Transcript Propagation

Chapter 4 Section A
Physical Principles of
Propagation
July, 1998
RF100 (c) 1998 Scott Baxter
4-1
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
July, 1998
RF100 (c) 1998 Scott Baxter
4-2
Frequency and Wavelength: Implications
 Radio signals in the atmosphere
propagate at almost speed of light
l=C/F
for AMPS:
l = wavelength
C = distance propagated in 1 second
F = frequency, Hertz
F= 870 MHz
l = 0.345 m = 13.6 inches
for PCS-1900:
F = 1960 MHz
l = 0.153 m = 6.0 inches
l/2
July, 1998
 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
RF100 (c) 1998 Scott Baxter
4-3
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 worldwide 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
July, 1998
RF100 (c) 1998 Scott Baxter
4-4
More Atmospheric Propagation Effects
“Rain Fades” on
MIcrowave Links
Refraction
by air layers
Ducting
by air layers
>100 mi.
July, 1998
 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 longdistance propagation beyond the horizon. May
last minutes or hours
• can occur in wireless systems
RF100 (c) 1998 Scott Baxter
4-5
Dominant Mechanisms of Mobile Propagation
Free Space
d
D
A
B
Reflection
with partial cancellation
Knife-edge
Diffraction
July, 1998
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 180out 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...
RF100 (c) 1998 Scott Baxter
4-6
Free-Space Propagation
r
Free Space
“Spreading” Loss
energy intercepted
by receiving
antenna is
proportional to 1/r2
d
A
 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)
1st Fresnel Zone
D
B
July, 1998
First Fresnel Zone =
{Points P where AP + PB - AB < l/2 }
Fresnel Zone radius d = 1/2 (lD)^(1/2)
RF100 (c) 1998 Scott Baxter
4-7
Reflection With Partial Cancellation
Heights Exaggerated
for Clarity
HTFT
HTFT
 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
DMILES
Path Loss [dB ]= 172 + 34 x Log (DMiles )
- 20 x Log (Base Ant. HtFeet)
- 10 x Log (Mobile Ant. HtFeet)
SCALE PERSPECTIVE
Comparison of Free-Space and Reflection Propagation Modes
Assumptions: Flat earth, TX ERP = 50 dBm, @ 1950 MHz. Base Ht = 200 ft, Mobile Ht = 5 ft.
DistanceMILES
1
2
4
6
8
10
15
20
Received Signal in
Free Space, DBM
Received Signal in
Reflection Mode
-52.4
-58.4
-64.4
-67.9
-70.4
-72.4
-75.9
-78.4
-69.0
-79.2
-89.5
-95.4
-99.7
-103.0
-109.0
-113.2
July, 1998
RF100 (c) 1998 Scott Baxter
4-8
Signal Decay Rates in Various Environments
Signal Level vs. Distance
0
-10
-20
-30
-40
1
2
3.16
5 6 7 8
Distance, Miles
One Decade
One Octave
of distance (2x)
July, 1998
of distance (10x)
10
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
RF100 (c) 1998 Scott Baxter
4-9
Knife-Edge Diffraction
H
R1
n = -H
R2
2
l
(
1
R1
+
1
R2
)
0
-5
atten -10
dB -15
-20
-25
-5 -4 -3 -2 -1 0 1 2 3
n
July, 1998
 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
n from the geometry of the path
 Next consult the table to obtain the
obstruction loss in db
 Add this loss to the otherwisedetermined 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
RF100 (c) 1998 Scott Baxter
4 - 10
Local Variability: Multipath Effects
Multi-path Propagation
Rayleigh Fading
A
l/2
10-15 dB
d
July, 1998
 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 l/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”
RF100 (c) 1998 Scott Baxter
4 - 11
Combating Rayleigh Fading: Space Diversity
D
Signal received
by Antenna 1
Signal received
by Antenna 2
Combined
Signal
July, 1998
 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 instantby-instant to whichever is best
 Required separation D for good
decorrelation is 10-20l
• 12-24 ft. @ 800 MHz.
• 5-10 ft. @ 1900 MHz.
RF100 (c) 1998 Scott Baxter
4 - 12
Space Diversity Application Limitations
D
Signal received
by Antenna 1
Signal received
by Antenna 2
Combined
Signal
July, 1998
 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
RF100 (c) 1998 Scott Baxter
4 - 13
Using Polarization Diversity
Where Space Diversity Isn’t Convenient
V+H
or
\+/
A B
A B
Antenna A
Antenna B
Combined
July, 1998
 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
RF100 (c) 1998 Scott Baxter
4 - 14
The Reciprocity Principle
Does it apply to Wireless?
-148.21 db
@ 870.03 MHz
-148.21 db
@ 835.03 MHz
-151.86 db
@ 870.03 MHz
July, 1998
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
RF100 (c) 1998 Scott Baxter
4 - 15
Chapter 4 Section B
Propagation Models
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 16
Types Of Propagation Models And Their Uses
Examples of various model types
 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
July, 1998
 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
RF100 (c) 1998 Scott Baxter
4 - 17
General Principles Of Area Models
-50
+90
-60
+80
-70
+70
-80
+60
Field
Strength,
+50 dBµV/m
RSSI,
-90
dBm
-100
+40
-110
+30
-120
+20
0
3
6
9
12 15 18 21 24 27 30 33
Distance from Cell Site, km
 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.
July, 1998
 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
RF100 (c) 1998 Scott Baxter
4 - 18
The Okumura Model: General Concept
100
(dB)
35
80
Correction factor, Garea
50
Urban Area
70
d, km
Median Attenuation A(f,d), dB
70
40
30
26
25
20
15
10
9 dB
5
2
5
1
10
30
850 MHz
850
100
500
Frequency f, MHz
3000
100
200
300
500 700 1000
Frequency f, (MHz)
2000
3000
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 19
Structure of the Okumura Model
Path Loss [dB] = LFS + Amu(f,d) - G(Hb) - G(Hm) - Garea
Base Station
Height Gain
= 20 x Log (Hb/200)
Amu(f,d) Additional Median Loss
from Okumura’s Curves
Urban Area
100
80
50
70
Mobile Station
Height Gain
= 10 x Log (Hm/3)
d, km
Median Attenuation A(f,d), dB
70
35
Correction factor, Garea (dB)
Free-Space
Path Loss
Morphology Gain
0 dense urban
5 urban
10 suburban
17 rural
30
25
20
15
10
5
850 MHz
40
100
30
26
200
300
500 700
1000 2000 3000
Frequency f, (MHz)
5
2
1
10
Frequency f, MHz
100
500
850
3000
 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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 20
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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 21
Hata Model General Concept and Formulas
(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.)
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
July, 1998
f - carrier frequency, MHz
hb and hm - BS and MS
antenna heights, m
d - distance between BS
and MS antennas, km
Environmental Factor C
0
dense urban
-5 urban
-10 suburban
-17 rural
RF100 (c) 1998 Scott Baxter
4 - 22
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 )
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
July, 1998
RF100 (c) 1998 Scott Baxter
Environmental
Factor C
1900
-2
dense urban
-5
urban
-10 suburban
-26 rural
4 - 23
Examples of Morphological Zones
Suburban
Urban
Dense Urban
Suburban
Urban
Dense Urban
 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
Although zone definitions are arbitrary, the examples and definitions illustrated above
are typical of practice in North American PCS designs.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 24
Example Morphological Zones
Rural - Highway
Rural - Highway
Rural
Rural
Suburban
Suburban
 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.
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 25
The MSI Planet General Model
Pr = Pt + K1 + k2 log(d) + k3 log(Hb) + K4 DL + K5 log(Hb) log(d)
+ K6 log (Hm) + Kc + Ko
Pr - received power (dBm)
Pt - transmit ERP (dBm)
Hb - base station effective antenna height
Hm - mobile station effective antenna height
DL - diffraction loss (dB)
K1 - intercept
K2 - slope
K3 - correction factor for base station antenna height gain
K4 - correction factor for diffraction loss (accounts for clutter heights)
K5 - Okumura-Hata correction factor for antenna height and distance
K6 - correction factor for mobile station antenna height gain
Kc - correction factor due to clutter at mobile station location
Ko - correction factor for street orientation
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 26
Typical Model Results
Including Environmental Correction
Okumura/Hata
Tower
Height,
m
EIRP
(watts)
C,
dB
Range,
km
30
30
30
50
200
200
200
200
0
-5
-10
-17
4.0
4.9
6.7
26.8
f =1900 MHz.
Tower
Height,
m
EIRP
(watts)
C,
dB
Range,
km
Dense Urban
Urban
Suburban
Rural
30
30
30
50
200
200
200
200
-2
-5
-10
-26
2.52
3.50
4.8
10.3
f = 870 MHz.
Dense Urban
Urban
Suburban
Rural
COST-231/Hata
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 27
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 28
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
Area View
Signal
Level
Legend
July, 1998
-20 dBm
-30 dBm
-40 dBm
-50 dBm
-60 dBm
-70 dBm
-80 dBm
-90 dBm
-100 dBm
-110 dBm
-120 dBm
 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
RF100 (c) 1998 Scott Baxter
4 - 29
Statistical Techniques
Distribution Statistics Concept
Signal Strength Predicted Vs. Observed
 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 s
• M and s can be applied to find
probability of receiving an
arbitrary signal level at a given
distance
July, 1998
Model is tweaked to
produce “Best-Fit” curve
RSSI,
dBm
Observed
Signal Strength
50% of observed
data is above curve
Distance
Occurrences
50% of observed
data is below curve
Normal
Distribution
RSSI
Median
Signal
Strength
RF100 (c) 1998 Scott Baxter
s,
dB
4 - 30
Statistical Techniques
Practical Application Of Distribution Statistics
SIGNAL STRENGTH vs DISTANCE
 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, s
(determine from histogram)
• add an extra allowance into model
– drop curve so a desired % of
observations are above model
predictions
RSSI,
dBm
25% of locations
exceed blue curve
50% exceed red
75%
exceed
black
Min signal
req’d for
operation
Distance
Cell radius for
75% reliability
at edge
Occurrences
Cell radius for
Cell radius for 75% reliability
90% reliability
at edge
at edge
Normal
Distribution
RSSI
Median
Signal
Strength
July, 1998
RF100 (c) 1998 Scott Baxter
s,
dB
4 - 31
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
Statistical View of
 For overall 90% location probability within cell
Cell Coverage
coverage area, probability will be 75% at cell edge
• Result derived theoretically, confirmed in
75%
modeling with propagation tools, and observed
from measurements
90%
• 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
Area Availability:
90% overall within area
• Recent publications by Nortel’s Dr. Pete
75%at edge of area
Bernardin describe the relationship between
area and edge reliability, and the field
measurement techniques necessary to
demonstrate an arbitrary degree of coverage
reliability
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 32
Application Of Distribution Statistics: Example
 Let’s design a cell to deliver at least 95 dBm to at least 75% of the
Cumulative Normal Distribution
locations at the cell edge
100%
(This will provide coverage to 90% of
90%
total locations within the cell)
 Assume that measurements you
80%
75%
have made show a 10 dB standard
70%
deviation s
60%
 On the chart:
50%
• To serve 75% of locations at the
40%
cell edge , we must deliver a
30%
median signal strength which is
20%
.675 times s stronger than -95
0.675s
dBm
10%
• Calculate:
0%
- 95 dBm + ( .675 x 10 dB )
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3
= - 88 dBm
Standard Deviations from
Median (Average) Signal Strength
• So, design for a median signal
strength of -88 dBm!
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 33
Statistical Techniques:
Normal Distribution Graph & Table For Convenient Reference
Cumulative Normal Distribution
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
-3
-2.5 -2
-1.5 -1
-0.5
0
0.5
1
1.5
2
2.5
Standard Deviation from Mean Signal Strength
July, 1998
RF100 (c) 1998 Scott Baxter
3
Standard
Deviation
-3.09
-2.32
-1.65
-1.28
-0.84
-0.52
0
0.52
0.675
0.84
1.28
1.65
2.35
3.09
3.72
4.27
Cumulative
Probability
0.1%
1%
5%
10%
20%
30%
50%
70%
75%
80%
90%
95%
99%
99.9%
99.99%
99.999%
4 - 34
Building Penetration
Statistical Characterization
Building penetration
Vehicle penetration
Typical Penetration Losses, dB
compared to outdoor street level
Environment
Type
(“morphology”)
Median Std.
Loss, Dev.
dB
s, dB
Dense Urban Bldg.
20
8
Urban Bldg.
15
8
Suburban Bldg.
10
8
Rural Bldg.
10
8
Typical Vehicle
8
4
July, 1998
 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
RF100 (c) 1998 Scott Baxter
4 - 35
Composite Probability Of Service
Adding Multiple Attenuating Mechanisms
Building
Outdoor Loss + Penetration Loss
sCOMPOSITE = ((sOUTDOOR)2+(s ENETRATION)2)1/2
P
LOSSCOMPOSITE = LOSSOUTDOOR+LOSSPENETRATION
 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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 36
Composite Probability of Service
Calculating Fade Margin For Link Budget
 Example Case: Outdoor attenuation s is 8 dB., and penetration loss
s is 8 dB. Desired probability of service is 75% at the cell edge
 What is the composite s? How much fade margin is required?
sCOMPOSITE = ((sOUTDOOR)2+(sPENETRATION)2)1/2
= ((8)2+(8)2)1/2 =(64+64)1/2 =(128)1/2 = 11.31 dB
Cumulative Normal Distribution
On cumulative normal distribution curve, 75%
probability is 0.675 s above median.
Fade Margin required =
(11.31)  (0.675) = 7.63 dB.
100%
90%
Composite Probability of Service
80%
Calculating Required Fade Margin
Building
OutComposite
Penetration Door
Total
Environment
Type
Median Std.
Std.
Area
Fade
(“morphology”) Loss, Dev.
Dev.
Availability
Margin
dB
s, dB s, dB
Target, %
dB
Dense Urban Bldg. 20
8
8
90%/75% @edge
7.6
Urban Bldg.
15
8
8
90%/75% @edge
7.6
Suburban Bldg.
10
8
8
90%/75% @edge
7.6
Rural Bldg.
10
8
8
90%/75% @edge
7.6
Typical Vehicle
8
4
8
90%/75% @edge
6.0
75%
70%
60%
50%
40%
30%
20%
10%
0%
.675
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1
1.5 2 2.5 3
Standard Deviations from
Median (Average) Signal Strength
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 37
Chapter 4 Section C
Commercial
Propagation Prediction
Software
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 38
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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 39
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 & CoChannel)
 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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 40
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 41
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
•ANet
(Unix)
(DOS PC)
•CNET
•Wings
•Solutions
(Unix)
(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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 42
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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 43
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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 44
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 45
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
July, 1998
RF100 (c) 1998 Scott Baxter
Signal
Level
Legend
C/I
Legend
-20 dBm
-30 dBm
-40 dBm
-50 dBm
-60 dBm
-70 dBm
-80 dBm
-90 dBm
-100 dBm
-110 dBm
-120 dBm
>20 dB
<20 dB
<17 dB
<14 dB
4 - 46
General Survey Of Tool Features, Continued
A
Pred.
Meas
Mean
-76
72
Std. Dv
9
12
Samples 545
545
A
Popular Features of Advanced Tools
A
A
A A AA
 Accepts measurement input, can
automatically generate predicted-vsmeasured 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
July, 1998
RF100 (c) 1998 Scott Baxter
A
A
A
A
A A
-
Area Name: DALLAS
Date: Initial Service
Subs: 100,000
Site Name Site # LatitudeLongitudeType Capacity
SITE - 1
SITE - 2
SITE - 3
SITE - 4
SITE - 5
A1
A2
A3
A4
A5
Number of Sites
5
33/17/4696/08/33
33/20/0896/11/49
33/16/5096/12/14
33/10/2896/11/51
33/25/2196/03/53
S322
S211
S332
S11
01
77
37
91
8
8
Total Capacity (Erlangs)221
7
9
6
1
3
1
3
4
8
2
2
7
1
8
9
6
7
9
5
10
3
11
2
8
4
6
4 - 47
General Survey Of Tool Features, Continued
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
July, 1998
RF100 (c) 1998 Scott Baxter
CELL
14
22
26X
26Y
26Z
2
3
7
1
6
4
5
ERL Channels
8.3
17
2.1
5
1.7
4
23
31
14
20
2
3
7
1
6
4
5
4 - 48
General Survey Of Tool Features, Continued
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
July, 1998
RF100 (c) 1998 Scott Baxter
Cell 51 -82 dBm
Cell 76 -97 dBm
C/I +15 dB
C/I Pct. of Area
>20 dB 93.0%
<20 dB
7.0%
<17 dB
2.2%
Cell 18
Cell 24 48%
Cell 16 22%
Cell 17 18%
Cell 05 8%
Cell 22 4%
4 - 49
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
July, 1998
RF100 (c) 1998 Scott Baxter
10m
10m
3 arc-seconds
3 arc-seconds
4 - 50
Resolution Of Terrain Databases, Continued
Latitude
Longitude
 It is useful to know the horizontal
(North Pole) N90º
0º Greenwich, UK
spacing in feet between sample points
N60º
W 30º
in a terrain database using arc-seconds,
N30º
i.e., latitude-longitude spacing
W 60º
(Equator) 0º
 North-South spacing is constant,
W 90º
S30º
everywhere on the planet
S60º
W 120º
• 1 arc-second = 101.34 feet
(South Pole) S90º
• 1 degree = 69.096 miles
 East-West sample spacing varies with
the cosine of the North Latitude
1
101.34 ft
• = 101.34 feet/arcsecond
sec.
at the Equator
• = 0 feet/arcsecond at Poles
101.34 ft * Cos (N Latº )
• = 101.34 ft. * Cos (N Lat)
per arcsecond, everywhere
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 51
Chapter 4 Section D
Commercial
Measurement Tools
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 52
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.
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 53
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 transmitterreceiver systems are available, at costs in the $100,000$275,000 range per TX-RX system.
Modulated Systems
CW Systems
Multiple Sites Simultaneously
Too expensive!
Yes
Propagation Loss Mapping
Yes
Yes
FER, BER statistics
Yes
No
Delay Spread
Usually Not. However, DSP
post-processing can yield
some multipath data using
various transforms. (Not
commercially available yet.)
Multipath Characteristics
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 54
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.).
July, 1998
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
RF100 (c) 1998 Scott Baxter
4 - 55
Elements of Typical Measurement Systems
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
July, 1998
RF100 (c) 1998 Scott Baxter
Wireless
Receiver
PC or
Collector
GPS
Receiver
Dead
Reckoning
4 - 56
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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 57
A Typical Mobile Test Receiver
 Receivers and decoders are installed Main
only for the appropriate technologies On/Off
and frequency bands
 Internal GPS or external GPS may
be used, with or without deadRF to
Int. GPS
reckoning capabilities
inputs to internal RXs
Up to 2 handsets
may be connected
for GSM or CDMA
at 800 or 1900 MHz.
Internal GPS
Receiver,
if used
Up to 4
technology and
band-specific
receivers:
800 MHz. cellular
150, 450, 800 Paging
1900 PCS
Up to 4
technology-specific
decoder boards:
AMPS, TDMA
GSM, CDMA
Paging
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 58
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 drivetest 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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 59
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?
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 60
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)
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 61
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
July, 1998
RF100 (c) 1998 Scott Baxter
4 - 62