Environments

We’ve seen how the signal decays with distance in two basic modes of propagation:

Free-Space

• 20 dB per decade of distance

• 6 db per octave of distance Reflection Cancellation

• 40 dB per decade of distance

• 12 db per octave of distance Real-life wireless propagation

decay rates are typically somewhere between 30 and 40 dB per decade of distance

Signal Level vs. Distance

-40

-30

-20

-10

0

Distance, Miles1 3.16 102 5 7 86

One Octave of distance

(2x)

One Decadeof distance (10x)


Knife-Edge Diffraction Sometimes a single well-defined

obstruction blocks the path, introducing additional loss. This calculation is fairly easy and can be used as a manual tool to estimate the effects of individual obstructions.

First calculate the diffraction parameter from the geometry of the path

Next consult the table to obtain the obstruction loss in db

Add this loss to the otherwise-determined path loss to obtain the total path loss.

Other losses such as free space and reflection cancellation still apply, but computed independently for the path as if the obstruction did not exist

H

R1 R2

attendB

0

-5

-10

-15

-20

-25

-4 -3 -2 -1 0 1 2 3-5

= -H2

1 1

R1 R2


Local Variability: Multipath Effects

The free-space, reflection, and diffraction mechanisms described earlier explain signal level variations on a large scale, but other mechanisms introduce small-scale local fading

Slow Fading occurs as the user moves over hundreds of wavelengths due to shadowing by local obstructions

Rapid Fading occurs as signals received from many paths drift into and out of phase

• the fades are roughly /2 apart in space:

7 inches apart at 800 MHz., 3 inches apart at 1900 MHz

• fades also appear in the frequency domain and time domain

• fades are typically 10-15 db deep, occasionally deeper

• Rayleigh distribution is a good model for these fades

these fades are often called “Rayleigh fades”

Multi-path Propagation

A

d

10-15 dB

Rayleigh Fading


Combating Rayleigh Fading: Space Diversity

Fortunately, Rayleigh fades are very short and last a small percentage of the time

Two antennas separated by several wavelengths will not generally experience fades at the same time

“Space Diversity” can be obtained by using two receiving antennas and switching instant-by-instant to whichever is best

Required separation D for good decorrelation is 10-20

• 12-24 ft. @ 800 MHz.

• 5-10 ft. @ 1900 MHz.

Signal received by Antenna 1


Combined Signal

D


Space Diversity Application Limitations

Space Diversity can be applied only on the receiving end of a link.

Transmitting on two antennas would:• fail to produce diversity, since the

two signals combine to produce only one value of signal level at a given point -- no diversity results.

• produce objectionable nulls in the radiation at some angles

Therefore, space diversity is applied only on the “uplink”, i.e.., reverse path

• there isn’t room for two sufficiently separated antennas on a mobile or handheld



Combined Signal

D


Using Polarization DiversityWhere Space Diversity Isn’t Convenient

Sometimes zoning considerations or aesthetics preclude using separate diversity receive antennas

Dual-polarized antenna pairs within a single radome are becoming popular

• Environmental clutter scatters RF energy into all possible polarizations

• Differently polarized antennas receive signals which fade independently

• In urban environments, this is almost as good as separate space diversity

Antenna pair within one radome can be V-H polarized, or diagonally polarized

• Each individual array has its own independent feedline

• Feedlines connected to BTS diversity inputs in the conventional way; TX duplexing OK

Antenna A

Antenna B

Combined

A B A B

V+Hor\+/


The Reciprocity PrincipleDoes it apply to Wireless?

Between two antennas, on the same exact frequency, path loss is the same in both directions

But things aren’t exactly the same in cellular --

• transmit and receive 45 MHz. apart

• antenna: gain/frequency slope?

• different Rayleigh fades up/downlink

• often, different TX & RX antennas

• RX diversity Notice also the noise/interference

environment may be substantially different at the two ends

So, reciprocity holds only in a general sense for cellular

-148.21 db@ 835.03 MHz

-151.86 db@ 870.03 MHz

-148.21 db@ 870.03 MHz


Propagation ModelsPropagation Models

Chapter 4 Section B


Types Of Propagation Models And Their Uses

Simple Analytical models • Used for understanding and

predicting individual paths and specific obstruction cases

General Area models• Primary drivers: statistical• Used for early system

dimensioning (cell counts, etc.) Point-to-Point models

• Primary drivers: analytical• Used for detailed coverage

analysis and cell planning Local Variability models

• Primary drivers: statistical• Characterizes microscopic level

fluctuations in a given locale, confidence-of-service probability

Simple Analytical• Free space (Friis formula)• Reflection cancellation• Knife-edge diffraction

Area• Okumura-Hata• Euro/Cost-231• Walfisch-Betroni/Ikegami

Point-to-Point• Ray Tracing

- Lee’s Method, others• Tech-Note 101• Longley-Rice, Biby-C

Local Variability• Rayleigh Distribution• Normal Distribution• Joint Probability Techniques

Examples of various model types


General Principles Of Area Models

Area models mimic an average path in a defined area

They’re based on measured data alone, with no consideration of individual path features or physical mechanisms

Typical inputs used by model:• Frequency• Distance from transmitter to

receiver• Actual or effective base

station & mobile heights• Average terrain elevation • Morphology correction loss

(Urban, Suburban, Rural, etc.) Results may be quite different

than observed on individual paths in the area

RSSI, dBm

-120

-110

-100

-90

-80

-70

-60

-50

0 3 6 9 12 15 18 21 24 27 30 33

Distance from Cell Site, km

FieldStrength,dBµV/m

+90

+80

+70

+60

+50

+40

+30

+20

Green Trace shows actual measured signal strengths on a drive test radial, as determined by real-world physics.

Red Trace shows the Okumura-Hata prediction for the same radial. The smooth curve is a good “fit” for real data. However, the signal strength at a specific location on the radial may be much higher or much lower than the simple prediction.


The Okumura Model: General Concept

The Okumura model is based on detailed analysis of exhaustive drive-test measurements made in Tokyo and its suburbs during the late 1960’s and early 1970’s. The collected date included measurements on numerous VHF, UHF, and microwave signal sources, both horizontally and vertically polarized, at a wide range of heights.

The measurements were statistically processed and analyzed with respect to almost every imaginable variable. This analysis was distilled into the curves above, showing a median attenuation relative to free space loss Amu (f,d) and correlation factor Garea (f,area), for BS antenna height ht = 200 m and MS antenna height hr = 3 m.

Okumura has served as the basis for high-level design of many existing wireless systems, and has spawned a number of newer models adapted from its basic concepts and numerical parameters.

Med

ian

Att

enu

atio

n A

(f,d

),

dB

1

2

5

40

70

80

100

100 3000500Frequency f, MHz

10

50

70Urban Area

d,

km

30

850

26

35

100 200 300 500 700 1000 2000 3000Frequency f, (MHz)

5

10

15

20

25

30

Co

rrec

tio

n f

acto

r, G

area

(d

B)

9 dB

850 MHz

Open area

Quasi open area

Suburban area


Structure of the Okumura Model

The Okumura Model uses a combination of terms from basic physical mechanisms and arbitrary factors to fit 1960-1970 Tokyo drive test data

Later researchers (HATA, COST231, others) have expressed Okumura’s curves as formulas and automated the computation

Path Loss [dB] = LFS + Amu(f,d) - G(Hb) - G(Hm) - Garea

Free-Space Path Loss Base Station

Height Gain= 20 x Log (Hb/200)

Mobile StationHeight Gain

= 10 x Log (Hm/3)

Amu(f,d) Additional Median Lossfrom Okumura’s Curves

Med

ian

Att

enu

atio

n A

(f,d

), d

B

1

2

5

40

70

80

100

100 3000500

Frequency f, MHz10

50

70Urban Area

d, k

m

30

850

26

Morphology Gain0 dense urban5 urban10 suburban17 rural

35

100 200 300 500 700 1000 2000 3000Frequency f, (MHz)

5

10

15

20

25

30

Co

rrec

tio

n f

acto

r, G

area

(d

B)

850 MHz

Open area

Quasi open area

Suburban area


The Hata Model: General Concept

The Hata model is an empirical formula for propagation loss derived from Okumura’s model, to facilitate automatic calculation.

The propagation loss in an urban area is presented in a simple general format A + B x log R, where A and B are functions of frequency and antenna height, R is distance between BS and MS antennas

The model is applicable to frequencies 100 MHz-1500 MHz, distances 1-20 km, BS antenna heights 30-200 m, MS antenna heights 1-10 m

The model is simplified due to following limitations:

• Isotropic antennas

• Quasi-smooth (not irregular) terrain

• Urban area propagation loss is presented as the standard formula

• Correction equations are used for other areas Although Hata model does not imply path-specific corrections, it has significant

practical value and provide predictions which are very closely comparable with Okumura’s model


Hata Model General Concept and Formulas

Formulas for median path loss are:(1) - Standard formula for urban areas(2) - For suburban areas(3) - For rural areas

Formulas for MS antenna ht. gain correction factor A(hm)(4) - For a small to medium sizes cities(5) and (6) - For large cities

f - carrier frequency, MHz

hb and hm - BS and MS antenna heights, m

d - distance between BS and MS antennas, km

(1) LHATA (urban) [dB] =69.55 + 26.16 x log ( f ) + [ 44.9 - 6.55 x log ( hb ) ] x log ( d ) -13.82 x log ( hb ) - A ( hm )

(2) LHATA (suburban) [dB] = LHATA (urban) - 2 x [ log ( f/28 ) ]2 - 5.4

(3) LHATA (rural) [dB] =LHATA (urban) - 4.78 x [ log ( f ) ]2 - 18.33 x log ( f ) -40.98

(4) A ( hm ) [dB] = [ 11 x log ( f ) - 0.7 ] x hm - [ 1.56 x log ( f ) - 0.8 ]

(5) A ( hm ) [dB] = 8.29 x [ log ( 1.54 x hm ) ]2 - 1.1 (for f<= 300 MHz.)

(6) A ( hm ) [dB] = 3.2 x [ log ( 1175 x hm ) ]2 - 4.97 (for f > 300 MHz.)

Environmental Factor C0 dense urban-5 urban-10 suburban-17 rural


The COST-231 model was developed by European COoperative for Scientific and Technical Research committee. It extends the HATA model to the 1.8-2 GHz. band in anticipation of PCS use.

COST-231 is applicable for frequencies 1500-2000 MHz, distances 1-20 km, BS antenna heights 30-200 m, MS antenna heights 1-10 m

Parameters and variables:• f is carrier frequency , in MHz• hb and hm are BS and MS antenna heights (m)• d is BS and MS separation, in km• A(hm) is MS antenna height correction factor

(same as in Hata model)• Cm is city size correction factor: Cm=0 dB for

suburbs and Cm=3 dB for metropolitan centers

The EURO COST-231 Model

LCOST (urban) [dB] = 46.3 + 33.9 x log ( f ) + [ 44.9 - 6.55 x log ( hb ) ] x log ( d ) + Cm -13.82 x log ( hb ) - A ( hm )

EnvironmentalFactor C1900 -2 dense urban -5 urban-10 suburban-26 rural


Examples of Morphological Zones Suburban: Mix of

residential and business communities. Structures include 1-2 story houses 50 feet apart and 2-5 story shops and offices.

Urban: Urban residential and office areas (Typical structures are 5-10 story buildings, hotels, hospitals, etc.)

Dense Urban: Dense business districts with skyscrapers (10-20 stories and above) and high-rise apartments

Suburban SuburbanSuburban

UrbanUrbanUrban

Dense Urban Dense UrbanDense Urban

Although zone definitions are arbitrary, the examples and definitions illustrated above are typical of practice in North American PCS designs.


Example Morphological Zones

Rural - Highway: Highways near open farm land, large open spaces, and sparsely populated residential areas. Typical structures are 1-2 story houses, barns, etc.

Rural - In-town: Open farm land, large open spaces, and sparsely populated residential areas. Typical structures are 1-2 story houses, barns, etc.SuburbanSuburban

RuralRural

Suburban

Rural

Rural - HighwayRural - HighwayRural - Highway

Notice how different zones may abruptly adjoin one another. In the case immediately above, farm land (rural) adjoins built-up subdivisions (suburban) -- same terrain, but different land use, penetration requirements, and anticipated traffic densities.


The MSI Planet General Model

Pr - received power (dBm)Pt - transmit ERP (dBm)Hb - base station effective antenna heightHm - mobile station effective antenna heightDL - diffraction loss (dB) K1 - intercept K2 - slopeK3 - correction factor for base station antenna height gainK4 - correction factor for diffraction loss (accounts for clutter heights)K5 - Okumura-Hata correction factor for antenna height and distanceK6 - correction factor for mobile station antenna height gainKc - correction factor due to clutter at mobile station locationKo - correction factor for street orientation

Pr = Pt + K1 + k2 log(d) + k3 log(Hb) + K4 DL + K5 log(Hb) log(d)

+ K6 log (Hm) + Kc + Ko

This is the general model format used in MSI’s popular PlaNET propagation prediction software for wireless systems. It includes terms similar to Okumura-Hata and COST-231 models, along with additional terms to include effects of specific obstructions and clutter on specific paths in the mobile environment.


Typical Model Results Including Environmental Correction

-2-5-10-26

TowerHeight,

m

EIRP(watts)

C,dB

Range,kmf =1900 MHz.

Dense Urban Urban

Suburban Rural

30303050

200200200200

2.523.504.8

10.3

COST-231/Hata

f = 870 MHz.

Okumura/Hata

0-5-10-17

TowerHeight,

m

EIRP(watts)

C,dB

Range,km

Dense Urban Urban

Suburban Rural

30303050

200200200200

4.04.96.7

26.8


Propagation at 1900 MHz. vs. 800 MHz.

Propagation at 1900 MHz. is similar to 800 MHz., but all effects are more pronounced.

• Reflections are more effective

• Shadows from obstructions are deeper

• Foliage absorption is more attenuative

• Penetration into buildings through openings is more effective, but absorbing materials within buildings and their walls attenuate the signal more severely than at 800 MHz.

The net result of all these effects is to increase the “contrast” of hot and cold signal areas throughout a 1900 MHz. system, compared to what would have been obtained at 800 MHz.

Overall, coverage radius of a 1900 MHz. BTS is approximately two-thirds the distance which would be obtained with the same ERP, same antenna height, at 800 MHz.


Walfisch-Betroni/Walfisch-Ikegami Models

Ordinary Okumura-type models do work in this environment, but the Walfisch models attempt to improve accuracy by exploiting the actual propagation mechanisms involved

Path Loss = LFS + LRT + LMS

LFS = free space path loss (Friis formula)

LRT = rooftop diffraction loss

LMS = multiscreen reflection loss Propagation in built-up portions of cities is

dominated by ray diffraction over the tops of buildings and by ray “channeling” through multiple reflections down the street canyons

-20 dBm-30 dBm-40 dBm-50 dBm-60 dBm-70 dBm-80 dBm-90 dBm-100 dBm-110 dBm-120 dBm

Signal Level

Legend

Area View


Statistical TechniquesDistribution Statistics Concept

An area model predicts signal strength Vs. distance over an area

• This is the “median” or most probable signal strength at every distance from the cell

• The actual signal strength at any real location is determined by local physical effects, and will be higher or lower

• It is feasible to measure the observed median signal strength M and standard deviation

• M and can be applied to find probability of receiving an arbitrary signal level at a given distance

Median Signal Strength

,dB

Occurrences

RSSI

Normal Distribution

RSSI, dBm

Distance

Model is tweaked to produce “Best-Fit” curve

Signal Strength Predicted Vs. Observed

Observed Signal Strength

50% of observeddata is above curve

50% of observeddata is below curve


Statistical TechniquesPractical Application Of Distribution Statistics

General Approach:• Use favorite model to predict Signal

Strength• Analyze measured data, obtain:

– median signal strength M(build histogram

of observed vs. measured data)

– standard deviation of error, (determine from

histogram) • add an extra allowance into model

– drop curve so a desired % of observations are above model predictions Median

Signal Strength

,dB

Occurrences

RSSI

Normal Distributio

n

RSSI, dBm

Distance

25% of locations exceed blue curve

50% exceed red

75% exceed black

SIGNAL STRENGTH vs DISTANCE

Min signal req’d for operation

Cell radius for 75% reliability

at edge

Cell radius for 75% reliability

at edgeCell radius for 90% reliability

at edge


Cell Edge Area Availability And Probability Of Service

Overall probability of service is best close to the BTS, and decreases with increasing distance away from BTS

For overall 90% location probability within cell coverage area, probability will be 75% at cell edge

• Result derived theoretically, confirmed in modeling with propagation tools, and observed from measurements

• True if path loss variations are log-normally distributed around predicted median values, as in mobile environment

• 90%/75% is a commonly-used wireless numerical coverage objective

• Recent publications by Nortel’s Dr. Pete Bernardin describe the relationship between area and edge reliability, and the field measurement techniques necessary to demonstrate an arbitrary degree of coverage reliability

Statistical View ofCell Coverage

Area Availability:90% overall within area

75%at edge of area

90%

75%


Application Of Distribution Statistics: Example Let’s design a cell to deliver at least -95

dBm to at least 75% of the locations at the cell edge (This will provide coverage to 90% of total locations within the cell)

Assume that measurements you have made show a 10 dB standard deviation

On the chart:• To serve 75% of locations at the cell

edge , we must deliver a median signal strength which is .675 times stronger than -95 dBm

• Calculate:- 95 dBm + ( .675 x 10 dB ) = - 88 dBm

• So, design for a median signal strength of -88 dBm!Standard Deviations from

Median (Average) Signal Strength

Cumulative Normal Distribution

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

75%

0.675


Statistical Techniques:Normal Distribution Graph & Table For Convenient Reference

Cumulative Normal Distribution

Standard Deviation from Mean Signal Strength

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

CumulativeProbability

0.1%1%5%

10%

StandardDeviation

-3.09-2.32-1.65-1.28-0.84 20%-0.52 30%

0.675 75%

0 50%0.52 70%

0.84 80%1.28 90%1.65 95%2.35 99%3.09 99.9%3.72 99.99%4.27 99.999%


Building PenetrationStatistical Characterization

Statistical techniques are effective against situations that are difficult to characterize analytically

• Many analytical parameters, all highly variable and complex

Building coverage is modeled using existing outdoor path loss plus an additional “building penetration loss”

• Median value estimated/sampled

• Statistical distribution determined

• Standard deviation estimated or measured

• Additional margin allowed in link budget to offset assumed loss

Typical values are shown at left

Building penetration

Typical Penetration Losses, dBcompared to outdoor street level

EnvironmentType

(“morphology”)

MedianLoss,

dB

Std.Dev., dB

Urban Bldg. 15 8

Suburban Bldg. 10 8

Rural Bldg. 10 8

8 4Typical Vehicle

Dense Urban Bldg. 20 8

Vehicle penetration


Composite Probability Of ServiceAdding Multiple Attenuating Mechanisms

For an in-building user, the actual signal level includes regular outdoor path attenuation plus building penetration loss

Both outdoor and penetration losses have their own variabilities with their own standard deviations

The user’s overall composite probability of service must include composite median and standard deviation factors

COMPOSITE = ((OUTDOOR)2+(PENETRATION)2)1/2

LOSSCOMPOSITE = LOSSOUTDOOR+LOSSPENETRATION

Building

Outdoor Loss + Penetration Loss


Composite Probability of ServiceCalculating Fade Margin For Link Budget

Example Case: Outdoor attenuation is 8 dB., and penetration loss is 8 dB. Desired probability of service is 75% at the cell edge

What is the composite ? How much fade margin is required?

Composite Probability of ServiceCalculating Required Fade Margin

EnvironmentType

(“morphology”)MedianLoss,

dB

Std.Dev., dB

Urban Bldg. 15 8

Suburban Bldg. 10 8

Rural Bldg. 10 8

8 4Typical Vehicle

Dense Urban Bldg. 20 8

BuildingPenetration

Out-DoorStd.Dev., dB

8

8

8

8

8

CompositeTotal

AreaAvailabilityTarget, %

90%/75% @edge

90%/75% @edge

90%/75% @edge

90%/75% @edge

90%/75% @edge

FadeMargin

dB

7.6

7.6

7.6

6.0

7.6

COMPOSITE = ((OUTDOOR)2+(PENETRATION)2)1/2

= ((8)2+(8)2)1/2 =(64+64)1/2 =(128)1/2 = 11.31 dB

On cumulative normal distribution curve, 75%

probability is 0.675 above median. Fade Margin required =

(11.31) (0.675) = 7.63 dB. Cumulative Normal Distribution

Standard Deviations from Median (Average) Signal Strength

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

75%

.675


CommercialPropagation Prediction

Software

CommercialPropagation Prediction

Software

Chapter 4 Section C


Point-To-Point Path-Driven Prediction Models

Use of models based on deterministic methods

• Use of terrain data for construction of path profile• Path analysis (ray tracing) for obstruction, reflection analysis• Appropriate algorithms applied for best emulation of underlying

physics• May include some statistical techniques• Automated point-to-point analysis for enough points to appear to

provide large “area” coverage on raster or radial grid Commonly-used Resources

• Terrain databases

• Morphological/Clutter Databases

• Databases of existing and proposed sites

• Antenna characteristics databases

• Unique user-defined propagation models


Path-Driven Propagation Prediction Tools Data Structure

Geographic “Overlay” Format: Output Map(s) on screen or plotter

• Coverage– field strengths @ probability– probabilities @ field strength

• Best-Server• C/I (Adjacent Channel & Co-

Channel) Cell locations, cell grid Terrain elevation data

• USGS & Commercial databases• Satellite or aerial photography

Clutter data• Roads, rivers, railroads, etc.• State, county, MTA, BTA boundaries

Traffic density overlay Land use overlay


The World as “seen” by a Propagation Prediction Tool

Propagation tools use a terrain database, clutter data for land use, and vectors to represent features and traffic levels. The figure at right is a 3-D view of such databases in the area of this demonstration. Notice the granularity of the data and the very mild terrain undulations in the area, exaggerated 8 times in this view.


Survey Of Commercially Available Tools

A wide variety of software tools are available for propagation prediction and system design

Some tools are implemented on PC/DOS/Windows platforms, others on more powerful UNIX platform

Capabilities and user interfaces vary greatly

Several of the better-known tools for cellular RF engineering are shown in the table at right

RF Prediction Software Tools•Qualcomm

•QEDesign CDMA Tool(Unix)

•MSI•PlaNet (Unix)

•LCC•CellCad (Unix)•ANet (DOS PC)

•CNET•Wings (Unix)•Solutions (mainframe)

•ComSearch•IQSignum(Unix)

•AT&T•PACE (DOS PC)

•Motorola•proprietary (Unix)

•TEC Cellular: Wizard (DOS)•Elebra: CONDOR, CELTEC•Virginia Tech MPRG

•SMT-Plus Indoor Site Planning Tool


Composite Coverage Plot A composite coverage plot shows

the overall coverage produced by each sector in the field of view

The color of each pixel corresponds to the signal level of the strongest server at that point

Such plots are useful for identifying coverage holes and overall coverage extent


Equal Power Handoff Boundaries Plot

A Best Server Plot or in CDMA terms, an Equal Power Handoff Boundaries plot paints each pixel with a unique color to identify the best-serving sector at that point

• the boundaries shown are the equal-power points between cells

This type of plot is extremely useful in creating initial neighbor lists and identifying areas of no dominant server

Some tools (MSI Planet) can generate automatic neighbor lists from such a plot


Qualcomm’s QEDesign

Qualcomm’s commercial tool QEDesign offers a number of features targeted at CDMA system design and analysis. The figures above show the output of its microcell propagation analysis tool in the Washington, DC area, and a three-dimensional view of an antenna pattern. Other features of this package include live cursor mode in which the user can drag the cursor about and see in near-real-time the line-of-sight area visible from the selected location, or a coverage footprint calculated from that location.


General Survey Of Tool Features

Universal Basic Features of Most Tools

Automatically calculates signal strength at many points over a geographic area

• Use databases of terrain data, environmental conditions, land use, building “clutter”, estimated geographic traffic distribution, etc.

• User-definable 3-dimensional antenna patterns

• Automatically analyzes paths, selects appropriate algorithms based on path geometry

• Produces plots of coverage, C/I, etc. Used for analysis of sites, interference,

frequency planning, C/I evaluation, etc. Drawback: requires significant computation

power, time and RF staff special training

-20 dBm-30 dBm-40 dBm-50 dBm-60 dBm-70 dBm-80 dBm-90 dBm

-100 dBm-110 dBm-120 dBm

Signal Level

Legend

C/ILegend

>20 dB<20 dB<17 dB<14 dB


General Survey Of Tool Features, Continued

Popular Features of Advanced Tools

Accepts measurement input, can automatically generate predicted-vs-measured statistics and map displays

Automatic hexagon-manipulation grid utility

Maintains cell sites in relational database

• Easy manipulation, import, export Flexible user interface allows

multitasking Allows multiple user-defined

propagation models Three dimensional terrain view Roads, boundaries, coastline easily

overlaid onto any display

A

A

AA

A A

AA

A AA

A A

A

A

Pred. MeasMean -76 -72Std. Dv 9 12Samples 545 545

Area Name: DALLAS

Site Name

Subs: 100,000

Site # LatitudeLongitudeType Capacity

Number of Sites5 Total Capacity (Erlangs)221

SITE - 1SITE - 2SITE - 3SITE - 4SITE - 5

A1A2A3A4A5

33/17/4633/20/0833/16/5033/10/2833/25/21

96/08/3396/11/4996/12/1496/11/5196/03/53

S322S211S332S1101

77379188

Date: Initial Service

7

8

9

1

3

2

1

3

24

5

6

7

8

9

7

8

9

1

3

2

6

4

6

10

11



More Popular Advanced Features

Produces plots of server boundaries, C/I plots, handoff boundaries, etc.

Allows interactive change of antenna number, type, orientation, power and tilt

Using growth-scaleable traffic input mask, can predict traffic carried by each site, # channels required

• Can automatically highlight cells not meeting specified grade of service

Algorithms for automatic frequency planning and optimization

User can define or “mask” cells to be changed or unchanged during automatic optimization

43

2

56

17

43

2

56

17

CELL ERL Channels14 8.3 1722 2.1 526X 1.7 426Y 23 3126Z 14 20



More Popular Advanced Features

Identification of server and interferer signal levels in live cursor mode upon graphical coverage display

Generates bin C/I & coverage statistics for system evaluation

Predicted handoff analysis

• Statistical analysis of most likely handoff candidates

• Automatic generation of neighbor cell lists

• Percentage probability of handover

Runs on powerful workstations to minimize computation time

Cell 51 -82 dBmCell 76 -97 dBmC/I +15 dB

Cell 18Cell 24 48%Cell 16 22%Cell 17 18%Cell 05 8%Cell 22 4%

C/I Pct. of Area>20 dB 93.0%<20 dB 7.0%<17 dB 2.2%


Resolution Of Terrain Databases

Elevation data in terrain databases can be stored in any of several formats:

• Contour vectors: lines of constant elevation in vector segment form, digitized from topographic maps

• Elevation sample points on rectangular grids with fixed spacing

• Elevation sample points on latitude-longitude grids with spacing of a fixed number of arc-seconds

• Data can be converted from one format to another

10m

10m

3 arc-seconds

3 arc-seconds


Resolution Of Terrain Databases, Continued

It is useful to know the horizontal spacing in feet between sample points in a terrain database using arc-seconds, i.e., latitude-longitude spacing

North-South spacing is constant, everywhere on the planet

• 1 arc-second = 101.34 feet

• 1 degree = 69.096 miles East-West sample spacing varies with

the cosine of the North Latitude

• = 101.34 feet/arcsecond at the Equator

• = 0 feet/arcsecond at Poles

• = 101.34 ft. * Cos (N Lat) per arcsecond,

everywhere

N30º

N60º

(North Pole) N90º

(Equator) 0º

S30º

S60º

(South Pole) S90º

Latitude

0º Greenwich, UK

W 30º

W 60º

W 90º

W 120º

Longitude

1sec.

101.34 ft

101.34 ft * Cos (N Latº )


CommercialMeasurement Tools

CommercialMeasurement Tools

Chapter 4 Section D


Propagation Data Collection Philosophy

RF testing of sites is usually performed for one of two reasons: Drive Testing for model calibration

• Prior to cell design of a wireless system, accurate models of propagation in the area must be developed for use by the prediction software. A significant number of typical sites are evaluated using the test transmitter and receiver to determine signal decay rates and to get a fairly accurate understanding of the effects of typical clutter in the area.

• Tests are also conducted to evaluate the additional attenuation which the signal suffers during penetration of typical buildings and vehicles.

• The focus is on developing models generally applicable to the area, not on the performance of specific individual sites.

Drive Testing for site evaluation

• Although propagation models for an area already have been refined, coverage of a particular site is so critical, or its environment so variable due to urban clutter, that it is essential to actually measure the coverage and interference it will produce. The focus is on this specific site.


CW or Modulated Test Signals? Can measurements of unmodulated RF carriers provide adequate

propagation data for system design, or is it advisable to use a modulated RF signal similar to the type which will be radiated by actual BTS in the contemplated system?

• CW (continuous wave, i.e., unmodulated carriers) transmitters are moderately priced ($10K-$25K). CW-only receivers are priced from $5K to over $20K.

• Technology-specific GSM or CDMA modulated test transmitter-receiver systems are available, at costs in the $100,000-$275,000 range per TX-RX system.

Multiple Sites Simultaneously

Multipath Characteristics

Modulated Systems CW Systems

FER, BER statistics

Too expensive!

Delay Spread

Yes

Yes

Usually Not. However, DSP post-processing can yield some multipath data using various transforms. (Not

commercially available yet.)

No

Propagation Loss Mapping Yes Yes


Summary of Available Commercial Tools

Measurement data can be collected manually, but it is simply too tedious to obtain statistically useful quantities by hand

There are many commercial data collection systems available to automate the collection process

Many modern propagation prediction software packages have the capability to import measurement data, compare it with predicted values, and generate statistical outputs (mean error, standard deviation, etc.).

Commercial Measurement Systems•Grayson Electronics:

•Inspector32, Spectrum Tracker•Wireless Measurement Instrument•Handheld Logger

•MLJ, Berkeley Varitronics•CW test transmitters, receivers

•Qualcomm•Mobile Diagnostic Monitor•CDMA test TX-RX & analyzer

•SAFCO•SmartSAM , SmartSAM Plus*, PROMAS*, CDMA OPAS32

•COMARCO•NES-150, NES-250, NES-350

•LCC•RSAT; “Walkabout”, RSAT 2000 w/expansion chassis* TDMA/AMPS, GPS

•ZKSAM - AMPS tools•Rohde & Schwarz: GSM Tools


Elements of Typical Measurement Systems

WirelessReceiver

PC or Collector

GPSReceiver

DeadReckoning

Main Features Field strength measurement

• Accurate collection in real-time• Multi-channel, averaging

capability Location Data Collection Methods:

• Global Positioning System (GPS)

• Dead reckoning on digitized map database using on-board compass and wheel revolutions sensor

• A combination of both methods is recommended for the best results

Ideally, a system should be calibrated in absolute units, not just raw received power level indications

• Record normalized antenna gain, measured line loss


Typical Test Transmitter Operations

Typical Characteristics

• portable, low power needs

• weatherproof or weather resistant

• regulated power output

• frequency-agile: synthesized Operational Concerns

• spectrum coordination and proper authorization to radiate test signal

• antenna unobstructed

• stable AC power

• SAFETY:

– people/equipment falling due to wind, or tripping on obstacles

– electric shock

– damage to rooftop


A Typical Mobile Test Receiver

Receivers and decoders are installed only for the appropriate technologies and frequency bands

Internal GPS or external GPS may be used, with or without dead-reckoning capabilities

Internal GPSReceiver,

if used

Up to 4 technology-specific

decoder boards:AMPS, TDMAGSM, CDMA

Paging

Up to 4 technology andband-specific receivers:800 MHz. cellular150, 450, 800 Paging1900 PCS

Up to 2 handsetsmay be connectedfor GSM or CDMAat 800 or 1900 MHz.

inputs to internal RXs

MainOn/Off

RF toInt. GPS


Selecting and Tuning Propagation Models

Parameters of propagation models must be adjusted for best fit to actual drive-test measured data in the area where the model is applied

The figure at right shows drive-test signal strengths obtained using a test transmitter at an actual test site

Tools automate the process of comparing the measured data with its own predictions, and deriving error statistics

Prediction model parameters then can be “tuned” to minimize observed error


Measured Data vs. Model Predictions

Is the propagation model approximately correct?• Is the data scatter small enough to justify use of a model?• correct slope to match data• correct position up/down on Y-axis?


Analysis of Measured vs. Predicted

Several tools produce histograms showing the distribution of the differences between measured and predicted values

The mean of the difference between predicted and measured is a very important quantity. It should be small (on order of a few dB).

The standard deviation of the difference also should be small. If it is substantially larger than 8 dB., then either:

• the environment is very diverse (perhaps it should be broken into pieces with separate models for better fit) or

• the slope of the model is significantly different than the observed slope of the measurements (review the Sig. vs. Dist. graph)


Displaying Error Distribution by Location

Suppose a major hill blocked the signal in one direction, or the antenna pattern had an unexpected minimum in that direction

This would cause the data in the shadowed region to differ substantially from data in all remaining directions

Some tools can display the error values on a map like the one at right, to provide quick visual evidence for recognizing this type of problem

Documents

July, 19984 - 1RF100 (c) 1998 Scott Baxter Physical Principles of Propagation Chapter 4 Section A