NTIA Report 98-355

Man-Made Noise in the 136 to 138-MHz

VHF Meteorological Satellite Band

Robert J. Achatz

Yeh Lo

Peter B. Papazian

Roger A. Dalke

George A. Hufford

U.S. DEPARTMENT OF COMMERCE

William M. Daley, Secretary

Larry Irving, Assistant Secretary

for Communications and Information

September 1998
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CONTENTS

Page

FIGURES ...................................................................... v

TABLES ..................................................................... viii

1. INTRODUCTION .............................................................. 1

1.1 Background and Terminology ............................................. 2

2. MEASUREMENT SYSTEM DESCRIPTION ....................................... 6

2.1 Vehicle ............................................................... 6

2.2 Antenna ............................................................... 7

2.3 Receiver ............................................................... 7

2.4 Data Acquisition ........................................................ 9

2.5 Radio Frequency Interference ............................................. 9

2.6 Verification ........................................................... 10

3. MEASUREMENTS ........................................................... 16

3.1 Procedure ............................................................ 16

3.2 Representative Noise Measurements ...................................... 16

4. NOISE FIGURE SUMMARIES ................................................. 29

4.1 Distribution Functions of Median, Mean, and Peak Powers .................... 29

4.2 Comparison of Measurement and Modeling Methods ........................ 30

4.3 Noise Figure Predictions ................................................ 31

5. AMPLITUDE PROBABILITY DISTRIBUTION ANALYSIS ......................... 36

5.1 Middleton Noise Model ................................................. 36

5.2 Simplified Noise Model ................................................. 37

5.3 Extraction of Noise Model Parameters from Measurements .................... 39

5.4 Simulation Results ..................................................... 40

5.5 Change of Simulation Time Increment ..................................... 41

6. CONCLUSIONS .............................................................. 62

7. REFERENCES ............................................................... 64

Appendix A RECEIVER SPECIFICATIONS SUMMARY ............................. 67
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v

FIGURES

Figure 1.1 Median values of Fa...................................................... 6

Figure 2.1 Antenna pattern of a quarter-wave monopole over circular ground plan .......... 11

Figure 2.2 Receiver block diagram .................................................. 12

Figure 2.3 Median, mean, and peak receiver noise power over a 24-h period ............... 12

Figure 2.4 APD of receiver noise power ............................................. 13

Figure 2.5 APD of environmental noise in a quiet canyon with power inverter on ........... 14

Figure 2.6 APD of continuous wave signal with a 10 dB signal-to-noise ratio ............... 15

Figure 3.1a Median, mean, and peak power at Lakewood, Colorado residence on November 10,

1996 .................................................................... 21

Figure 3.1b Median, mean, and peak power at Lakewood, Colorado residence on November 11,

1996 .................................................................... 21

Figure 3.2a Median, mean, and peak power at Boulder, Colorado residence on November 16,

1996 .................................................................... 22

Figure 3.2b Median, mean, and peak power at Boulder, Colorado residence on November 17,

1996 .................................................................... 22

Figure 3.3a Median, mean, and peak power at edge of office park near interstate highway on

November 25, 1996 ....................................................... 23

Figure 3.3b Median, mean, and peak power at center of office park on November 27, 1996 ... 23

Figure 3.3c Median, mean, and peak power at edge of office park near residential area on

November 30, 1996 ....................................................... 24

Figure 3.4a Median, mean, and peak power at downtown Boulder, Colorado on November 20,

1996 .................................................................... 25

Figure 3.4b Median, mean, and peak power at downtown Denver, Colorado on December 3,

1996 .................................................................... 25

Figure 3.5a Median, mean, and peak power at rural mountain site near Ward, Colorado on

December 5, 1996 ......................................................... 26

Figure 3.5b Median, mean, and peak power at rural plains site in eastern Colorado on December

6, 1996 .................................................................. 26

Figure 3.6 Median, mean, and peak power of automobiles measured in Clear Creek Canyon,

Colorado ................................................................ 27

Figure 3.7 Median, mean, and peak power of electrical network measured near Leyden,

Colorado ................................................................ 27

Figure 3.8 Median, mean, and peak power of computers measured at Plainview Open Space

near Boulder, Colorado .................................................... 28

Figure 4.1 Cumulative distribution functions of the median, mean, and peak values for a 4-day

sequence of measurements at the Lakewood, Colorado residential location .......... 32

Figure 4.2 Cumulative distribution functions of the mean values for two residential locations

............................................................................... 32

Figure 4.3 Cumulative distribution functions of the combined median, mean, and peak values

of Lakewood, Colorado and Boulder, Colorado residential locations ............... 33

Figure 4.4 Cumulative distribution functions of the mean values for six business locations ... 33
vi

Figure 4.5 Cumulative distribution functions of combined median, mean, and peak values for 

three business locations .................................................... 34

Figure 4.6 Cumulative distribution functions of combined median, mean, and peak values for 

three “light urban” locations ................................................ 34

Figure 4.7 Cumulative distribution functions of combined median, mean, and peak values for 

four rural locations ........................................................ 35

Figure 5.1 Class A noise from measurements at Plainview Open Space site near Boulder,

Colorado on November 7, 1996 at 3:11 p.m. ................................... 44

Figure 5.2 Class B noise from measurements at Lakewood, Colorado residence on November 

10, 1996 from 12:00 to 12:40 a.m. ............................................ 45

Figure 5.3 Class B noise from measurements at Lakewood, Colorado residence on November 10,

1996 from 3:30 to 4:00 p.m. ................................................. 46

Figure 5.4 Class B noise from measurements at Lakewood, Colorado residence on November 11,

1996 from 12:16 to 12:46 p.m. ............................................... 47

Figure 5.5 Class B noise from measurements at Boulder, Colorado residence on November 16,

1996 from 12:00 to 12:30 a.m. ............................................... 48

Figure 5.6 Class B noise from measurements at Boulder, Colorado residence on November 17,

1996 from 9:00 to 9:30 a.m. ................................................. 49

Figure 5.7 Class B noise from measurements in office park on November 25, 1996 from 12:00 to

1:00 a.m. ................................................................ 50

Figure 5.8 Class B noise from measurements in office park on November 25, 1996 from 12:00 to

12:30 p.m. ............................................................... 51

Figure 5.9 Class B noise from measurements in office park on November 30, 1996 from 12:00 to

1:00 a.m. ................................................................ 52

Figure 5.10 Class B noise from measurements in office park on November 30, 1996 from 1:00 to

1:30 p.m. ................................................................ 53

Figure 5.11 Class B noise from measurements in downtown Boulder on November 20, 1996 

from 1:00 to 1:30 p.m. ..................................................... 54

Figure 5.12 Class B noise from measurements in downtown Denver on December 3, 1996 from

11:00 to 11:30 a.m. ........................................................ 55

Figure 5.13 Class B noise from measurements in downtown Denver on December 3, 1996 from

11:20 to 11:50 a.m. ........................................................ 56

Figure 5.14 Class B noise from automobiles measured in Clear Creek Canyon, Colorado on 

December 21, 1996 from 1:00 to 1:30 p.m. ..................................... 57

Figure 5.15 Class B noise from automobiles measured in Clear Creek Canyon, Colorado on

December 21, 1996 from 2:00 to 2:30 p.m. ..................................... 58

Figure 5.16 Class B noise from electrical network measured near Leyden, Colorado on

November 12, 1996 at 2:02 p.m. ............................................. 59

Figure 5.17 Class B noise from measurements in office park on November 27, 1996 from 12:20 

to 12:50 p.m. ............................................................. 60

Figure 5.18 Class B noise from measurements in office park on November 27, 1996 from 11:15

 to 11:45 a.m. ............................................................. 61
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viii

TABLES

Table 1. Measured Noise Figure Statistics Compared to CCIR Recommendations at 137 MHz . .

.............................................................................. 31

Table 2. Simulation Parameters for Various Noise Environments ......................... 42
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1The authors are with the Institute for Telecommunication Sciences, National

Telecommunications and Information Administration, U.S. Department of Commerce, Boulder,

CO 80303-3328

MAN-MADE NOISE IN THE 136 to 138-MHz 

VHF METEOROLOGICAL SATELLITE BAND

1Robert Achatz, Yeh Lo, Peter Papazian, Roger Dalke, George Hufford

Satellite radio system performance in the 136 to 138-MHz VHF meteorological satellite band is

compromised by man-made noise external to the receiver. Methods used for predicting man-made

noise power in this band are based on measurements conducted in the 1970's. These methods may

be inaccurate due to technological advances such as quieter automotive ignition systems and the

proliferation of consumer electronic devices such as the personal computer. This report describes

noise power measurements the Institute for Telecommunication Sciences performed in the 136 to

138-MHz VHF meteorological satellite band. Statistics of average noise power were compared to

those of measurements conducted in the 1970's. The noise power measurements were also used to

model instantaneous noise power for simulation of radio links.

Key words: radio channel, man-made noise, impulsive noise, non-Gaussian noise, meteorological

satellite, satellite communications, simulation of communication systems, noise measurement, noise

modeling.

1. INTRODUCTION

In 1974, Spaulding and Disney [1] presented results that summarized many years of measurements

of radio noise - both natural and man-made. From these results they devised methods for estimating

the noise power statistics that are important in the design of radio systems. These methods are

described in the CCIR Reports [2,3] and have been widely used by industry. Figure 1.1, taken from

these reports, presentsthe median antenna noise figure (ameasure ofthe environment’s average noise

power) from 0.1 to 1000.0 MHz. The graph shows that contributions in the 136 to 138-MHz VHF

meteorologicalsatellite band by atmospheric noise (distant lightning) or galactic sources areminimal,

and that man-made noise in business, residential, or rural environments might be significant.

In recent reports, Spaulding [4,5] has warned that the CCIR methods - at least when referring to

man-made noise - may have been made inaccurate by technological advances. For example, newer

automobile ignition systems radiate less noise, but personal computers capable of producing

considerable noise have become ubiquitous in business and residential environments. Thus,

measurement and modeling of man-made noise is timely for the design of VHF meteorological

satellite radio systems [6,7].

We began our noise power measurement and modeling campaign by building a noise power

measurement receiver and writing computer software that digitized and stored noise power samples
2

for later analysis. The receiver and computer were installed in a van that was driven to rural,

residential, and business environmentsfor data collection. We also attempted to isolate noise power

generated by automobiles, the electrical distribution network, and electronic devices.

The data, stored in histograms representing two minutes of noise power samples, were analyzed in

two distinct ways. First, the statistical behavior of each histogram’s average power was analyzed for

rural, residential, and business environments. Second, the first-order statistics of instantaneous noise

power within a histogram were studied. Algorithms capable of generating noise with similar firstorder statistics were then created for simulation of radio links.

This section continues with a review of the terminology used to describe radio noise. Section 2

describesthe noisemeasurement equipment. Section 3 gives an overview of the measurements taken.

Section 4 summarizes the average noise power in rural, residential, and business environments.

Section 5 analyzes first-order statistics of instantaneous noise power and presents a discrete noise

model for simulation of radio links. Section 6 summarizes our findings.

1.1. Background and Terminology

1.1.1 Noise Voltage Representations

A noise voltage is a random function of time whose behavior only can be described statistically. The

time-varying noise voltage, v(t), is represented as a passband signal centered about a carrier

frequency, f

c

,

(1.1)

where Re{} denotes the real part and v^(t) is the noise voltage complex baseband signal centered

about 0 Hz that can be represented in Cartesian or polar form as follows:

(1.2)

where x(t) and y(t) are the baseband signal real and imaginary components, respectively. Both v(t)

and ^v(t) are randomprocesses defined by one or more randomvariables. For example, if v(t) is white

Gaussian noise it is represented by a Gaussian distributed random variable whose mean is zero and

powerspectral density (PSD) isflat. The baseband real and imaginary components of white Gaussian

noise are independent and identically distributed Gaussian random variables with zero means and

flat PSD's. The baseband amplitude and phase of white Gaussian noise are independent but not

identically distributed randomvariables. The amplitude random variable isRayleigh distributedwhile

the phase random variable is uniformly distributed.
3

1.1.2 Instantaneous Noise Power

We define the instantaneous noise power as

(1.3)

In this report the instantaneous noise power is normalized by the average noise power due to blackbody radiation and thermal noise that is present in allradio systems. This average noise power is kT0b

where k = 1.38x10-23 W/Hz/K is Boltzman’s constant, T0 = 288K is the absolute temperature, and

b is the receiver noise equivalent bandwidth.

1.1.3 Statistics of Instantaneous Noise Power

The cumulative distribution function (CDF) of instantaneous noise power describes the probability

that the noise power will not exceed a value 

(1.4)

where WRV is the noise power random variable, w is the noise power independent variable, and p(w)

is the probability density function (PDF) of the noise power random variable. Radio engineers are

concerned with the probabilitythat the noise power will exceed a value. This probability is expressed

as

(1.5)

and is customarily referred to as the amplitude probability distribution function (APD).

For white Gaussian noise, the amplitude PDF, expressed in w, is

(1.6)

the amplitude CDF, expressed in w, is

(1.7)
4

(1.9)

and the APD, expressed in w, is 

(1.8)

In thisreport APD’s are plotted on a Rayleigh probability graph whose axesrepresent the amplitude

in dB above kT0b and the percent oftime the amplitude is exceeded. On a Rayleigh probability graph,

noise with a Rayleigh amplitude distribution forms a straight line with slope -1/2 whose mean lies on

the 37.0 percentile, median lies on the 50.0 percentile, and peak (as defined in this report) lies on the

0.01 percentile. The median of the Rayleigh amplitude distribution is 1.6 dB below the mean while

the peak is 9.6 dB above the mean. Impulsive noise is represented by amplitudes that exceed this line

at low probabilities and continuous wave interference isrepresented by an approximately straight line

with a slope that approaches zero as the continuous wave power to Gaussian noise power ratio

increases.

1.1.4 Average Noise Power

WhiteGaussian noise is completely described by its variance, which is equivalent to the average noise

power. The average noise power is vitally important for non-Gaussian noise also. The average noise

power is the defined as

where E{} denotes the expected value of its argument. The average noise power relative to kT0b is

called the noise factor and is given by

(1.10)

and the noise figure in dB is

(1.11)

1.1.5 Antenna Noise Factors

The noise collected bythe antenna originates, presumably, fromwidely scattered directions at or near

the horizon and istherefore altered by the receiving station antenna directional gain. If S(2,N) is the

power density coming from elevation 2 and azimuth N, and g(2,N) is the antenna directional gain

relative to isotropic, then the total noise power received by an antenna is 
5

(1.12)

where 8 is the wavelength. The corresponding antenna noise factor is 

(1.13)

A noise power measurement system consists of an antenna, antenna matching circuit, transmission

line, and receiver. If the antenna matching circuit and transmission line are assumed to be lossless and

operating at a temperature T0, the measured noise factor is related to the antenna noise factor and

receiver noise factor by

(1.14)

where f is the measured noise factor and f

r

 is the receiver noise factor. The corresponding antenna

noise figure, the principle quantity used by radio system designers, is 

(1.15)

1.1.6 Statistics of Antenna Noise Figure

The statistical behavior ofFa can be shown by plotting the distribution on a normal probability graph

where random variables that are Gaussian distributed form a straight line with a slope equal to its

standard deviation and a median equal to its mean. The graph is used to determine the median

antenna noise figure Fam of a rural, residential, or business environment. Further analysis of F a

includes determining within-the-hour-, hour-to-hour-, and location-to-location-variability.
6

Figure 1.1 Median values of Fa

2. MEASUREMENT SYSTEM DESCRIPTION

The mobile noisemeasurementsystem(NMS) consistsof a computer, receiver, antenna, and vehicle.

This section describes the design and operation of the NMS.

2.1 Vehicle

Noise measurements were conducted at many sites over a wide geographic range in the Denver

metropolitan area. For convenience, all measurements were obtained with the NMS installed in a

vehicle. The vehicle is an extended-length van powered by a diesel engine. An aluminum sheet was

welded to the top of the van to serve as a ground plane for the antenna. Instrument racks were

installed in the rear of the van to hold measurement equipment. 

2.1.1 Vehicle Power Sources

The power to the instruments was supplied directly from a 120-V ac connection or indirectly by a

power inverter. The power inverter produced 120-V ac from the van 12-V dc power system (diesel

engine, alternator, voltage regulator, and batteries).
7

The ac connection was preferred over the power inverter for two reasons. First, the ac connection

could provide continuous powerfor extended measurement periods while the measurements made

using the power inverter were limited in time by the size of the van fuel tank. The power inverter

also added a small amount ofradiated radio frequency interference (RFI)to themeasurementresults.

2.2 Antenna

Man-made noise is expected to arrive at the horizon from widely scattered directions. The effect of

the directional gain on noise power measurements has been of concern [3,8] and has led to

suggestions that there should be a standard measurement antenna. The standard most often

suggested is a simple, short monopole antenna. This is a good choice at frequencies less than

30 MHz; however, at higher frequencies a more effecient quarter-wave monopole is a better choice.

The quarter-wave monopole antenna used for these measurements was mounted on a rectangular

aluminum ground plane attached to the roof of the measurement van. The ground plane dimensions

are 3.6m by 1.6m, or 1.648 by 0.738 at 137MHz where 8 is the 2.19 m wavelength.

The quarter-wave monopole antenna pattern is dependant upon the size and shape of the ground

plane [9]. A quarter-wave monopole antenna mounted on this ground plane will not have a perfectly

omnidirectional antenna pattern; however, the distorted azimuth antenna pattern is not expected to

significantly impact measurements. Of more concern was the elevation antenna pattern. 

Finite-difference time-domain techniques were used to estimate theelevationantennapattern.Figure

2.1 depictsthe elevation antenna pattern foraquarter-wavemonopole antennamounted on a circular

ground plane for three different radii representing the ground plane width (ka = 2.3), length (ka =

5.2), and a point in between (ka = 3.7), where a is the radius and k is 2B/8. The gain is

approximately unity at the horizon for all cases; hence the ground plane size and shape is not

expected to significantly impact the measurements.

Like the antenna pattern, the impedance ofthe quarter-wave monopole antenna depends on the size

and shape of the ground plane. The mounted antenna voltage standing-wave ratio is 1.2 with a

corresponding transmission loss of 0.04 dB. This measurement shows the impedance of the

mounted antenna is well matched to the receiver input impedance.

2.3 Receiver

A custom receiver was built to ensure that wideband noise would not saturate receiver components

and interference from other services would be minimized. The receiver has a noise figure of 2.9 dB

and a center frequency of 137.0 MHz. The receiver could be tuned over the entire 136.0 to

138.0 MHz VHF meteorological satellite band.
8

The single conversion superheterodyne design is composed of rf, IF, and video detection stages.

Figure 2.2 shows all components used in the receiver construction. Appendix A lists specifications

of the components.

2.3.1 The rf Stage

A three-pole Chebyshev preselection filter, F1, with a 4.1-MHz, 3-dB bandwidth rejects the image

frequency and strong out-of-band interferers prior to preamplification. A low-noise, high-gain

preamplifier, A1, establishes receiver sensitivity. After preamplification, a five-pole Chebyshev

image-stripping filter, F2, attenuates noise at the image frequency.

A double balanced mixer driven by a frequency synthesized local oscillator (LO) signal

downconverts the signal to a 10.7MHz IF. A double balanced mixer with a high 1-dB compression

point minimizes the introduction of nonlinear effects. The frequency synthesizer has excellent

frequency stability and low phase noise.

2.3.2 IF Stage

Alow-passfilter(LPF), F3, with a 70-MHz 3-dBcutofffrequency rejectsundesirablemixerproducts

that may overload the first IF amplifier. A 10-dB pad, at the input of F3, ensures that reflected

signals are attenuated.

The first IF amplifier, A2, boosts the signal before the first IF bandlimiting filter. The first IF filter,

F4, has a three-pole Chebyshev response and a 214-kHz 3-dBbandwidth. Reducing the bandwidth

from 4.1 MHz to 214 kHz reduces noise power and adjacent channel interference priorto further IF

amplification. The 10-dB pad before this filter ensures that reflected signals are attenuated.

The next two amplifiers, A3 and A4, and 6-dB pad, P3, were used to adjust IF gain to correspond

to the dynamic range of the logamplifier. The receiver IF gain was designed to measure 16 dB below

and 64 dB above the average receiver input noise power. 

The final IF bandlimiting filter, F5, is a six-pole Chebyshev crystal filter with a 32-kHz 3-dB

bandwidth. This filter was chosen so that all noise measured would be within the APT satellite

frequency allocation [10]. The filter noise equivalent bandwidth is approximately 34 kHz. The 10-

dB pad, P4, before this filter ensures that reflected signals are attenuated.

2.3.3 Video Detection Stage

A log amplifier (LA) with a dynamic range of 80 dB was used for detection. The log amplifier has

a rise and fall time of 700 ns and a 3-dB bandwidth of approximately 6 MHz. The maximum input

signal is 0 dBm and the minimum input is -80 dBm. The maximum signal generates 2.2 V and the

minimum signal generates 0.1 V. The slope of the log amplifier is 26.25 mV/dB. The log amplifier

output is dc coupled so that a continuous wave signal can be used for power calibration.
9

2.4 Data Acquisition

An industrialcomputer housed in ametal,shielded case was used for data acquisition. The processor

and bus speeds were 133 MHz and 66 MHz, respectively. The computer digitized, stored, and

displayed noise power samples and their statistics.

2.4.1 Digitization

The output of the log amplifier was connected to the computer analog-to-digital conversion (ADC)

card. The ADC card sampled the voltage at the log amplifier output 1000 times a second. The ADC

card has 12 bitsthat cover 0-10 volts or 2.442mV/ADCunit. The log amplifier slope is 26.25 mV/dB

therefore our amplitude precision was approximately 0.1 dB.

2.4.2 Noise Histograms

Saving every noise power sample would have required tremendous amounts of computer data

storage. To alleviate this problem, noise power samples were stored in 1000 histogram bins. The

bins had a resolution of 0.1 dB and a range that extended from 26 dB below to 74 dB above the

average receiverinput noise floor. Each noise histogram contained 60,000 noise power samples that

required approximately 60 seconds to digitize, and 50 seconds to develop into a histogram,record,

and display on the screen. 

2.4.3 Graphical User Interface

As the data were collected, two graphs were visible to the user. The first graph showed the recently

acquired noise power samples while the second graph showed the last complete noise histogram.

2.5 Radio Frequency Interference

2.5.1 Radiated Radio Frequency Interference from Out-of-Band Interferers

Thesuperheterodyne receivermay give erroneousresults because ofintermodulation productsfrom

strong out-of-band interferers or failure to properly attenuate image frequency signals. These

possibilities were minimized by the proper selection of rf and IF filters and amplifiers. A spectrum

survey conducted by ITS was examined to determine out-of-band attenuation requirements [11].

Out-of-band interferer- and image-rejection was measured with a continuous wave signal injected

into the receiver input.

2.5.2 Radiated Radio Frequency Interference from NMS

Radiated radio frequency interference (RFI) was found to originate from the computer and power

inverter. To suppress this RFI, Type 43 ferrites were placed on all cables coming out of their metal
10

cases and van doors were closed duringmeasurements. Radiated RFI was measured with an antenna

in a radio-quiet canyon with the computer and power inverter turned on.

2.5.3 Conducted Radio Frequency Interference

Conducted RFI originating from the ac connection or power inverter was attenuated by power line

filtering. Conducted RFI was measured by replacing the antenna with a 50-ohm termination.

Conducted RFI was never observed in any of these measurements.

2.6 Verification

The NMS operation was verified with the following measurements: noise power measured with

50 ohm receiverinput termination,noise power measured with antenna in a radio-quiet canyon, and

continuouswave interference power measured with a continuous wave signalinjected at thereceiver

input.

Figure 2.3 shows median, mean, and peak powers measured with a 50-ohm receiver input

termination over a 24-hour period. This measurement was performed to assure measurement

repeatability over changing temperatures. Figure 2.4 shows an APD during this extended

measurement. This APD indicates that receiver noise is Gaussian.

Figure 2.5 shows an APD obtained with the antenna in a radio-quiet canyon. The van is operating

with its power inverter. The mean of this measurement is less than 1 dB higher than the mean with

the 50-ohm load.

Figure 2.6 shows an APD obtained with a signal generator connected to the receiver input. The

signal generatorinjected a continuous wave signal with 10 dBmore powerthan the receiver average

noise power. The measured APD corresponds to the APD of a Nakagami-Rice distribution with a

K factor of approximately 10 dB as expected.
11

Figure 2.1 Antenna pattern of a quarter-wave monopole antenna over a circular ground plane.
12

Figure 2.2 Receiver block diagram.

Figure 2.3 Median (bottom), mean (middle), and peak (top) receiver noise power over a 24-h period.
13

Figure 2.4 APD of receiver noise power.
14

Figure 2.5 APD of environmental noise in a quiet canyon with power inverter on.
15

Figure 2.6 APD of a continuous wave signal with a 10-dB signal-to-noise ratio.
16

3. MEASUREMENTS

In this section the measurement procedure is described and representative measurements are

summarized.

3.1 Procedure

All measurements were obtained while the van was parked. All doors to the van were closed during

measurementstomaximize shieldingfromradio frequency interference (RFI) generated by theNMS

equipment. If possible, the van was powered with an ac connection; otherwise the van power

inverters were used. After the instruments had warmed up, a series of tests were conducted to ensure

the system was operating properly.

First, the antenna was replaced by a 50-ohm load. The log amplifier output was connected to a

digital oscilloscope to measure the noise power and determine if any conducted RFI was present.

The log amplifier output then was connected to the ADC and one or more noise histograms were

collected for calibration purposes. Next, the antenna was reconnected and the preamplifier output

was connected to the input of a spectrum analyzer to look for interference.

Themeasurements were conducted at the 137.5 MHz APT satellite frequency allocation. We chose

to operate at this frequency so that the measurements would be protected from other satellite

transmitters. We also were able to use the APT signal to verify receiver operation. The satellite was

visible every 12 hours when it would make three passes that were 15 minutes in duration, at 100-

minute intervals. The frequency modulated satellite signal was evident in the noise power

measurement data when mean and median powers rose, converged, fell, and diverged in time

intervals corresponding to the three satellite passes. Figure 3.1a illustrates three satellite passes at

5:30 a.m., 7:15 a.m., and 9:00 a.m.

3.2 Representative Noise Measurements

Representative noise power measurements are summarized graphically by showing the median,

mean and peak (value exceeded 0.01% of time) powers in dB relative to kT0b for each measured

histogram over time. The median, mean, and peak powers are the bottom, middle, and top curves

respectively of these graphs. Noise added by the receiver has not been removed from these values;

therefore, the mean corresponds to the measured noise figure, F. All measurements shown were

taken from September 1996 to February 1997.

3.2.1 Residential Noise Measurements

The first residential site is located in a Lakewood, Colorado, subdivision built in the 1960's. The

nearest business is approximately 1 km away and the nearest majorroad also is approximately 1 km

away. Measurements were obtained over a 4-day period from Friday, November 8, to Tuesday,
17

November 12. Two complete days, Sunday, November 10, and Monday, November 11, are shown

in Figure 3.1.

The two days show similar median, mean, and peak power behavior. The median power was

constant throughout both days. The mean power was fairly constant; however, it was occasionally

affected by the peak power. During both days, the peak power was constant from 20:00 to 08:00 the

next morning; however, during the day the peak power was variable. The drop of the peak power

during the day on Monday, November 11 is dramatic in comparison to Sunday, November 10.

The second residential site is located in a Boulder, Colorado, subdivision built in the 1950's. The

nearest businessis approximately 1 km away and the nearest major road also is approximately 1 km

away. Measurements were taken over a 3-day period from Friday, November 15, to Monday,

November 18. Two days of measurements are shown in Figure 3.2.

Before 7:00 on November 17, median and mean power were fairly constant. After 7:00 the median,

mean, and peak power rose and stayed at the increased level until 10:00 November 18, when the

measurement was stopped. The weather on November 16 was snowy, cold, and cloudy while

November 17 was clear, warm, and sunny.

3.2.2 Business Noise Measurements

Theoffice park consisted ofseveralfour-story officebuildingsinGolden,Colorado,located between

a major interstate highway and a residential area. The electrical distribution lines are buried. Three

extended measurements were conducted at the park. The first measurement location was at the edge

of the office park near an interstate highway and approximately 200 m from the nearest office

building. The measurements began on Friday, November 22, and ended Tuesday, November 26.

Figure 3.3a showsthe resultsfrom Monday, November 25, at this location. At 00:30 median, mean,

and peak powers were very low. The peak power increased slightly until 04:00 with very little effect

on the mean power. From 04:00 until 15:00 the median, mean, and peak power increased slowly.

After 15:00 all three powers decreased.

The next measurement location was in the center of the office park. The nearest office building was

15m from the receiver, a road was within 10m ofthe receiver, and a metal electricalutility closet was

located 4 m from the receiver. The measurements began Tuesday, November 26, and ended Friday,

November 29. Figure 3.3b shows the results from Wednesday, November 27, at this location. The

median and mean power were the highest of the three measurements in the office park. The peak

powerindicatesthe noise was more impulsive in the day than duringthe night. The largest impulses

corresponded to the beginning and end of the work day.

The lastmeasurement location was at the edge of the office park near a residential area. The van was

parked within 15 m ofthe office building and about 50 m from the residential area. A large heating

ventilation and air conditioning (HVAC) motor was located 5 m from the receiver. The

measurements began Friday,November29,and ended Monday,December 2. Figure 3.3c shows the
18

results from Saturday, November 30, at this location. From 00:00 to 06:00 there were some large,

regularly occurring peak values. This regularity was unique in the office park data set and is

conjectured to be due to noise originating from the nearby HVAC motor.

The first downtown site was located at the intersection of 13th Street and Pearl Street in downtown

Boulder, Colorado. This location is surrounded by one- and two- story office buildings and streets

with heavy traffic. The measurement was conducted with inverter power because no ac connection

was available.

The measurement, shown in Figure 3.4a, lasted approximately 3 hours from 11:40 to 14:40

Wednesday, November 20. The peak power was variable while the median and mean were fairly

steady.

The second downtown site included three different locations in downtown Denver, Colorado. The

longest duration measurement was obtained at the corner of 17thandLawrence Street. This location

is characterized by tall buildings and streets with heavy traffic. The measurement was conducted

with inverter power because no ac connection was available.

The measurement, shown in Figure 3.4b, lasted approximately 3 hours from 10:15 to 13:15 on

Tuesday, December 3. Median, mean, and peak power were very similar to those obtained in

downtown Boulder, Colorado. 

3.2.3 Rural Noise Measurements

Rural mountain noise levels were measured in a steep canyon near Ward, Colorado. At the

measurement location there were no visible power lines or houses. A few cars passed by while the

measurement was taken. The measurement, shown in Figure 3.5a, was conducted between 10:00

and 11:00 on Thursday, December 5. The results are characterized by constant median, mean, and

peak power.

Rural plains noise levels were measured in rural eastern Colorado. The location is characterized by

the absence of houses and powerlines. Nearby roads have little or no traffic. The measurement was

conducted with inverter power because no ac connection was available. The measurement, shown

in Figure 3.5b,was conducted on Friday,December 6,from 13:15 to 15:15. The measurement shows

fairly constant median, mean, and peak values. Rural plains values were very similar to rural

mountain values.

3.2.4 Automotive Noise Measurement

Automotive ignition noise was measured on a road passing through Clear Creek Canyon between

Golden,Colorado, and Black Hawk, Colorado. We parked the measurement van along the road at

a place in the canyon with steep walls and no powerlines or buildingsso that we could be reasonably

sure the only noise measured originated from automobiles. The measurements were conducted with
19

inverter power since no ac connection was available. The measurement, shown in Figure 3.6, was

collected on Thursday,November 21, between 13:00 and 15:00. Median and mean values were fairly

constant, while peak values varied.

3.2.5 Electrical Network Noise Measurement

Electrical utilities generate, transmit, and distribute electrical power throughout most business,

residential, and rural areas. The transmission generally is performed at high voltage levels while

distribution of the electrical power to buildings is performed at lower voltage levels. The noise

attributed to the electrical network is generated by corona and gap discharge phenomena and has

been studied for many years [12].

Noise from electrical transmission and distribution was measured on a lightly traveled road located

between Highway 93 and the small town ofLeyden,Colorado. A high-voltage transmission line runs

perpendicular to this road, while a low-voltage distribution line runs parallel to the road. The road

is located in the bottom of a gently sloping valley. The measurements were obtained with inverter

power because no ac connection was available.

Threemeasurements,shown in Figure 3.6, were obtained alongthis road on Tuesday,November 12.

Thefirstmeasurement,obtained directly underthe high-voltage line from 13:30 to 14:00,showed low

mean powers. The second measurement, obtained about 70 m from the high-voltage transmission

line from 14:30 to 15:30, was similar to the first. The third measurement; however, conducted

approximately 2 km from the high-voltage transmission line from 15:45to 16:30,showed amean and

peak power 15 to 20 dB higher than the previous two measurements. The third measurement was

obtained in close proximity to an electrical distribution device mounted on a wooden pole. 

3.2.6 Electronic Equipment Noise Measurement

Electronic equipment has proliferated since similar noise measurements were undertaken in the

1970's. Microprocessors with clock speeds of hundreds of MHz are embedded in many consumer

items, not the least of which is the ubiquitous personal computer. During the measurement

campaign we observed electronic equipment noise that was both broadband and narrowband in

comparison to the bandwidth of the final IF filter of the measurement receiver.

Thenoise from two computers was measured in a relatively quiet area located in the Plainview Open

Space near Boulder, Colorado. The computers tested were placed about 3 m from the receiving

antenna. Three measurements, shown in Figure 3.8, were taken on Tuesday, December 10. First,

noise from a 20-MHz clock speed personal computer was measured from 14:15 to 14:45; next, a

background measurement without computers was obtained from 14:45 to 15:15,and last, noise from

a 50-MHz clock speed personal computer was measured from 15:30 to 16:00.
20

These measurements show that computer noise varies considerably. These spikes in the mean and

peak power of the background measurement were observed at other times at this location. We

assumed it was coincidence that they were not present when either of the two computers was

measured.
21

Figure 3.1a Median, mean, and peak power at Lakewood, Colorado, residence on November 10,

1996.

Figure 3.1b Median, mean, and peak power at Lakewood, Colorado, residence on November 11,

1996.
22

Figure 3.2a Median, mean, and peak power at Boulder, Colorado, residence on November 16, 1996.

Figure 3.2b Median, mean, and peak power at Boulder, Colorado, residence on November 17, 1996.
23

Figure 3.3a Median, mean, and peak power at edge of office park site near interstate highway on

November 25, 1996.

Figure 3.3b Median, mean, and peak power at center of office park site on November 27, 1996. 
24

Figure 3.3c Median, mean, and peak power at edge of office park site near residential area on

November 30, 1996.
25

Figure 3.4a Median, mean, and peak power at downtown Boulder, Colorado, site on November 20,

1996. 

Figure 3.4b Median, mean, and peak power at downtown Denver, Colorado, site on December 3,

1996.
26

Figure 3.5a Median, mean, and peak power at rural mountain site near Ward, Colorado, on

December 5, 1996. 

Figure 3.5b Median, mean, and peak power at rural plains site in eastern Colorado on December 6,

1996.
27

Figure 3.6 Median, mean, and peak power of automobiles measured in Clear Creek Canyon,

Colorado.

Figure 3.7 Median, mean, and peak power of electrical network measured near Leyden, Colorado.
28

Figure 3.8 Median, mean, and peak power of computers measured at Plainview Open Space near

Boulder, Colorado.
29

4. NOISE FIGURE SUMMARIES

In thissectionwe present distributionfunctions ofmedian, mean, and peak noise powers, we describe

and compare the measurement and analysis methods to those used by the CCIR, and we conclude by

contrasting the noise figure results with those used by the CCIR.

4.1 Distribution Functions of Median, Mean, and Peak Powers

We began our noise figure analysis by constructing distribution functions of median, mean, and peak

powers for rural, residential, and business environments. These values were derived from

measurement histograms spaced approximately one hour apart. The distribution functions were

plotted on a normal probability graph where a Gaussian distributed variable is represented by a

straight line whose mean lies on the 50th percentile and slope is the standard deviation. The

distributionfunctionsshowsthe probabilitythat the median, mean, or peak exceeds a particular value.

The mean, in these plots, is the antenna noise figure, Fa, derived from 

(4.1)

where, recalling the notation in Section 1, f is the measured noise factor and f

r

is the receiver noise

factor. In a similar way the median value is 

(4.2)

In this case g representsthe measuredmedian noise power and grrepresentsthe receiver median noise

power. This correction is based on the somewhat dubious approximation [13] that the median of a

convolution isthe sum(or difference) ofthe two component medians. The peak value is uncorrected

and representsthe measured noise power in dB above kT0b that is exceeded 0.01 percent ofthe time.

Figure 4.1 shows the distribution function of Ga, Fa, and peak power over a 4-day period at the

Lakewood, Colorado, residential site described in Section 3. In Figure 4.2 we have plotted the Fa

for both residential sites. Note how the median Fa varies for the two locations. Figure 4.3 shows

the Ga , Fa , and peak power for the two residences combined.

Similarly in Figure 4.4 the distribution functions for the Fa of six business locations are displayed.

There seems to be two populations - a noisy, “business” set and a quieter, “light urban” set. The

center of the office park falls within the business set, while locations adjacent to interstate highways

fall within the light urban set. Figures 4.5 and 4.6 show the Ga, Fa, and peak power for each

population. Only measurements taken during working hours were used.

Figure 4.7 shows the combined data from four rural locations. All of the measurements included in

the distribution were taken during working hours. Cummulative distribution functions for each rural
30

location are not shown because of the short measurement periods involved. While the peak is

considerably less than that observed in other environments, it still exceeds Gaussian noise values.

Thus, even in rural areas, there is impulsive noise.

4.2 Comparison of Measurement and Modeling Methods 

4.2.1 Measurement and Analysis Methods used by CCIR

CCIR methodsfor predicting man-made noise factors are based on approximately 300 hours of noise

measurements at 31 rural-, 38 residential-, and 23 business-environment “measurement areas” [1].

The measurementswere obtained during “mobile runs” through the measurement area, which ranged

in size from a few city blocks for a business environment to several square kilometers for a rural

environment. The mobile run was typically made during working hours and lasted approximately one

hour. Eight frequencies ranging from 250 kHz to 250 MHz were measured simultaneously.

The objective was to estimate each environment Fam defined as the average noise power that can be

expected in 50% of the measurement areas for 50% ofthe time within-the-hour. To accomplish this

objective the mobile runs were sorted according to environment, and the median Fa of each mobile

run was determined. Since the mobile runs lasted approximately one hour this median represented

the hourly median Fa. The hourly median F a values were plotted as a function of frequency, and a

linear regression line representing the environment Fam across the frequency range was determined.

A similar procedure was used to determine the environment within-the-hour upper and lower deciles

of Fa represented by Du and Dl , respectively. 

The standard deviation of the hourly median Fa values from the environment Fam value is defined as

the location variability, FL. The Du and Dl can be combined to represent the within-the-hour time

variability

(4.3)

Finally the composite variability represents the location- and within-the-hour time- variability

(4.4)

Using these parameters, the behavior of Fa can be modeled by 

(4.5)

where yL and yTrepresent location and time deviations, which are zero-mean Gaussian distributions

with standard deviations of F L and F T , respectively.
31

4.2.2 Measurement and Analysis Method Used by this Study

We analyzed approximately 100 hours of noise measurements in 4 rural-, 2 residential-, and 6

business-environment locations. Our measurements were obtained while the measurement van was

parked and only noise at a single frequency was measured. Rural measurement durations were

typically less than an hour, residential measurement durations were often more than 24 hours, and

business measurement durations varied from 1 to more than 24 hours.

Our measurements indicated that Fa changed little within the hour. This is in contrast to the

6.6 dB FT, independent of environment or frequency, reported by Spaulding and Stewart [14]. One

consequence of a negligible FTis that it was not necessary to determine the hourly median Fa of a

location. Instead, sampling Fa once per hour (avoiding satellite passes) issufficient for any location.

The distributions of the sampled Fa are shown in Figures 4.3, 4.5, 4.6, and 4.7. The median of the

sampled Farepresents the median over all hours and locations measured. This median can be

compared to the Fam used by the CCIR methods. The standard deviation of the sampled Fa can be

compared to CCIR method F L since F T was negligable.

4.3 Noise Figure Predictions.

Table 1 shows our measured Fam and F compared to values in CCIR Recommendations. Business

and rural environment Fam fall within one standard deviation of CCIR Recommendations; however,

residential Fam has decreased dramatically and is more than two standard deviations from CCIR

Recommendations. This indicates that residential noise power may have decreased.

Table 1. Measured Noise Figure Statistics Compared to CCIR Recommendations at 137 MHz

Environment Measured

Fam (dB) F (dB)

CCIR Recommendations

Fam (dB) F L (dB)

Business 18.0 2.6 17.6 8.0

Light Urban 8.5 5.8 ---- ----

Residential 6.0 2.9 13.3 2.7

Rural 6.3 1.5 8.0 3.2
32

Figure 4.1 Cummulative distribution functions of the median, mean, and peak values for a 4-day

sequence of measurements at the Lakewood, Colorado, residential location.

Figure 4.2 Cumulative distribution functions of the mean values for two residential locations. 
33

Figure 4.3 Cumulative distribution functions of the combined median, mean, and peak values of

Lakewood, Colorado, and Boulder, Colorado, residential locations.

Figure 4.4 Cumulative distribution functions of the mean values for six business locations.
34

Figure 4.5 Cumulative distribution functions of combined median, mean, and peak values for three

business locations.

Figure 4.6 Cumulative distribution functions of combined median, mean, and peak values for three

“light urban” locations.
35

Figure 4.7 Cumulative distribution functions of combined median, mean, and peak values for four

rural locations.
36

5. AMPLITUDE PROBABILITY DISTRIBUTION ANALYSIS

The effect of man-made noise, in the 136 to 138-MHz VHF meteorological satellite band, on radio

links can be evaluated through simulation. In this section we describe how we used the noise power

measurements to model man-made noise for radio link simulation.

5.1 Middleton Noise Model

To simulate man-made noise, it is desirable to first model the noise with analyticalrepresentations of

the APD using as few statistical parameters as possible (e.g., moments and various measures of

“impulsiveness”). To this end, Middleton [15-18] has published a detailed analysis of statisticalphysical models of man-made and natural radio noise. This work is lengthy and detailed, consisting

of four parts published over a period of more than 4 years.

Middleton’s analysis of non-Gaussian noise is based on the assumption that the noise sources are

Poisson distributed in space and time and that “source waveforms” can have random amplitudes,

durations, and frequencies. In this work, noise is divided into classes based on the interaction of the

time-varying noise voltage and the receiver. Class A noise, composed of Gaussian noise and random

pulses, is defined as having a bandwidth that is significantly smaller than the receiver filter ofinterest

(the finalIF filter for our purposes). With this assumption, the APD of received instantaneous power

w is:

(5.1)

where ( is the mean pulse arrival rate, T is the mean pulse duration, D

2

 is the average pulse power,

and 2F

2

 is the average Gaussian noise power. In this equation the APD depends on the “impulse

index” (T and not explicitly on ( or T. Thus, only three parameters are required to model the ADP

ofClass A noise. Furthermore, the average received power is roughly proportional to (T; hence, the

IF filter should not affect the shape of the APD as long as its bandwidth is large when compared to

the random pulse bandwidth.

Class B noise is defined as having a bandwidth that is larger than the receiver filter of interest. The

resultant APD as calculated by Middleton consists of three components: a Gaussian component, a

rare event component, and an intermediate event component. The Gaussian and rare event

components have the same functional form as Equation (5.1). The intermediate component is much

more complicated and includes an infinite series of confluent hypergeometric functions (M):

(5.2)

where A $is an “intermediate event impulse index”, and $ is known as a “spatial density-propagation

parameter” with the restriction 0 # $ < 2. In addition to the three parameters required for A1 and the
37

two parameters required for A2, another parameter specifying the intersection point for the two

functions must be used. 

Clearly, the implementation of Equation (5.2) in practical simulations is likely to be onerous. In

addition, the determination and implementation of the required six parameters appears to be quite

tedious and as noted by Hagn [19], practical parameter estimation techniques deserve considerable

additional attention. 

Our data represent the noise statistics after the final IF filter in the measurement system. Since the

actual receiver bandwidth may differ from the measurement system, it is desirable to simulate the

noise process priorto the finalIF filter. Determining the parameters that fit Equations (5.1) and (5.2)

to our data does not achieve this end. We are able, however, to use these results as a guide in

developing noise simulation models from our measurements as described below.

5.2 Simplified Noise Model

Asindicated above, we were interested in developing a complex baseband, time seriesrepresentation

of the noise process prior to the finalIF filter of our measurement system. Following Middleton, we

assumed that as observed by the receiver, Class A and Class B noise have a non-Gaussian component

with a randomly distributed time of arrival and a Gaussian component that is always present. The

Gaussian component is modeled as 

(5.3)

where gkisthe Rayleigh-distributed amplitude, 2kisthe uniformly distributed phase, and k isthe time

index.

The non-Gaussian pulse time of arrival was assumed to be Poisson distributed with pulse arrival rate

(. The probability that one pulse will arrive in )t seconds is ()t, therefore, the presence of a pulse

is determined by 

(5.4)

Representations of pulse duration and pulse amplitude differed between Class A and Class B noise.

Prior to receiver filtering, the Class A noise was represented by rectangular pulses, pk, whose

duration corresponded to a bandwidth less than the receiver filter bandwidth. In contrast, prior to

receiver filtering,theClassBnoise wasrepresented bypulses whose bandwidthexceeded the receiver

filter bandwidth.

Class A noise pulse amplitude was characterized by a sudden “step” at low APD exceedence

probabilities when plotted on Rayleigh paper. This suggested that a pulse or group of pulses with

a constant pulse amplitude was present. Class B noise was characterized by a distribution of

amplitudes at low APD exceedence probabilitiesindicating that a group of pulses with variable pulse
38

(5.7)

(5.11)

amplitudes was present. When w >> 1, the asymptotic expansion of the confluent hypergeometric

function in Equation (5.2) yields [ 20]:

(5.5)

Setting $ << 1 and A$ << 1, so that only the first few terms of the series are important, A 2 can be

approximated using a Weibull APD [21,22]:

(5.6)

 where wow and " are the Weibull parameters and

In summary, our complete simplified Class A noise model is 

(5.8)

where B and N are the pulse amplitude and phase. The complete simplified Class B noise model is

(5.9)

where bk is the Weibull distributed amplitude of the non-Gaussian noise component.

The Weibull distributed amplitude is generated by 

(5.10)

where ukis a uniformly distributed random variable with a range from 0 to 1. In a similar way the

Rayleigh distributed amplitude was generated by 

 

where wog is the mean Gaussian power.
39

A number of our measurementsshow that in addition to impulsive noise, there were “constant” noise

sources withbandwidths narrower than our IF filter. The constant noise may originate from periodic,

pulsed emissionsfromnearbyelectricaland electronic equipment. The constant noise component was

characterized by a decrease in the slope of the straight line at high APD exceedence probabilities.

These noise sources were modeled by adding a constant to the Gaussian noise component

(5.12)

where c is a constant. The amplitude of the resulting variate is Nakagami-Rice distributed 

(5.13)

where 

(5.14)

is the ratio of constant noise-power to Gaussian noise-power. When plotted on Rayleigh paper, the

Nakagami-Rice cumulative distribution function is approximately a straight line with a slope that

depends on the K (see e.g., Figure 2.6).

5.3 Extraction of Noise Model Parameters from Measurements

The APD’s used for simulation were composed of several measured histograms from each

environment. Combining histograms was necessary to increase the accuracy of the low exceedence

probabilityestimates. For most of our measurementswog was estimated readily fromthe APD’s 37th

percentile amplitude. For Class A noise two additional parameters were extracted from the APD:

(T and B. For Class B noise, three additional parameters were required for each Poisson/Weibull

process: (T, ", and wow.

The product (T is estimated fromthe APD exceedence probability associated with a departure from

a Rayleigh distribution. The parameter ( was calculated from the product (T after T was measured

or estimated. For Class A noise we assumed T was much greater than the receiver filter time

constant, therefore, the T before and after the receiver filter was the same. For Class B noise, prior

to filtering, the pulse was assumed to be an impulse. After filtering, the pulse duration was estimated

to be the duration of a unit amplitude, rectangular pulse with approximately the same area asthe filter

impulse response. 

The constant amplitude ofClass A noise was read directly fromthe amplitude of the low exceedence

probabilities ofthe APD. The Weibull distribution parameters" and wow of Class B noise are ideally

estimated from the slope and amplitude of the lower exceedence probability events whose event

spacing is much greater than the filter time constant. In practice, for many of the Class B
40

APD’s, there was not sufficient data to measure ". In these cases, " was adjusted empirically to

provide the best fit.

Using the estimated parameters, the simulated time series was passed through a digital

implementation of our noise measurement receiver final IF filter, and the resultant APD was

compared with the measured APD. It was found that, except for wog,severaliterations were required

to determine the optimum parameter values.

5.4 Simulation Results

For our analysis, we selected measurementsthat covered a variety of man-made noise environments.

These APD’srepresent typical examples offirst-orderstatisticsfor a particular measurement location

or environment. From the representative noise measurements in Section 3 only the rural environment

and computer APD’s have been excluded. The rural environment was excluded because it was the

quietest environment. The computer APD was excluded because it was similar to the NakagamiRice APD found in the office park. As indicated above, these Class A and Class B noise parameters

characterize noise before the finalIF filter ofthe measurement system. The simulated APD’s shown

in Figures 5.1 through 5.18 were filtered by a six-pole Chebychev filter with a 34-kHz noise

equivalent bandwidth which approximated our noise measurement receiver filter. A 10-:s time

increment was used. In the following discussion wo = E{w}, Wo = 10log10(wo), Wog = 10log10(wog) and

Wow = 10log10(wow).

Figure 5.1 shows an example of a Class A noise APD. For this simulation, the Class A noise pulse

duration is 1.0 ms, pulse arrival rate is 0.3 pulses/second, and the pulse amplitude is 67.0 dB above

kT0b. Class B noise also is included in this simulation. The Class B noise parameters are " = 1.0,

(= 30.0 pulses/second, and Wog = 7.3 dB, Wow = 27.0 dB, Wo = 33.4 dB above kT0 b. Class A noise

with large amplitudes was observed at many of our measurement sites. The time between Class A

noise events, however, was on the order of hours, and the duration of the events was less than 100

ms. Since our measurements indicated that Class A events are rare and of short duration, the

remainder of our analysis focused on the simulation of the more common Class B noise. 

In Table 2 we have tabulated the simulation parameters for Class B noise corresponding to several

man-made noise locations and sources. Figures 5.2 through 5.16 show the comparison between the

measured and simulated APDsfor each entry in Table 2. Note that in some cases, two non-Gaussian

noise components are required to obtain a suitable APD. This may be the case, for example, when

both strong power line and automotive noise sources are present.

Figures 5.17 and 5.18 show Nakagami-Rice distributed APD’s from the office park measurements.

Electrical or electronics equipment with periodic, pulsed emissions may be a source of constant

narrowband noise. The Class B and Nakagami-Rice parameters for Figure 5.17 are K = 3.0 dB, " =

2.0, (= 0.8 pulses/second, and Wog = 11.0 dB, Wow = 32.0 dB, Wo = 14.5 dB above kT0b. The Class

B and Nakagami-Rice parameters for Figure 5.18 are K = 3.0 dB, " = 2.0, (= 10.0 pulses/second,
41

and Wog = 11.0 dB, Wow = 32.5 dB, Wo = 14.7 dB above kT0b. Figure 3.3b shows the median, mean,

and peak power for this time and location.

5.5 Change of Simulation Time Increment

When performing Class B noise simulations, the average power of the non-Gaussian component

depends on the time increment as follows:

(5.15)

where)t isthe simulation time increment. The tacit assumption in our model is that prior to the final

IF filter, Class B noise can be treated as a series of pulses having a duration less than )t. The )t for

a particular receiver analysis would, of course, be based on the bandwidth of the receiver IF filter.

To determine Wow for a different time increment the average powers of the non-Gaussian processes

are equated at the two time increments, and therefore 

(5.16)

where the prime denotes )t and Wow values at the new time increment.
42

Table 2 Simulation Parameters for Various Noise Environments

Related

Figures

Location Environment

or Source

Date 

mm/dd/yy

Time

hh:mm

(

pulses/s

" Wow dB

relative

to kTo*

Wog dB

relative

to kTo

Wo dB

relative

 to kTo

5.2

3.1a

Lakewood,

Colorado

Residential 11/10/96 12:00 a.m. 

12:40 a.m.

220.0 0.5 31.0 4.6 6.9

5.3

3.1a

Lakewood,

Colorado

Residential 11/10/96 3:30 p.m.

4:00 p.m.

220.0

2.0

0.5

0.5

23.0

62.0

5.1 15.0

5.4

3.1b

Lakewood,

Colorado

Residential 11/11/96 12:16 p.m.

12:46 p.m.

1.5 3.0 43.0 4.6 5.6

5.5

3.2a

Boulder,

Colorado

Residential 11/16/96 12:00 a.m.

12:30 a.m.

30.0 3.0 18.0 3.2 3.4

5.6

3.2b

Boulder,

Colorado

Residential 11/17/96 9:00 a.m.

9:30 a.m.

1500.0

3.0

0.75

1.0

32.0

43.0

5.0 13.5

5.7

3.3a

Office park 

near highway

Light Urban 11/25/96 12:00 a.m.

1:00 a.m.

220.0

4.5

0.5

3.5

19.0

20.0

6.2 6.4

5.8

3.3a

Office Park

near highway

Light Urban 11/25/96 12:00 p.m.

12:30 p.m.

30.0 3.5 19.0 8.3 8.6

5.9

3.3c

Office park 

near

residential

Light Urban 11/30/96 12:00 a.m.

1:00 a.m.

2.0 5.0 25.0 5.7 5.8
Table 2, cont., Simulation Parameters for Various Noise Environments

Related

Figures

Location Environment

or Source

Date 

mm/dd/yy

Time

hh:mm

(

pulses/s

" Wow dB

relative

to kTo*

Wog dB

relative

to kTo

Wo dB

relative

 to kTo

43

5.10

3.3c

Office park 

near

residential

Business 11/30/96 1:00 p.m.

1:30 p.m.

25.0 4.0 22.0 6.6 7.2

5.11

3.4a

Downtown

Boulder,

Colorado

Business 11/20/96 1:00 p.m.

1:30 p.m.

150.0 2.5 30.0 18.0 18.5

5.12

3.4b

Downtown

Denver, 

Colorado

Business 12/03/96 11:00 a.m.

11:30 a.m.

25.0 3.0 34.0 19.0 19.1

5.13

3.4b

Downtown

Denver,

Colorado

Business 12/03/96 11:20 a.m.

11:50 a.m.

60.0 2.5 35.0 19.0 19.4

5.14

3.6

Clear Creek

Canyon,

Colorado

Automotive 12/21/96 1:00 p.m.

1:30 p.m.

25.0 3.0 15.0 5.5 5.5

5.15

3.6

Clear Creek

Canyon,

Colorado

Automotive 12/21/96 2:00 p.m.

2:30 p.m.

15.0 6.0 16.0 5.3 6.3

5.16

3.7

Leyden,

Colorado

Electrical

Network

11/12/96 2:02 p.m. 495.0 0.5 46.0 5.0 22.6

* depends on the time increment of the simulation (see equation 5.15).
44

Figure 5.1 Class A noise from measurements at Plainview Open Space site near Boulder,

Colorado, on November 7, 1996, at 3:11 p.m.
45

Figure 5.2 Class B noise from measurements at Lakewood, Colorado, residence on November 10,

1996, from 12:00 to 12:40 a.m.
46

Figure 5.3 Class B noise from measurements at Lakewood, Colorado, residence on November 10,

1996, from 3:30 to 4:00 p.m.
47

Figure 5.4 Class B noise from measurements at Lakewood, Colorado, residence on November 11,

1996, from 12:16 to 12:46 p.m.
48

Figure 5.5 Class B noise from measurements at Boulder, Colorado, residence on November 16,

1996, from 12:00 to 12:30 a.m.
49

Figure 5.6 Class B noise from measurements at Boulder, Colorado, residence on November 17,

1996, from 9:00 to 9:30 a.m.
50

Figure 5.7 Class B noise from measurements in office park on November 25, 1996, from 12:00 to

1:00 a.m.
51

Figure 5.8 Class B noise from measurements in office park on November 25, 1996, from 12:00 to

12:30 p.m.
52

Figure 5.9 Class B noise from measurements in office park on November 30, 1996, from 12:00 to

1:00 a.m.
53

Figure 5.10 Class B noise from measurements in office park on November 30, 1996, from 1:00 to

1:30 p.m.
54

Figure 5.11 Class B noise from measurements in downtown Boulder on November 20, 1996,

from 1:00 to 1:30 p.m.
55

Figure 5.12 Class B noise from measurements in downtown Denver on December 3, 1996, from

11:00 to 11:30 a.m.
56

Figure 5.13 Class B noise from measurements in downtown Denver on December 3, 1996, from

11:20 to 11:50 a.m.
57

Figure 5.14 Class B noise from automobiles measured in Clear Creek Canyon, Colorado, on

December 21, 1996, from 1:00 to 1:30 p.m.
58

Figure 5.15 Class B noise from automobiles measured in Clear Creek Canyon, Colorado, on

December 21, 1996, from 2:00 to 2:30 p.m.
59

Figure 5.16 Class B noise from electrical network measured near Leyden, Colorado, on

November 12, 1996, at 2:02p.m.
60

Figure 5.17 Class B noise from measurements in office park on November 27, 1996, from 12:20

to 12:50 a.m.
61

Figure 5.18 Class B noise from measurements in office park on November 27, 1996, from 11:15

to 11:45 a.m.
62

6. CONCLUSIONS

Spauldinghypothesized that man-made noise values, asreported inCCIRReport 258,may no longer

be valid because of changes in electrical device technology such as the quieting of automotive

ignition systems. These changes necessitate the measurement and modeling of man-made noise to

determine if this is indeed the case. In this report the 136 to 138-MHz meteorological satellite band

was measured and modelled, since space-to-earth links in this band are impacted greatly by manmade noise. We found that the characteristics of man-made noise in this band have changed, and

we recommend further noise measurements in this band and others. The most striking results were

the lack of within-the-hour variability of Fain all environments, the drop in F am for residential

environments, and the relative quietness of the automobiles.

Graphs depicting the time-varying median, mean, and peak noise powers were presented in

Section 3. The graphs show that within-the-hour variation of Faisrelatively small. CCIR Report 258

provided upper and lower decile values, Du and D l respectively, for within-the-hour variation of F

a

 as a function of frequency and environment. Spaulding and Stewart [14] have analyzed the data

used to obtain Du and Dl and have found it appropriate to use D u = 9.7 dB and D l = 7 dB,

independent of environment or frequency. These decile values correspond to a within-the-hour

standard deviation of approximately 6.6 dB. Clearly our measurements and those used for CCIR

Report 258 differ significantly in within-the-hour variability.

The measured Fam, presented in Section 4, was 18.0, 6.0, and 6.3 dB for business, residential, and

rural environments. CCIR Report 258 gives 17.6, 13.3, and 8.0 dB for the same environments [2].

Only residential Fam has changed appreciably from those values reported by the CCIR. These

findings are significant for radio link designers. The discussion in Section 4 indicates that the CCIR

noise measurement data were collected and analyzed somewhat differently. In particular, (1) the

CCIR noise measurement data were collected during “mobile runs” through a “measurement area”

while the measurements in this report were collected while stationary,(2) CCIR measurement data

contained more location variability but not as much hour-to-hour time variability as the

measurements in this report, and (3) CCIR estimates of Fam were dependent upon its behavior over

8frequencies,whereasthe estimate in thisreport is derived frommeasurementsin a single frequency.

Further measurements and analysis are needed to determine if these changes in measurement and

analysis methods have impacted our conclusions.

Measurements of automobile noise suggest that automobiles are no longer a significant VHF noise

source. In fact, stretches of urban highway were found to be quiet enough to coin the "light urban"

environment classification. For example, Figure 3.6 shows automobile noise along an isolated

mountain canyon road with an Fa of approximately 4 dB and Figure 3.3a shows automobile noise

along an interstate highway adjacent to an office park with an Fa of approximately 7 dB. Spaulding's

measurements[1] predict an Fa of approximately 15 dBforlocations adjacentto interstatehighways.

Power-line noise was noticed throughout the measurement campaign. Our limited measurements

of power transmission- and distribution-lines indicate that this problem still exists. The power-line
63

measurements were conducted in a rural setting on a road perpendicular to a high-voltage

transmission line and parallel to a lower voltage distribution line. Measurements near the highvoltage transmission linedid notshowan unusually high Fa at 136-138MHz. Along the road, farther

from the high-voltage transmission line, yetstill nearthe lowervoltagedistributionline,thenoisewas

found to be highly variable. This variability due to power lines is likely to be experienced in business,

residential, and rural areas.

Computers were found to be capable of generating a significant amount of noise in this band. A

simple experiment was conducted in a ruralsettingwhich documented the noise from two randomly

selected computers. One of the computers was found to be noisy while the other was relatively

quiet. Measurements outside our laboratory indicated that a telecommunication “switch” with an

embedded computer introduced a narrowband continuous tone in the measurement receiver

passband. Further study is needed to determine how narrowband noise power from computers and

other electronic devices within a building would impact a receiving antenna mounted on or near an

office building.

Analysis of noise APD’s revealed a wide spectrum of noise types. Nakagami-Rice interference was

found in several bussiness locations. This interference has a constant component along with a

Gaussian component. Class A pulsed interference (emission bandwidth less than measurement

bandwidth)was measured infrequently in all environments. Generally several hours passed between

Class A interference events. We speculate that Class A noise events may be the result of line spectra

generated from electrical devices.

ClassBpulsed interference (emission bandwidth greaterthan measurement bandwidth)is by far the

most common. Middleton spent considerable effort in modelling this class of noise. Using the ideas

put forward by Middleton, we constructed a simplified noise model dependent upon a smallset of

parameters that were derived from the measurements. Using this approach we were able to simulate

noise with first-order statistics that agreed with our measurements.
64

7. REFERENCES

[1] A.D. Spaulding and R.T. Disney, “Man-made radio noise, part 1:estimates for business,

residential, and rural areas,” Office of Telecommunications Report OT 74-38, Jun. 1974.

[2] CCIR, “Man-made radio noise,” Report 258-5, International Radio Consultive Committee,

International Telecommunications Union, Geneva, Switzerland, 1990.

[3] CCIR, “World distribution and characteristics of atmospheric radio noise,” Report 322,

International Radio Consultive Committee, International Telecommunications Union,

Geneva, Switzerland, 1964.

[4] A.D. Spaulding, “The roadway natural and man-made noise environment,” IVHS Journal,

2, pp. 175-211, 1995.

[5] A.D. Spaulding, “The natural and man-made noise environment in personal communications

services bands,” NTIA Report 96-330, May 1996.

[6] N.G. Riley and K. Docherty, “Modeling and measurement of man-made radio noise in the

VHF-UHF Band,” Proc. Of the Ninth Internat. Conf. On Ant. Prop., vol. 2. Pp. 313-316.

[7] S.N. Murthy and G. Krishnamraju, “interference to low earth orbit satellite (LEOS) services

in VHF band from ground based emissions,” IETE Technical Review, 12, pp. 325-329, 1995.

[8] G.H. Hagn, “Selected radio noise topics,” SRI International Final Report, Project 45002,

Contract No. NT83RA6-36001, 1984.

[9] W.M. Weiner, S.P. Cruze, C. Li, and W.J. Wilson, “ Monopole Elements on Circular Ground

Planes,” Norwood, MA: Artech House, 1987.

[10] NOAA, “The Tiros-N/NOAA A-G Satellite Series,” NOAA Technical Memorandum NESS

95, Aug. 1979.

[11] F.H. Sanders and V.S. Lawrence, “Broadband Spectrum Survey at Denver, Colorado,” NTIA

Report 95-321, Sept. 1995.

[12] V.L. Chartier, “The location, correction and prevention of RI and TVI sources from overhead

power lines,” IEEE 76-CH1163-5-PWR, pp. 12-19, 1976.

[13] P.L. Rice, A.G. Longley, K.A. Norton, and A.P. Barsis, “Phase interference fading and

service probability,” Annex V in vol.2 of Transmission loss predictions for tropospheric

communication circuits, NBS Technical Note 101, 1966.
65

[14] A.D. Spaulding and F.G. Stewart, “An updated noise model for use in IONCAP,” NTIA

Report 87-212, Jan. 1987.

[15] Middleton, D. “Statistical-physical models of man-made and natural radio-noise

environments–Part I: First-order probability models of the instantaneous amplitude,” Office

of Telecommunications Report OT 74-36, April 1975.

[16] Middleton, D. “Statistical-physical models of man-noise and natural radio-noise

environment–Part II: First-order probability models of the envelope and phase,” Office of

Telecommunications Report OT 76-86, April 1976.

[17] Middleton, D. “Statistical-physical models of man-made and natural radio-noise

environments–Part III: First-order probability models of the instantaneous amplitude of

Class B interference,” NTIA Contractor Report 78-1, June 1978.

[18] Middleton, D. “Statistical-physical models of man-made and natural radio-noise

environments–Part IV: Determination of the first-order parameters of Class A and Class B

interference, NTIA Contractor Report 78-2 Sept. 1978.

[19] G.H. Hagn, “Man-made radio noise and interference,” in Proc. Of AGARD Conf. No. 420,

Lisbon, Portugal, Oct. 26-30, 1987, pp. 5-1 to 5-15.

[20] M. Abramowitz and I.A. Stegun, Handbook of Mathematical Functions, NBS Applied Math

Series 55, New York, NY: Dover Pub., 1964.

[21] W. Weibull, “A statistical distribution function of wide applicability,” J. Appl. Mech., 18, pp.

293-297, 1951.

[22] M.C. Jeruchim, P. Balaban, and K.S. Shanmugan, Simulation of communication Systems,

New York, NY: Plenum Press, 1992.
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APPENDIX A: RECEIVER SPECIFICATIONS SUMMARY

This appendix summarizes the noise measurement receiver component and system

specifications.

A.1 Receiver Description

Design: Single Conversion Superheterodyne

Detection: Log video

Radio frequency range: 136 - 138 MHz

Intermediate frequency: 10.7 MHz

Local oscillator range: 125.3- 127.3 MHz

Image frequency range: 114.6 to 116.6 MHz

Predetection 3-dB bandwidth: 32 kHz

A.2 Component Specifications

The acronyms in front of each component specification are used in Figure 2.2.

ANT, antenna: quarter-wave monopole mounted on center of van roof mounted ground plan 3.6

m long and 1.6 m wide

F1, bandpass filter: 3-pole Chebychev, 0.1-dB ripple, 4.1 MHz 3-dB bandwidth, 0.3-dB loss

A1, amplifier: 37-dB gain, 8-dBm 1-dB compression point, 1.1-dB NF

F2, bandpass filter: 5-pole Chebychev, 0.1-dB ripple, 4.1 MHz 3-dB bandwidth, 1.7-dB loss

M, mixer: 8-dB conversion loss, 14 dBm (input) 1-dB compression point

LO, Hewlet Packard HP8662 frequency synthesizer: +15 dBm output power

P1, attenuator: 10 dB

F3, low pass filter: 70 MHz 3-dB cutoff frequency, 0.0- dB loss

A2, amplifier: 22-dB gain, 9 dBm 1-dB compression point, 5.3 dB noise figure

P2, attenuator: 10 dB

F4, bandpass filter: 3-pole Chebychev, 0.1-dB ripple, 214 kHz 3-dB bandwidth, 9.5-dB loss

A3, amplifier: 28-dB gain, 18 dBm 1-dB compression point, 7-dB noise figure

P#, attenuator: 6 dB

A4, amplifier: 34-dB gain, 21 dBm 1-dB compression point, 5-dB noise figure

P4, attenuator: 10 dB

F5, bandpass filter:; 6-pole Chebychev, 1.0-dB ripple, 32 kHz 3-dB bandwidth, -1.8 dB loss

LA, log amplifier: 0.1 to 2.1 V, -80 to 0 dBm

ADC, digitizer: 0-10 V, 12 bit, 2.44 mV/ADC unit
68

A.3 System Specification

All system power specifications are the power at the receiver input.

Gain (measured from receiver input to log amplifier input): 63.0 dB

Noise figure: 2.9 dB

Predetection noise equivalent bandwidth: 34.2 kHz

Average noise power: 126 dBm in 34.2 kHz bandwidth, -171 dBm in 1 Hz bandwidth

1-dB compression point: -50 dBm

Two-tone test: 136.999 (f1) at -66 dBm combined with 137.001 (f2) at -66 dBm, -63 dBm combined

input power, third order output (2f1-f2,2f2-f1): 136.997 and 137.003 are 42 dB down, fifth order

output (3f1, 2f2, 3f2-2f1): 136.995 and 137.005 are 65 dB down

Log amplifier dynamic range (0.1 to 2.1 V): -143 dBm to -63 dBm in 3.2 kHz bandwidth, 

-188 dBm to -108 dBm in 1 Hz bandwidth

Image frequency rejection:> 100 dB