An overview of Mobile Satellite Service (MSS) propagation measurements and modeling is intended as a summary of current results. While such research is on-going, the simple models presented here should be useful to systems engineers. A complete summary of propagation experiments with literature references is also included

Barts, R. Michael

Bostian, C. W.

Butterworth, J. S.

Campbell, Richard L.

Goldhirsh, Julius

Stutzman, Warren L.

Vogel, Wolfhard J.

English

NASA Technical Reports Server

N88-25696
MOBILE SATELLITE PROPAGATION MEASUREMENTS AND MODELING:
A REVIEW OF RESULTS FOR SYSTEMS ENGINEERS
Editor:
W. L. Stutzman
Contrlhutors:
R. M. Barts
C. W. Bostian
J. S. Butterworth
R. Campbell
J. Goldhirsh
W. L. Stutzman
W. J. Vogel
Virginia Tech
Virginia Tech
Virginia Tech
CRC, Canada
Michigan Tech
Applied Physics Lab./JHU
Virginia Tech
U. of Texas
VIRGINIA TECH
Satellite Communications Group
Bradley Department of Electrical Engineering
627 Whittemore Hall
Blacksburg, VA 24061
ABSTRACT
This overview of Mobile Satellite System (MSS)
propagation measurements and modeling is intended as a
summary of current results. While such research is on
going, the simple models presented here should be useful
to system engineers. A complete summary of MSS
propagation experiments with literature references is
also included.
INTRODUCTION
This paper is a collective effort by a group of
researchers in the U. S. and Canada in the area of Land
Mobile Satellite propagation. It concerns the
propagation effects of the natural environment as
encountered under highway conditions at UHF and L-Band
frequencies. It presents results, experimental and
theoretical, that are applicable to future operational "
Mobile Satellite Systems (MSS). Our understanding of MSS
propagation is by no means complete. Much needs to be
done both in measurement and in theory. _ This review is
essentially a summary of the current state of knowledge.
I07
https://ntrs.nasa.gov/search.jsp?R=19880016312 2020-03-20T06:53:26+00:00Z
This paper emphasizes results, not the details of
how experiments were performed or the theoretical
background of the models developed. A comprehensive list
of MSS related propagation experiments is presented (see
Table i); literature references for these experiments as
well as for key theory and modeling works are given.
CHARACTERISTICSOF MSS PROPAGATIONEFFECTS
MSS communications system design presents new
problems. Mobile terminals do not have the fixed-
terminal advantage of placing antennas with a clear line
of sight view to the satellite. Instead, a mobile with a
roof mounted antenna will encounter several propagation
effects that will influence the system margin,
reliability, and modulation format selection. These may
be classified as follows:
(a) Tree shadowing of the direct wave
(b) Blockage from natural terrain and structures
like overpasses
(c) Multipath scattering from terrain, and from
simple scatterers like utility poles
Specular reflection is not significant above 15
degrees elevation with circularly polarized antennas [5]
and therefore, we have not listed it as a propagation
problem. Blockage from structures such as overpasses
have been noted by Vogel during his balloon experiments
[9]. They are obviously serious short term blockage
problems but are not statistically significant. This
paper does not include structure blockage effects, but
concentrates on vegetative blockage and multipath
scattering.
MSS PROPAGATIONRESEARCHLITERATURE
Overview
The MSS propagation problem is attacked on three
fronts: experimental, basic EM theory, and modeling.
The most important of these is the establishment of an
experimental data base. Satellite, helicopter, balloon,
remotely piloted aircraft and tower platforms have been
used, and most resuZts appear to be applicable to
satellite-to-earth mobile systems. This data base serves
108
two purposes. First, it may be used for system planning
if data of the proper frequency, elevation angle, etc.
are available or can be scaled. Second, experimental
data are used in verifying theoretical models and to
drive software simulators.
Complete analytical solutions from electromagnetic
theory are not available due to the complexity of the
prup_gatiu** _**viLu, uuent, but partial analytical and
empirical statistical models of MSS propagation have been
developed [14, 20, 21, 22]. These offer considerable
insight into the physics of slant path propagation.
The mobile aspect of MSS propagation complicates the
problem. Theoretical models do not provide complete
information on the variation of the parameters in terms
of highway and system constraints. Simple empirical
models can be developed to give fade statistics for
simple relationships with the highway and system
parameters as inputs.
Experiments
Mobile satellite propagation experiments have been
conducted since 1978. Table 1 presents a chronology of
slant path propagation experiments in the UHF and L-
bands. Most of the experiments produced fade depth and
duration statistics for various mobile travel conditions.
Some results from these are given in following sections.
Some experiments also collected phase data. The
peak-to-peak variations of the phase, P, as a function of
peak-to-peak signal level fluctuations in dB, L, can be
expressed by [14]
where
P = C * L [degrees] (i)
C = 6.0 analytical value for unshadowed multipath
7.6 90th percentile} measured, includes
3.9 median } shadowing for fades up
2.1 10th percentile} to about 15 dB
The signal arriving at an MSS receiver has two
components: a direct path component which can experience
shadowing, and a diffuse component that is the sum of
signals reflected from nearby objects. Campbell has
performed experiments focusing on characterization of the
diffuse component, which is quantified by the power level
relative, to the unattenuated direct signal power, b0, and
the spatial distribution of arrival angles for the
diffuse power. The measurements were made at 1300 MHz at
109
several elevation angles near i0 degrees along a section
of a hardwood tree lined road in upper Michigan. The
average total diffuse signal power is 17 dB ± 1 dB below
the unattenuated direct path signal. The diffuse signal
has a rather even angular distribution, except for trees
within about i00 m of the receiver which have significant
contributions. The diffuse component is relatively
unaffected by instantaneous blockage of the direct
component; therefore, during deep fading the diffuse
component will dominate.
FADE DISTRIBUTIONS
Form Of The Fade Distribution
Both experimental observations and basic theory
indicate that the fade statistics measured over a large
distance, become Rician at low fade levels, because of
the dominant multipath effects. For high fade levels a
number of experiments indicate that they become lognormal
when vegetative shadowing dominates. Thus, MSS fading is
modeled by a Rician distribution, described by the
multipath-to-carrier ratio K, to predict fading when
there is no blockage of the signal. When tree blockage
is present, the fading is modeled by the sum of a
lognormal distribution, described by the mean, W and
standard deviation, _ , and a Rayleigh distribution,
described by b0, the average scattered power in the
multipath component. Bradley and Stutzman [18] showed
how these distributions can be combined for an arbitrary
percentage of shadowing along the travel route;
apparently Lutz et al. [ii] found this independently.
Experimental Results
Representative experimental results are plotted in
Fig. i. Shown are cumulative fade distributions over
typically tens of km of road distance [18]. The
distributions depend strongly on the elevation angle when
shadowing by roadside trees dominates. A four-lane
divided freeway lined with trees (Fig. la) can produce
larger fades than a two-lane road lined with utility
poles and trees (Fig. ib). In comparison, a road with a
wider cleared easement, even though tree lined (RT. 32),
will have smaller probabilities. Measurements obtained
at UHF on a road for various growing seasons (Leafless,
March 1986; Fall Foliage, October 1985; Full Blossom,
t10
June 1986) show only a small increase in attenuation due
to leaves (Fig. ic).
To scale UHF data to L-Band, the relationship
F L = (1.35 -+ O.I)*F u (3)
can be used where F L and FU are the L-Band and UHF fades
in dB s r_spect_v_]y [IR] _ __ _ _4_,._
obtained in differing geographic areas (Fig. id)
emphasizes the dependence of the distribution on the
dominating environmental parameters, from heavy tree
shadowing (Curve A) to pure multipath (Curve G).
Simple Model
A simple model for predicting the probability that
the fades will be less than a certain amount F is given
by [23]
G(F) [i - e-(F-UI)/U2](I-P)
+ [i - {(F + 50)/vl)V2]p (2)
where
F = fade level with respect to LOS in dB
P = fraction of time vegetative shadowing occurs
along travel route
UI, U2, Vl, V2 = fit coefficients
U1 = 0.01*K 2 - .378.K + 53.98
U2 = 331.25.K -2-29
Vl = 0.275-b 0 + 0.723* , + 0.336* O + 56.987
V2 = 0.006*b 0 - 0.008* , + 0.013" O + 0.121
where
K = average multipath-to-unshadowed carrier
ratio for
unshadowed path
b 0 = average multipath-to-carrier ratio for
shadowed path
", = mean of lognormal signal
o = standard deviation of lognormal signal
This simple model is valid for typical ranges of the
propagation parameters. Typical values for each of the
parameters are as follows:
111
-13 dB < K < -22 dB
-12 dB < b 0 < -18 dB
-i dB < _ < -i0 dB
0.5 dB < o < 3.5 dB
Behavior Of Statistical Parameters With Time
Cumulative fade distributions give information about
the average link performance for a large number of
transmissions for particular environmental conditions. A
typical telephone call has a shorter duration (about 90
seconds) and therefore covers a shorter distance than the
one on which Fig. 1 is based. When cumulative fade
distributions are determined for successive 90 second
intervals, they will vary, depending upon the short term
variation of the shadowing parameter statistics. At
percentage levels of main interest, from 1% to 20%, the
10th, 50th and 90th percentiles of the 90 second fade
levels have been calculated and are given in Fig. 2 for
elevation angles of 30, 45 and 60 degrees. The curve
labeled median represents the median cumulative fade
distribution and the upper and lower curves give the
bounds into which 80% of the measured distributions fall.
The curves can be fit by the relationship:
F(n,P) = A(n)*in(P) + B(n) (4)
where
F(n,P) = Fade at nth percentile for P percent of
time fade is exceeded
A(n), B(n) = fit coefficients
Hence, these experimental results indicate that over the
limited percentage interval a simple logarithmic
relationship holds.
FADE DURATIONS
Measured fade durations for a 5 dB threshold are
given in Fig. 3. The curves correspond to the combined
results of repeated runs along several roads at the
indicated elevation angles, both under predominantly
shadowing or under multipath conditions. Two sets of
statistics are provided, "fade durations": the duration
the signal is below the threshold and "nonfade
durations": the duration the signal is greater than the
112
threshold. The duration is expressed as spatial variable
in wavelengths (0.2m) to gain independence from the
vehicle speed. The durations are seen to systematically
depend upon the elevation angle for the shadowing data.
The lower the elevation, the longer the fades and the
shorter the nonfades. Multipath durations are
independent of the elevation angle, having very short
fades and long nonfades.
The distribution of fade durations cannot in general
b_ pz_dicted analytically. Schmier and Bostian have
reported a software simulator [24] which they claim will
accurately predict the fade duration statistics of a
signal whose cummulative fade distribution is known. The
simulator operation is based on scaling values from a
universal data set. Its predictions of the total number
of fades exceeding specified thresholds agree well with
experiment, but unpublished work by Barts indicates that
it may not predict average fade durations well. Barts'
work indicates that Schmier and Bostian's simulator works
correctly, but its universal data set may have been
incorrectly derived. Each side can make an effective
case for the correctness of its results, and at the time
this paper was written the issue was still unsettled.
Table 1
summary of Slant Path UHF/L-Band
Propagation Experiments
Entry
No.
1
2
3
4
5
6
7
8
9
i0
ii
12
13
14
15
16
17
18
19
Investigators Date Source
Hess[l,2] 3/78 ATS-6 860
Anderson[3,4] 78 ATS-6 1550
Butterworth[5] 4-11/81 Tower 840
" [5] 82 Tethered
Balloon
" [5] 9/82 Helicopter 870
" [6] 6/83 Helicopter 870
" [7] 11/82 MARECS A 1542
" [6] 6/83 MARECS A 1542
Vogel[8] 10/83 Balloon 869
1/84
Vogel[9] 11/84 Balloon 869
1501
Bultitude[10] 85 Tower 800
900
Lutz, et. al.[ll] 1/21-28/84 MARECS 1560
2/25-3/8/84
6/27-7/1/84
Vogel/Goldhirsh[12] 6/85 RPV
Goldhirsh/Vogel[13] 10/85 Helicopter
3/86
Vogel/Hong[14] 7/86 Balloon
Vogel/Goldhirsh[15] 8/86 Helicopter
PiFex[16,17] 3/87 Tower
Goldhirsh/Vogel[18] 6/87 Helicopter
MARECS AVogel/Goldhirsh[19] 12/87
Freq
(MHz) Comments
870
870
870
1502
870
1502
870
870
1502
1541
Static tests
Spread spectrum
measurements
Measurements
made in Europe
No propagation
data avail.
21 ° elevation
angle
113
CUMULATIVE DISTRIBUTIONS FOR ROUTE 295 SOUTH (RHS)
COMPARISON FOR DIFFERENT ELEVATION ANGLES AT L-BAND
102 -
6-
m
,_o 5-
,3_ 3.
°'_ 2-
_60 N\ 45' _'tl, I ) 30 U
4 2 u _ 4 6 _ io 12 ,4 16 18 20 n =_ ;_ 2',
FADE DEPTH (dB}
Figure la.
COMPARISON OF CUMULATIVE FADE DISTRIBUTIONS
FOR VARIOUS ROADS AT AN ELEVATION ANGLE OF
45 u FOR L.BAND
b
5
z _
o<
< o b
____ 4
t
N[ SW
N_ 10_ _ _ •
4 2 U 2 4 _ t_ _0 12 _4 16 _8 20 22 2,1 26 2B
FAOE DEPTH (dB(
Figure lb.
_uJ
COMPARISON OF L BAND FADE DISTRIBUTIONS
FOR VARIOUS MSS INVESTIGATIONS
CUMULATIVE FADE DISTRIBUTIONS FOR lO!_
VARIOUS SEASONS =
,0 _
O
! ,_, _i_, _ "IUN_
10 0 " 1987
1111111111111111 i i i i i i i i i i _ 1
-6 -4 -2 0 2 4 6 8 10 t2 14 16 18 20 22 24 26 28 1 1_ , _ _ , , , , ,
-6 -4 -2 0 2
FADE DEPTH (dB)
Figure ic.
A BUT TERWORIH (1984)
(RURAL FORRESTED)
B GOLDHIRSH AND ',OGEL (IgB8)
(ROADSIDE TREES SHADOWING; EL=45
C JONGEJANS ET AL (1986)
(RURAL)
D BUTTERWORTH 11984)
(RURAL FARMLAND)
E VOGEL AND HONG (198S)
(RURAL FORRESTEO)
F GOLDHIRSH AND VOGEL (1988}
(ROADSSDE TREES • MULTIPATH)
G VOGEL AND HONG (1988)
(RURAL FARMLAND)
6 8 10 12 14 16 18 20 22 24 26 28
FADE DEPTH (dl_)
Figure id.
114
3O
25-
_. 20.
.I-
15.
,<
Ik .
10-
BEST FIT CUMULATIVE MEDIAN FADE DISTRIBUTION
WITH 10 AND 90 PERCENTILE BOUNDS
(ELEVATION ANGLE = 30")
10 PERCENTILE:
F=-4.9 Ln(P) + 24.9
F=-4.0 LnIP) + 14.2
, i i , J , _l I , , , i , iii
2 3 4 5 6 8 10 2 3 4 5 6 8 102
PERCENTAGE OF DISTANCE FADE IS GREATER
THAN ORDINATE
Figure 2a.
BEST FIT CUMULATIVE MEDIAN FADE DISTRIBUTION
WITH 10 AND 90 PERCENTILE BOUNDS
ELEVATION ANGLE = 45 °
25
20
A
_--- 15
,,r.
Q
_ 10-
0
10o
L 10 PERCENTILE:
0 _ _l* MEDIAN:
F = -1.9 Ln(P) . 725
i i i , i i,! , . , i | |r
2 3 4 5 6 8 101 2 3 4 5 6 II 102
PERCENTAGE OF THE DISTANCE
FADE IS GREATER THAN ORDINATE
Figure 2b.
BEST FIT CUMULATIVE MEDIAN FAOE DISTRIBUTION
WITH 10 AND 90 PERCENTILE BOUNDS
ELEVATION ANGLE = 60 °
15.
lO.
o
10 PERCENTILE:
F=-4.7 Ln(P) ÷ 18.1
F=-0.75 Ln(P) + 4 0
PERCENTAGE OF DISTANCE FADE
IS GREATER THAN ORDINATE
Figure 2c.
Fode/Nonfode Durotions ot L Bond
5 dB Fode Threshold
10 * _l_l_J_ _ ----x--=-_ L'a_u'i-'---L --L-L-Lx L_
Ul J . % k_.'<_-., -L
] \ \ \%\\ \% ',,
,04 \ \),X, ".,',
.o_ \ . "\X'X', [
• \- \\,k ", [
% \\_ % ',%1
o o4Sdeg %. \ % %_ _ L
Munll_th: "k \ % % _ -I
I -r-_r-r_q ---r_-r*_rr_ ---'-_ _-_'-rrrr_-
I0 _' 1 10 10 '
Duration - wavelengths
Figure 3.
115
REFERENCES
i. G. C. Hess, "Land Mobile Satellite Path Loss
Measurements," Third Year ATS-6 Experiment Final
Report, Motorola Inc., Schaumberg, IL, Sept. 6,
1978.
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" IEEE Trams on Vehicular Technology,measurements,
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116
869 Mhz derived from remotely piloted aircraft
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117


Mobile satellite propagation measurements and modeling: A review of results for systems engineers

Mobile satellite propagation measurements and modeling: A review of results for systems engineers

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