NTIA Report 00-371

Radio Link Performance Prediction

via Software Simulation

E.A. Quincy, R.J. Achatz

M.G. Cotton

M.P. Roadifer

J.M. Ratzloff

u.s. DEPARTMENT OF COMMERCE

William M. Daley, Secretary

Bernadette McGuire-Rivera,

Acting Deputy Assistant Secretary

for Communications and Information

October 1999

CONTENTS

Page

1. INTRODUCTION 1

2. RADIO LINK MODELING 3

2.1 Channel Model 4

2.2 Transmitter and Receiver Models 8

3. EFFECT OF RADIO LINK ON BIT ERRORS 10

4. EFFECT OF BIT ERRORS ON SPEECH AND IMAGE INFORMATION 14

4.1 Information Formats 14

4.2 Subjective Quality Rating 15

4.3 Effect of Signal-to-Interference Ratio on Speech and Image Quality 18

5. CONCLUSION 22

6. REFERENCES 23

APPENDIX A-I

111

RADIO LINK PERFORMANCE PREDICTION

VIA

SOFTWARE SIMULATION

Edmund A. Quincy', Robert J. Achatz',

Michael G. Cotton', Michael P. Roadifer, and Jeanne M. Ratzloff'

The subjective quality of speech and image infonnation, transmitted over a high

frequency radio link impaired with varying levels ofinterference, has been evaluated

using software simulation. The high frequency radio link was also degraded by

frequency-selective multipath and non-Gaussian noise. DUring radio link signal

simulation an error sequence, detennined from a comparison of transmitted and

received bits, was collected. Next digitized speech and image infonnation was

distorted by the error sequence. Last, the quality ofthe distorted speech and image

infonnation was subjectively evaluated. This process was repeated for a large number

ofinterference conditions. The same process can be used to show how multipath and

non-Gaussian noise affects speech and image quality. Other wireless applications

such as personal communications services and wireless local area networks can be

analyzed in the same way using radio channel measurements and models made by

the Institute for Telecommunication Sciences.

Keywords: Digital modulation, radio link, radio channel, multipath, non-Gaussian

noise, interference, bit error, speech coding, image coding, speech quality, image

quality, software simulation.

1. INTRODUCTION

Digitally modulated signals, carrying speech and image infonnation, are often transmitted over radio

links whose radio channels are degraded by non-Gaussian noise, time-varying multipath, and

interference. These radio channels are found, for example, in applications as diverse as high

frequency (HF) radio, land mobile radio (LMR), personal communications services (PCS), and

wireless local area networks (WLAN's). In this study, a flexible and efficient two-step software

simulation process is described that determines how radio channel impainnents affect the quality of

speech and image infonnation. As an example, this process is used to evaluate the perfonnance of

several HF radio links subjected to varying levels ofinterference. This process may also be used to

evaluate the perfonnance ofradio links subjected to varying levels ofmultipath and non-Gaussian

nOIse.

'The authors are with the Institute for Telecommunication Sciences, National

Telecommunications and Information Administration, U.S. Department ofCommerce, Boulder, CO

80303.

2The author was with the Institute for Telecommunication Sciences when this work was done.
The Institute for Telecommunication Sciences (ITS) is interested in this type of performance

prediction for two reasons. First, ITS has a long history ofradio channel measurement and modeling

achievement. Radio channel impulse response, noise, and interference measurements have been

made at ITS at many frequencies, for many applications, for over 30 years. ITS is interested in using

the results of these measurements to develop new signal processing and information coding

techniques that use the spectrum more efficiently. Second, the number of radio link software

simulation environments (RLSSE's) has increased dramatically. Incorporating the radio channel

measurements into these packages is an ideal way for ITS to assist industry and government in

designing and acquiring robust, radio products.

Software simulation is ideal for radio link performance prediction because it is repeatable and allows

investigation ofan isolated radio link parameter. In this study, software simulation is broken into two

distinct steps as shown in Figure 1. In the first step, the transmission and reception of a digitally

modulated signal over an impaired radio channel is simulated within the RLSSE. The output ofthe

first step is the bit errorsequence derived from a comparison between transmitted and received bits.

In the second simulation step, the transmission and reception ofinformation bits is simulated outside

the RLSSE. Here, speech and image information in various information formats such as pulse code

modulated (PCM) speech, compressed PCM speech, and PCM image is distorted. The distorted

information is finally played back for quality assessment. A quality measure is then used to

determine the effect an impaired radio channel parameter has on speech or image quality.

- - - - -1- - - - -I

1

1

Sequence

Radio

Infonnation Channel

Impainnent Fonnats

Radio Link Radio Link Quality Signal Bit f--r-+ Play Back r---.

Simulation 1 Bit Error Simulation 1 Assessment

1 1

r---------

I

I

I

I

I

I

I

I

I

I

I

I

I

I

I

I

1

1 1

1

1

STEP I

I

STEP 2

Figure 1. Radio link performance prediction.

Signal distortion during the first simulation step requires a large number ofmathematical operations

because there are many components (e.g. modulators, filters, detectors) and effects (e.g. multipath,

interference, noise) that need to be included and because the signal is sampled at a high rate to

minimize distortion: In contrast, information distortion during the second simulation step only

requires a single operation executed one time per bit. There is no savings in computation time when

the transmission of a single information format is simulated; however, computation time decreases

dramatically as the number ofinformation formats increases. The decrease in total computationtime,

when analyzing the effect ofradio channel impairments on numerous information formats, is the

primary motivation for breaking the simulation into two steps.

2
RX

BITS

This report is divided into five sections. The first and last are introductory and conclusion sections

respectively. The second section discusses radio link modeling. The third section describes the first

step where signal distortion is simulated and error sequences are collected. The fourth section

describes the second step where speech and image infonnation distortion is simulated and the quality

ofthe received infonnation is evaluated.

2. RADIO LINK MODELING

Figure 2 shows a simplified block diagram of a typical radio link. The blocks ofthe radio link are

broken into three main sections: transmitter, channel, and receiver. The source block (TX BITS)

generates the transmitted bit sequence which is then mapped into a sequence of discrete symbol

values (BIT-TO-SYM). The modulator (MOD) translates the discrete symbol values to a bandlimited

signal which can be transmitted over the radio channel (CHAN). The radio channel adds noise and

interference and introduces multipath. The demodulator (DEM) and detector (DET) extract the

bandlimited symbols from the received signal. The bandlimited symbols are finally sampled (SMP),

quantized (DEC), and converted (SYM-TO-BIT) to the received bit sequence.

Radio

Channel

Impainnent

·----------1 r-------------------------~

.-----,1 1 1 Ir---"'"

TX I BIT . 1 1 SYM I

BITS TO .. MOD +CHAN.... DEM ... DET r-. SMP r-. DEC f+ TO -t 1 SYM 1 1 BIT 1 '---_..... 1 I 11...-_-1 ....._---' 1- 1 I

Transmitter Channel Receiver

Figure 2. Radio link block diagram.

An HF radio link was simulated for this study. The HF radio channel is characterized by multipath

and non-Gaussian noise. High frequency transmitters typically use frequency-shift keying (FSK) and

differential phase-shift keying (DPSK) modulation to avoid requiring coherent detection in the

receiver. The baud rate is typically low to avoid problems with frequency-selective multipath.

Specialized HF radio links included in this study include a voice frequency carrier telegraphy

(VFCT) link and a link with interleaving combined with block forward error correction (FEC). Voice

frequency carrier telegraphy is a modulation method used in HF radio links to increase the baud rate

by increasing the number ofsimultaneously modulated carrier frequencies. Interleaving combined

with FEC can be used to mitigate burst errors introduced by time-varying multipath. Transmitter,

channel, and receiver models used in the simulations of the HF radio link are discussed in this

section.

3
2.1 Channel Model

Signals transmitted through the HF radio channel are distorted by multipath, non-Gaussian noise,

and interference. The multipath is due to reflections off ofionospheric layers. The non-Gaussian

noise is the result of atmospheric lightning and man-made electrical noise. The interference can be

caused by intentional jamming or spurious emissions from adjacent bands.

Figure 3 shows a block diagram ofthe radio channel. The transmitter power amplifier is included

in the channel since its main function is to provide gain to overcome channel attenuation. The

transmitted signal is convolved with the multipath filter and added to the noise and interference

signals.

Amplifier ... Multipath

--.

+

Non-Gaussian I--

Noise

Gaussian

Noise

Interference

Figure 3. Channel model.

4
2.1.1 Multipath

The time-varying multipath channel is represented by

N

h (t ,'r:) = LPn( t) 0('t - 'tn)

n=l

(1)

where N is the number of paths, Pn (t) is the complex path gain, and 't' is the path delay.

Implementation ofthe multipath model is illustrated with the block diagram shown in Figure 4.

Path 1 f--

Path 2 r--

·

·

· L--..+

PathN +

Figure 4. Multipath model.

Figure 5 shows how Pn (t) values are generated in the RLSSE. White noise is first generated by a

zero mean, unit variance, complex-Gaussian random number generator. This noise, which has

Rayleigh distributed amplitudes and uniformly distributed phases, is then amplified and filtered with

a 2-pole Butterworth low pass filter

Pn(t) = A b(O *g(t) (2)

where bet) is the white noise signal, A is a power scaling factor, and get) is the filter impulse

response.

5
,--------------------------------------------

I

I I

I

X I I Delay

Complex Power Bandwidth Gaussian Scaling Filter Noise

Figure 5. Path model.

The multipath model parameters used in this study are derived from HF radio channel measurements

performed by ITS [1]. The measurements were made at 9 MHZ between Boulder, Colorado and

Long Branch, Illinois. Three distinct paths were determined from the measurements. These paths

correspond to a single hop offthe ionospheric E layer (IE), a single hop offthe ionospheric F2layer

(IF), and two hops offthe F2layer (2F). The E and F2 layers are 80 and 300 km above the earth ", surface respectively. The parameters for each path are depicted in Table 1.

Table 1. HF Multipath Parameters

Path Relative Relative Bandwidth

Delay (us) Power (dB) (Hz)

IE 40 -1.2 0.0272

IF 290 -7.2 0.144

2F 1140 -13.5 0.344

The relative path delay is the path time delay in excess ofthe direct (i.e. great circle) path time delay,

the relative path power is the ratio ofthe average power in a single path to the average power in all

paths, and the path bandwidth is the 3-dB bandwidth ofthe path power spectral density. Referring

to Equation 1, 't corresponds to the path delay, the variance ofPnet) corresponds to path power, and

the 3-dB bandwidth ofget) corresponds to the path bandwidth.

6
2.1.2 Gaussian Noise

The Gaussian noise, n(t), is modeled as a signal whose amplitude is Rayleigh distributed

fen) = (3)

,

where no

2 is the average Gaussian noise power equal to kTob where k is the Boltzman constant, To

is the temperature in K, and b is the noise equivalent bandwidth, and whose phase is uniformly

distributed

where eis the phase in radians.

2.1.3 Non-Gaussian Noise

f(8) = 1

21t (4)

The non-Gaussian noise is modeled as identically distributed and independent wideband pulses with

random amplitudes, phases, and time intervals [2]. This type ofnon-Gaussian noise is often called

impulsive noise. The pulse amplitude is described by the Power-Rayleigh distribution

fer) = (5)

where r is the pulse amplitude, rois a number that adjusts the average non-Gaussian noise power,

and CG controls the shape of the amplitude distribution. Setting CG to 1.0 yields an exponential

amplitude probability density function (PDF) whereas setting CG to 2.0 yields a Rayleigh amplitude

PDF. The phase ofthe pulse is uniformly distributed.

The time interval between pulses is determined by the Poisson distribution which describes the

probability of k pulses occurring in t seconds

7
(6)

where A. is the average pulse rate. The corresponding probability of a single pulse occurring in a

simulation time increment is

(7)

where !it represents the simulation time increment.

Non-Gaussian HF noise parameters are derived from HF noise measurements performed by ITS and

published in CCIR Report 322 [3]. The measurements indicated that hourly median environmental

noise power (on a winter day, between 8 and 12 a.m., in Illinois) at 9 MHZ was 30 dB above kTob.

Second-orderstatistics describing the time interval between impulses were not measured. Parameters

used in this study to simulate the non-Gaussian noise are shown in Table 2.

Table 2. HF Non-Gaussian Noise Parameters

Non-Gaussian noise power 30 dB above kTob

Power-Rayleigh distribution ex 1.0

Poisson distribution A. 1/!it pulses/second

2.1.4 Interference

The interfering signal, unless otherwise noted, has the same modulation, baud rate, and symbol

timing as the desired signal. The interfering signal data sequence is not correlated with the desired

signal data sequence and the interfering signal is not distorted by the ionospheric radio channel.

2.2 Transmitter and Receiver Models

Simulated radio links include 2-, 4-, and 8-FSK, 2-, 4-, and 8-DPSK, 2-FSK with FEC and

interleaving, and 6-tone, 2-FSK VFCT. Table 3 summarizes the radio links and their corresponding

baud and effective bit rates. The baud rate, R, is the symbol transmission rate and the effective bit

rate is the information transmission rate. Forward error correction decreases the effective bit rate in

comparison to R by introducing parity bits. Four and eight-level modulationsincrease the effective

bit rate in comparison to R by transmitting more than one bit per symbol. Voice frequency carrier

telegraphy increases the effective bit rate in comparison to R by transmitting symbols on several

carrier frequencies simultaneously.

8
Table 3. HF Radio Link Rates

HF Radio Link R (symbols/second) Effective Bit Rate

(bits/second)

2-FSK 100 100

2-FSK with FEC 100 47

4-FSK 100 200

8-FSK 100 300

2-DPSK 100 100

4-DPSK 100 200

8-DPSK 100 300

2-FSK VFCT 100 600

2.2.1 Frequency-Shift Keying

Simulations of 2-, 4-, and 8-FSK were executed with a non-coherent, matched filter receiver.

Frequency-shift keying is represented in complex baseband notation by

00

set) = A L pet -m T)ei2rtmj.d(t)-(M-I)12)t

m=O

(8)

where A is the signal amplitude, p(t) is the pulse shape, mf is the frequency modulation coefficient,

d(t) is the pulse amplitude modulated (PAM) data sequence, and M is the alphabet size of2, 4, or 8.

For FSK simulations in this study p(t) is rectangular, mf is 100 HzN, and d(t) is defined by

d(t) = amII(t -m T - T/2) (9)

where am is the symbol value 0,1,...,M-1 and TI(t) is the rectangular function that is 1 from -T/2 to

T/2 and aotherwise.

2.2.2 Frequency-Shift Keying with Forward Error Correction and Interleaving

Simulations of2-FSK were also conducted with FEC and interleaving. It has been demonstrated that

a combination of Bose-Chaudhuri-Hocquenghem (BCH) FEC and interleaving is effective in

mitigating the effects oftime-varying HF radio link multipath [4]. A BCH (15,7) FEC capable of

correcting 2 errors in each IS-bit codeword and a block interleaver with 32 rows and 15 columns is

9
used. In theory, this combination ofFEC and interleaving could negate the effects of an isolated 64

symbol error burst.

At the transmitter the symbols are FEC coded and interleaved while at the receiver the symbols are

deinterleaved and corrected. The interleaver reads the bits in row-by-row and reads them out

column-by-column. The deinterleaver reverses this process, that is, the received bits are read into

the deinterleaver column-by-column and read out row-by-row. The symbol rate is not increased to

compensate for the 7/15 code rate. Consequently the effective bit rate is reduced to approximately

47 bits/second.

2.2.3 Differential Phase-Shift Keying

Simulations of 2-, 4-, and 8-DPSK were executed with a differentially-coherent, matched filter

receiver. DPSK is represented in complex baseband notation as

00

jmd(t) s(t) = A L; p(t -m T)e P

m=O

(10)

where mp is the phase modulation constant. To differentially encode, the phase ofthe current symbol

is added to the phase ofthe previous symbol. To differentially decode, the phase ofthe previous

symbol is subtracted from the phase ofthe current symbol. For DPSK simulations in this study p(t)

is rectangular and mp is 21t/M.

2.2.4 Voice-Frequency Carrier-Telegraphy

Voice-frequency carrier-telegraphy [5] is implemented with six, 2-FSK signals operating

simultaneously at evenly spaced carrier frequencies (or tones). Each 2-FSK signal is operated at 100

baud; therefore the effective bit rate is 600 bits/second.

The spacing between the 2-FSK signals is 400 Hz. Four pole, Chebychev filters with a 400 Hz

bandwidth and I-dB passband ripple are used to reduce interference between adjacent 2-FSK signals.

The interfering signal is provided by a broadband noise generator that transmits a complex impulse

with unit amplitude and a phase that randomly flips between 0 and 180 degrees every simulation

time increment.

3. EFFECT OF RADIO LINK ON BIT ERRORS

The first simulation step works at the signal level as shown in Figure 1. Components such as

modulators, filters, and detectors and effects such as multipath, non-Gaussian noise, and interference

are accounted for during signal simulation. Signal simulation is run once for each radio link and

channel impairment condition. In this study, each interference level corresponds to a unique channel

impairment condition.

10
Radio link signal simulation is performed with a commercial, off-the-shelfRLSSE. This tool allows

the user to manipulate transmitter, channel, and receiver models using a graphical user interface. The

RLSSE provides useful graphical information including in-phase and quadrature-phase (I/Q)

constellation diagrams, spectrum magnitude plots, and signal eye diagrams. Figure 6 shows the

computer screen ofthe RLSSE when 2-FSK performance is limited by interference. The interference

is added after the block representing the HF radio channel (labeled "hfchan04"). The interfer~nce

causes spectrum distortion and additional discrete eye diagram traces. Figure 7 shows the RLSSE

screen when 2-FSK is limited by non-Gaussian noise. The noise severely distorts the spectrum and

causes a confusion of eye-diagram traces.

In Figures 6 and 7, frequency is normalized by the symbol rate yielding units ofcycles/symbol while

time is normalized by symbol period yielding units ofsymbols. Conversion to SI units is possible

by multiplying normalized frequency by symbol rate and multiplying normalized time by symbol

period. Signal-to-noise ratio (SNR) represents the ratio of average signal power to average Gaussian

noise power. Non-Gaussian noise power is not included in SNR. Signal-to-interference ratio (SIR)

represents the ratio ofthe average desired signal power to average interfering signal power.

The bit error sequence is determined in the RLSSE by

(11)

where EE> is the "exclusive or" logical operation, dt represents the transmitted bits, and dr represents

the received bits. Figure 8 illustrates the radio link signal simulation process that generates the bit

error sequences. The delay block represents the delay involved in transmitting, propagating, and

receiving the transmitted bit sequence. Figure 9 shows how a bit error sequence is generated from

transmitted and received bit streams. Correct bit detection results in a 0 while incorrect bit

detection results in a 1.

11
Figure 6. Computer screen from RLSSE showing interference limited performance

for 2-FSK. Top figure shows eye diagram and I/Q plots while bottom

screen shows eye diagram and spectrum magnitude (in dB) plots.

Simulation parameters are SNR=80 dB, SIR=10 dB, and BER=6.5e-2.

12
/

Figure 7. Computer screen from RLSSE showing non-Gaussian noise limited

performance for 2-FSK. Top figure shows eye diagram and I1Q plots

while bottom screen shows eye diagram and spectrum magnitude (in dB)

plots. Simulation parameters are SNR=30 dB, SIR=50 dB, and

BER=2.4e-2.

13
Delay

dt

TX 1---+ --+ CHAN --+ RX f--+ RX I~ EB -4Archive

BITS TX BITS dr

Figure 8. Radio link signal simulation and bit error sequence collection.

4. EFFECT OF BIT ERRORS ON SPEECH AND IMAGE INFORMATION

The second simulation step works at the bit level as shown in Figure 1. Bit level simulation can be

performed directly from simulated bit error sequences or from a statistical bit error model (which

can be derived from simulated bit error sequences). The Gilbert model [6] is an example of a

statistical bit error model. Both approaches should yield the same first and second order statistics.

. First order statistics include the average bit error rate while second order statistics include the

probability of consecutive bit errors. In this study the bit error sequences collected during the first

simulation step are used.

Bit level simulations need to be run once for every information format. The bit error sequence

distorts the speech and image information by

ir = itffie (12)

where it is the undistorted information and ir is the distorted information predicted to be present at

the output ofthe receiver. This process is shown diagrammatically in Figure 10.

4.1 Information Formats

A speech sample from the TIMIT standard source, distributed by the National Institute for Standards

and Technology, is used to evaluate speech quality. The 3.4 second sample, recorded at 16 kilosamples per second (ksps) with 16-bit resolution, contains the sentence "Rob sat by the pond and

sketched stray geese." The sample was decimated to 8 ksps and 8-bit resolution to produce "toll

quality" 64 kilo-bits per second (kbps) PCM speech. The 64-kbps PCM speech was also compressed

by the U.S. Federal Standard 1016, 4.8-kbps code excited linear prediction (CELP) vocoder [7]

algorithm. Corresponding file sizes are 27.2 kilobytes (kB) for 64-kbps speech and 2 kB for

compressed PCM speech.

A U.S. Geological Survey topographic map, "scanned" into the computer and saved in bit-map file

format (filename.bmp), was used to evaluate image quality. Bit-map file formatting information,

14
sometimes referred to as "header" information, was assumed to be transmitted error free. The image

has easily identifiable features such as shading, sharp lines, symbols, and text. The resulting PCM

image sample is 320 pixels/line by 200 lines. Each pixel had one of 16 possible gray shades

represented by 4 bits. The PCM image file size is 32 kB.

4.2 Subjective Quality Rating

Speech and image quality ratings presented in this study are based on the opinions oftwo people.

These subjective quality ratings are expressed in terms ofthe typical opinion class scale ranging

from 1 to 5. Class 1 is completely undecipherable, class 2 is marginally useful, and class 5 is free

from distortion. Speech degradation is characterized by "pops", "hisses", "burps", and "warbles"

while image degradation is characterized by "dots", "snow", "dashes", and "streaks."

PCM speech quality was evaluated by playing the distorted speech file through an 8-bit personal

computer sound card. Compressed PCM speech was converted to PCM speech before its quality was

evaluated. Auxiliary sound card software was used to display the time-varying voltage associated

with each speech "waveform." The speech waveform is useful for qualitative comparisons of

degraded and clear speech.

Figures 11 and 12 show "marginal" and "best" speech waveforms for PCM speech and compressed

PCM speech, respectively. The top waveform of each figure represents class 2 while the bottom

waveforms represent approximately class 4. The ordinate ofthe speech waveforms, corresponding

to the amplitude ofthe demodulated signal, extends from -127 to 127 analog-to-digital conversion

units. The speech waveforms show approximately 1.5 seconds ofthe speech segment. Although both

class 2 examples are intelligible, their speech waveforms show clear qualitative differences. PCM

speech errors in these waveforms have a much larger amplitude variation than compressed PCM

speech errors. Also compressed PCM speech errors occur in bursts while PCM speech errors appear

to be more randomly distributed in time.

PCM image quality was evaluated on a high resolution computer screen. Figure 13 shows marginal

and best images. The top image represents class 2 while the bottom image represents approximately

class 4. The class 2 image text is illegible; however, map shading and contour lines are still

decipherable. Examples of"marginal" and "best" speech and image transmission for all radio links

and information formats are included in the Appendix.

15
I

", ...., I

J

"'

,Iklt J J

""1 ,.. I I

Figure 11. PCM speech wavefonn for 2-FSK. Top wavefonn represents class 2.0

[SNR=30 dB, SIR=20 dB, BER=2.6e-2] and bottom wavefonn represents

class 3.8 [SNR=70 dB, SIR=30 dB, BER=3.6e-3].

".

Figure 12. Compressed PCM speech wavefonn for 2-FSK. Top wavefonn represents

class 2.0 [SNR=30 dB, SIR=10 dB, BER=7.0e-2] and bottom wavefonn

represents class 4.0 [SNR=80 dB, SIR=30 dB, BER=1.2e-3].

16
Figure 13. PCM image for 2-FSK. Top image represents class 2.0 [SNR=20 dB,

SIR=lO dB, BER=1.7e-1] and bottom image represents class 4.5 [SNR=80 dB,

SIR=50 dB, BER=3.6e-3].

17
4.3. Effect of Signal-to-Interference Ratio on Speech and Image Quality

Speech and image quality for a 2-FSK HF radio link subjected to interference are shown in Figures

14-16. Each graph represents a different information format and each curve in the graph represents

a distinct interference level. Interference levels identified in the graph legend are in units of

jamming-to-signal ratio (JSR) in dB. The JSR is the inverse of SIR which makes JSR in dB the

negative ofSIR in dB. The ordinate is in units ofsymbol error rate (SER) and the abscissa is in units

of SNR in dB (non-Gaussian noise power is not included in SNR). Subjective quality regions are

represented by different shades ofgray and identified with circled numbers. For comparison, results

for 2-FSK in Gaussian noise alone are displayed by the curve labeled "2FSKRGN." Similar speech

and image quality graphs for all radio links and information formats are included in the Appendix.

Data contained in the quality graphs can be used to answer questions concerning the robustness of

radio link and information format combinations. This is most easily accomplished by comparing

radio channel impairment levels for several radio links at a fixed SNR and quality classification. For

example, Table 4 shows the lowest SIR necessary for class 3 quality when the SNR is fixed at 80

dB. The values in the table are estimated from the speech and image quality graphs in the Appendix.

Table 4. Minimum SIR for Class 3 quality when SNR is 80 dB.

HF Radio Link PCMSpeech Compressed PCM PCMImage

SIR (dB) Speech SIR (dB) SIR (dB)

2-FSK 30 20 15

2-FSK with FEC 25 15 15

4-FSK 30 20 20

8-FSK 30 20 20

2-DPSK 30 20 15

4-DPSK 30 20 20

8-DPSK 40 30 30

VFCT 30 20 10

Table 4 indicates that for all radio links PCM speech needs 10 dB more SIR for class 3 quality than

compressed PCM speech. Thus compressed PCM speech is more robust than PCM speech in the HF

radio channel. The error correction in the CELP speech compression algorithm may partially explain

this performance improvement. Most radio links required 30 dB SIR for PCM speech and 20 dB for

compressed PCM speech. Exceptions were 2-FSK with FEC, which performed 5 dB better, and 8-

DPSK, which performed 10 dB worse.

The PCM image information format required the lowest SIR values. However, it also had the most

variation from radio link to radio link. The performance improvement may be due to differences in

perception between the ear and the eye. A comparison between compressed PCM image quality and

PCM image quality, not included in this study, would be more relevant.

18
.....

\0

2FS:KRGN JSR=9 -.-

JSR=-29 :J~H~:f~~ JSR=-39

JSR=-49

JSR=-59

Figure 14. PCM speech quality for 2-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
tv

o

Figure 15. Compressed PCM speech quality for 2-FSK. Circled numbers represent quality class and ordinate is in units

ofSER.
tv

.....

Figure 16. PCM image quality for 2-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
5. CONCLUSION

This study has demonstrated how software simulation can be used to predict wireless speech and

image quality degradation caused by an impaired radio channel. This performance prediction

capability will become increasingly important as multimedia wireless applications increase in

number. The software simulation process is composed oftwo steps. In the first step the radio link

is simulated at the signal level while in the second step the radio link is simulated at the bit level.

Division ofthe simulation process, in this way, makes comparison of numerous speech and image

formats very efficient.

Eight different HF radio links were simulated to illustrate this performance prediction method.

Speech in PCM and compressed PCM formats and an image in PCM format were transmitted over

a radio channel impaired by multipath, non-Gaussian noise, and varying levels of interference.

Quality degradation was shown in three ways. First, example speech waveforms and images at

specific SNR and SIR conditions exhibiting "marginal" and "best" quality were presented. Second,

graphs showing quality as a function of SNR and SIR were presented. Last, the minimum SIR

necessary for class 3 quality at a high SNR was tabulated. These tabulated results, found in Section

4, Table 4, indicate that PCM speech was more sensitive to interference than compressed PCM

speech for all HF radio links. PCM image transmission allowed the lowest SIR of the three

information formats. These results highlight the fact that quality can be dependent upon the choice

ofinformation format as much as the type ofmodulation.

This type ofperformance prediction can be extended to include more radio links, radio channels, and

information formats. Radio links for applications such as PCS and WLAN are currently ofmuch

interest. These applications are dominated by the same type ofmultipath as that found in HF radio

links. ITS multipath measurements in PCS and WLAN frequency bands are currently available for

signal level simulations [8,9]. PCS and WLAN applications also use digital facsimile (FAX) [10]

and compressed PCM image formats such as the JPEG [11] and MPEG [12]. These formats can be

easily added to bit level simulations.

The performance prediction method can be improved by using objective quality measures and

modeling the bit error sequences. ITS has been developing objective speech and image quality

measurement software in support ofvarious standards groups for a number ofyears [13]. This work

has been directed towards fixed radio channels such as cable and line-of-sight microwave. Recently

ITS has turned its attention towards the mobile radio channel. In the future, performance prediction

can be improved by using objective quality measures instead ofsubjective panel quality measures.

Lastly, for this study, bit-level simulation was performed with bit error sequences collected during

signal simulations. It may be possible to reduce signal simulation time by modeling the bit error

sequences from shorter signal simulations and using the model to generate bit error sequences ofany

length. This approach will result in lower overall computation times when larger speech or image

files are distorted.

22
6. REFERENCES

[1] C.C. Watterson, lR Juroshek, and W.D. Bensema, "Experimental confirmation of an HF

channel model," IEEE Trans. on Comm. Tech., pp. 792 - 803, Dec. 1970.

[2] J.W. Modestino and KR Matis, "Interactive simulation of digital communication systems,"

IEEE Journal on Selected Areas in Comm., pp. 51-76, Jan. 1984.

[3] CCIR Report 322, "World distribution and characteristics of atmospheric radio noise."

[4] K Brayer, "The improvement ofdigital HF communication through coding: I - interleaved cyclic

coding," IEEE Trans. on Comm. Tech., pp. 771-778, Dec. 1968.

[5] CCIR Report 864-1, "Data transmission at 2400/1200/600/300/150/75 bits/s over HF circuits

using multi-channel voice-frequency telegraphy and phase-shift keying."

[6] M.C. Jemchim, P. Balaban, and KS. Shanmugan, Simulation ofCommunication Systems, New

York: Plenum Press, 1992, pp. 386-396.

[7] J.P. Campbell, Jr., T.E. Tremain, and V.c. Welch, "An expandable error-protected 4800 bps

CELP coder (U.S. Federal Standard 4800 BPS Voice coder)," Proc. of1989 ICASSP., 1989, pp. 735-

738.

[8] P.B. Papazian, Y. Lo, E.E. Pol, M.P. Roadifer, T.G. Hoople, and Rl Achatz, "Wideband

propagation measurements for wireless indoor communications," NTIA Report 93-292, Jan. 1993.

[9] lA. Wepman, J.R Hoffinan, and L.H. Loew, "Impulse response measurements in the 1850-1990

MHz band in large outdoor cells," NTIA Report 94-309, Jun. 1994.

[10] K.R McConnell, FAX: Digital Facsimile Technology & Applications, Artech House, 1990.

[11] G.K. Wallace, "The JPEG still picture compression standard," Communications ofthe ACM,

pp. 31-44, Apr. 1991.

[12] D. Le Gall, "MPEG: a video compression standard for multimedia communications,"

Communications ofthe ACM, pp. 47-58, Apr. 1991.

[13] N.B. Seitz, S. Wolf, S. Voran, and R Bloomfield, "User-oriented measures of

telecommunications quality," IEEE Comm. Magazine, pp. 56-67, Jan. 1994.

23
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APPENDIX

Page

AI.I - AI.8 PCM Speech Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. A-2

A.2.1 - A.2.8 Compressed PCM Speech Quality: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. A-IO

A.3.1 - A.3.8 PCM Image Quality A-18

A4.1 - AA.8 PCM Speech Waveform A-26

A5.1 - A5.8 Compressed PCM Speech Waveform A-30

A.6.1 - A.6.8 PCM Image A-34

This Appendix includes performance prediction results for the radio links and information formats

described in the main text. Speech and image quality and symbol error rate (SER) as a function of

SNR and SIR are shown graphically in Figures AI.I to A3.8. Each graph represents a unique radio

link and information format combination and each graph curve represents a distinct interference

level. Interference levels identified in the graph legend are in units ofjamming-to-signal ratio (JSR)

in dB. The JSR is the inverse ofSIR which makes JSR in dB the negative of SIR in dB. The graph

ordinate is in units ofSER and the abscissa is in units of SNR in dB. Non-Gaussian noise power is

not included in SNR. Subjective quality regions are represented by different shades of gray and

identified with circled numbers. For comparison, results for 2-FSK and 2-DPSK in Gaussian noise

alone are displayed by the curve labeled "RGN." Quality measures range from I to 5 with I being

undecipherable, 2 marginally useful, and 5 excellent. Figures AA.I to A6.8 show examples of

"marginal" and "best" speech and image transmission. Marginal performance corresponds

approximately to class 2 quality. Best performance may be anywhere from class 3 to class 5

depending on the radio link.

A-I
N

2FSKRGN JSR=9 -.-

JSR=-29 :Jil~:l~~ JSR=-39

JSR=-49

JSR=-59

Figure A. 1.1. PCM speech quality for 2-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
w

2FSHRGN JSR=9 -.-

JSR=-15 :J~H~:~91 JSR=-29

JSR=-25

JSR=-39

JSR::-35 -Dr.

JSR=-49 V

Figure A.l.2. PCM speech quality for 2-FSK with FEC and interleaving. Circled numbers· represent quality class and ordinate

is in units of SER.
2FSHRGN JSR=Q .... --.-

~11~~lli··.·••

Figure A. 1.3. PCM speech quality for 4-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
VI

Figure A.IA. PCM speech quality for 8-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
v

0\

2DPSKRGN __ JSR=9

:Jil;:l~ JSR=-29 JSR=-39 JSR=-49 JSR=-59 JSR=-69

Figure A.I.5. PCM speech quality for 2-DPSK. Circled numbers represent quality class and ordinate is in units ofSER.
-...J

4DPSKRGH JSR=9 •••••..•....-... -.-

~11;21Iii•••••••

Figure A.1.6. PCM speech quality for 4-DPSK. Circled numbers represent quality class and ordinate is in units of SER.
00

8DPSHRGN

JSR=9 -.-

~11~~11 JSR=-59

Figure A.1.7. PCM speech quality for 8-DPSK. Circled numbers represent quality class and ordinate is in units ofSER.
\0

2FSKRGN JSR=9 -.-

:Jil~:~tI JSR=-39

JSR=-49 JSR=-59 __ JSR=-69

Figure A.1.8. PCM speech quality for 6-tone 2-FSK VFCT. Circled numbers represent quality class and ordinate is in

units of SER.
oFigure A.2.1. Compressed PCM speech quality for 2-FSK. Circled numbers represent quality class and ordinate is in

units of SER.
--

Figure A.2.2. Compressed PCM speech quality for 2-FSK with FEC and interleaving. Circled numbers represent quality

class and ordinate is in units of SER.
>-I

......

N

Figure A.2.3. Compressed PCM speech quality for 4-FSK. Circled numbers represent quality class and ordinate is in

units of SER.
>I

......

v.>

2FS](RGN

JSR=9 .---.---

~11;~~liil

Figure A.2.4. Compressed PCM speech quality for 8-FSK. Circled numbers represent quality class and ordinate is in

units of SER.
-.J:::.

Figure A.2.5. Compressed PCM speech quality for 2-DPSK. Circled numbers represent quality class and ordinate is in

units of SER.
.....

VI

4DPSJ<RGN

JSR==9

:J~H~::~H JSR::--39

JSR=--49

JSR=--59

Figure A.2.6. Compressed PCM speech quality for 4-DPSK. Circled numbers represent quality class and ordinate is in

units of SER.
.......

0\

8DPSJ<RGN JSR=0 -.-

~11~~tl

Figure A.2.7. Compressed PCM speech quality for 8-DPSK. Circled numbers represent quality class and ordinate is in

units of SER.
>I

......

-.....l

Figure A.2.8. Compressed PCM speech quality for 6-tone, 2-FSK VFCT. Circled numbers represent quality class and

ordinate is in units of SER.
.....

00

Figure A.3.1. PCM image quality for 2-FSK. Circled numbers represent quality class and ordinate is in units of SER.
1.0 -

Figure A.3.2. PCM image quality for 2-FSK with FEC and interleaving. Circled numbers represent quality class and

ordinate is in units of SER.
:>I

N

o

2FSHRGN JSR=9 .. --.-

~1I!~tI¥

Figure A.3.3. PCM image quality for 4-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
tv

-

2FSKRGN JSR=9--.-

~11~il S

Figure A.3.4. PCM image quality for 8-FSK. Circled numbers represent quality class and ordinate is in units ofSER.
N

N

Figure A.3.5. PCM image quality for 2-DPSK. Circled numbers represent quality class and ordinate is in units ofSER.
N

w

4DPSHRGN JSR=Q --.-

UI~sll

Figure A.3.6. PCM image quality for 4-DPSK. Circled numbers represent quality class and ordinate is in units ofSER.
:>I

tv

.j:::..

8DPSHRGN

JSR=9:::;.==

~11~~il S

Figure A.3.7. PCM image quality for 8-DPSK. Circled numbers represent quality class and ordinate is in units ofSER.
N

VI

2FSHRGN JSR=9 -.-

JSR=-39 ~~H~:~9~

JSR=-49 JSR=-59 __ JSR=-69

Figure A.3.8. PCM image quality for 6-tone, 2-FSK VFCT. Circled numbers represent quality class and ordinate is in

units of SER.
I I II

rn I "

.11L •.~L"""l J ,..1

"H • /' /fHf\' II I

Figure AA.l. PCM speech waveform for 2-FSK. Top waveform represents class 2.0

[SNR=30 dB, SIR=20 dB, BER=2.6e-2] and bottom waveform represents

class 3.8 [SNR=70 dB, SIR=30 dB, BER=3.6e-3].

I II

~~t ,I I I. " nil I II

I '" ". I T

Figure AA.2. PCM speech waveform for 2-FSK with FEC and interleaving. Top

waveform represents class 2.0 [SNR=30 dB, SIR=15 dB, BER=2.1e-2

and bottom waveform represents class 3.8 [SNR=80 dB, SIR=30 dB,

BER=l.3e-3].

A-26
Figure AA.3. PCM speech wavefonn for 4-FSK. Top wavefonn represents class 2.0

[SNR=30 dB, SIR=20 dB, SER=5.0e-2, BER=2.ge-2] and bottom

wavefonn represents class 3.2 [SNR=80 dB, SIR=40 dB, SER=4.2e-3,

BER=2.5e-3].

I

I 'll~~..

Figure AAA. PCM speech wavefonn for 8-FSK. Top wavefonn represents class 2.0

[SNR=30 dB, SIR=20 dB, SER=6.5e-2, BER=2.8e-2] and bottom

wavefonn represents class 3.0 [SNR=70 dB, SIR=40 dB, SER=4.1e-3,

BER=1.7e-3].

A-27
Figure A.4.5. PCM speech waveform for 2-DPSK. Top waveform represents class 2.0

[SNR=30 dB, SIR=15 dB, BER=2.5e-2] and bottom waveform represents

class 5.0 [SNR=80 dB, SIR=40 dB, BER=5.0e-4].

I LI~ .... .r" '" ...

Figure A.4.6. PCM speech waveform for 4-DPSK. Top waveform represents class 1.3

[SNR=70 dB, SIR=lO dB, SER=1.7e-l, BER=1.2e-l] and bottom

waveform represents class 4.0 [SNR=80 dB, SIR=50 dB, SER=1.2e-3,

BER=1.0e-3].

A-28
I f-------

- r"4l ~~ .l I-- "'lfrm '.,

fLilLlI ..,. m

'rll 'rll r,.

Figure AA.7. PCM speech waveform for 8-DPSK. Top waveform represents class 2.0

[SNR=60 dB, SIR=20 dB, SER=7Ae-2, BER=3.ge-2] and bottom

waveform represents class 3.5 [SNR=80 dB, SIR=50 dB, SER=5.5e-3,

BER=3.1e-3].

Figure AA.8. PCM speech waveform for 6-tone 2-FSK VFCT. Top waveform represents

class 1.7 [SNR=30 dB, SIR=10 dB, BER=3.7e-2] and bottom waveform

represents class 3.0 [SNR=80 dB, SIR=50 dB, BER=7.5e-3].

A-29
11" ,11ab1..JL

....... I'" r'IJJlI'"'

Figure A,5.I. Compressed PCM speech waveform for 2-FSK. Top waveform represents

class 2.0 [SNR=30 dB, SIR=lO dB, BER=7.0e-2] and bottom waveform

represents class 4.0 [SNR=80 dB, SIR=30 dB, BER=I.2e-3].

Figure A,5.2. Compressed PCM speech waveform for 2-FSK with FEC and interleaving.

Top waveform represents class 2.0 [SNR=20 dB, SIR=lO dB, BER=I.2e-l]

and bottom waveform represents class 5.0 [SNR=70 dB, SIR=30 dB,

BER=O.Oe-O].

A-30
Figure A.5.3. Compressed PCM speech waveform for 4-FSK. Top waveform represents

class 1.5 [SNR=30 dB, SIR=lO dB, SER=1.2e-l, BER=7.2e-2] and bottom

waveform represents class 4.0 [SNR=80 dB, SIR=40 dB, SER=4.2e-3,

BER=3.0e-3].

Figure A.5.4. Compressed PCM speech waveform for 8-FSK. Top waveform represents

class 1.8 [SNR=40 dB, SIR=lO dB, SER=1.2e-l, BER=8.8e-2] and bottom

waveform represents class 4.5[SNR=70 dB, SIR=40 dB, SER=4.1e-3,

BER=3.2e-3].

A-31
Figure A.5.5. Compressed PCM speech waveform for 2-DPSK. Top waveform represents

class 2.0 [SNR=30 dB, SIR=lO dB, BER=5.1e-2] and bottom waveform

represents class 5.0 [SNR=80 dB, SIR=40 dB, BER=4.0e-3].

....1

Figure A.5.6. Compressed PCM speech waveform for 4-DPSK. Top waveform represents

class 1.5 [SNR=50 dB, SIR=lO dB, SER=1.7e-l, BER=1.2e-l] and bottom

waveform represents class 4.5 [SNR=80 dB, SIR=50 dB, SER=1.2e-3,

BER=1.3e-3].

A-32
·•.. ........ . "I'rff

Figure A.5.7. Compressed PCM speech waveform for 8-DPSK. Top waveform represents

class 1.8 [SNR=40 dB, SIR=20 dB, SER=8.8e-2, BER=6.8e-2] and bottom

waveform represents class 4.0 [SNR=60 dB, SIR=40 dB, SER=5.5e-3,

BER=5.ge-3].

... "u

Figure A.5.8. Compressed PCM speech waveform for 6-tone, 2-FSK VFCT. Top

waveform represents class 2.0 [SNR=30 dB, SIR=30 dB, BER=7.4e-2]

and bottom waveform represents class 3.0 [SNR=80 dB, SIR=50 dB,

BER=1.7e-2].

A-33
Figure A.6.1. PCM image for 2-FSK. Top image represents class 2.0 [SNR=20 dB,

SIR=lO dB, BER=1.7e-l] and bottom image represents class 4.5

[SNR=80 dB, SIR=50 dB, BER=3.6e-3].

A-34
Figure A.6.2. PCM image for 2-FSK with FEC and interleaving. Top image represents class

. 2.0 [SNR=20 dB, SIR=5 dB, BER=2.0e-l] and bottom image represents class

5.0 [SNR=80 dB, SIR=30 dB, BER=1.2e-3].

A-35
Figure A.6.3. PCM image for 4-FSK. Top image represents class 2.0 [SNR=20 dB,

SIR=20 dB, BER=1.6e-l] and bottom image represents class 5.0

[SNR=80 dB, SIR=50 dB, SER=4.2e-3, BER=2.2e-3].

A-36
Figure A.6.4. PCM image for 8-FSK. Top image represents class 2.0 [SNR=20 dB,

SIR=20 dB, SER=3.1e-l, BER=1.6e-l] and bottom image represents class 5.0

[SNR=70 dB, SIR=40 dB, SER=4.1e-3, BER=1.6e-3].

A-37
Figure A.6.5. PCM image for 2-DPSK. Top image represents class 2.0 [SNR=20 dB,

SIR=5 dB, BER=1.ge-l] and bottom image represents class 5.0 [SNR=80 dB,

SIR=60 dB, BER=4.0e-4].

A-38
Figure A.6.6. PCM image for 4-DPSK. Top image represents class2.0 [SNR=20 dB,

SIR=lO dB, SER=3.1e-l, BER=2.2e-l] and bottom image represents class 5.0

[SNR=80 dB, SIR=50 dB, SER=1.3e-3, BER=8.0e-4].

A-39
Figure A.6.7. PCM image for 8-DPSK. Top image represents class 2.0 [SNR=30 dB,

SIR=15 dB, SER=2.6e-l, BER=1.4e-l] and bottom image represents class 5.0

[SNR=80 dB, SIR=50 dB, SER=5.5e-3, BER=2.7e-3].

A-40
Figure A.6.8. PCM image for 6-tone, 2-FSK VFCT. Top image represents class 2.0

[SNR=20 dB, SIR=O dB, BER=2.0e-l] and bottom image represents class 4.5

[SNR=80 dB, SIR=20 dB, BER=8.4e-3].

A-41
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FORM NTIA-29 U.S. DEPARTMENT OF COMMERCE

(4-ml) NAT"L. TELECOMMUNICATIONS AND INFORMATION ADMINISTRATION

BIBLIOGRAPHIC DATA SHEET

1. PUBLICATION NO. 2. Gov't Accession No. 3. Recipient's Accession No.

4. TITLE AND SUBTITLE 5. Publication Date

Radio Link Performance Prediction

via Software Simulation 6. Performing Organization Code

NTIA/ITS

7. AUTHORIS) E.A. Quincy, R.J. Achatz, M.G. Cotton, 9. ProjecVTask/Work Unit No.

M.P. Roadifier, J.M. Ratzloff

8. PERFORMING ORGANIZATION NAME AND ADDRESS

NTIA/ITS

325 Broadway 10. ContracVGrant No.

Boulder, CO 80303-3328

11. Sponsoring Organization Name and Address 12. Type of Report and Period Covered

NTIA

1401 Constitution Ave, NW

Washington, DC 20230 13.

14. SUPPLEMENTARY NOTES

15. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant bibliography or literature

survey, mention it here.)

The subjective quality ofspeech and image information, transmitted over a high frequency radio

link impaired with varying levels of interference, has been evaluated using software simulation.

The high frequency radio link was also degraded by frequency-selective multipath and nonGaussian noise. During radio link signal simulation an error sequence, determined from a

comparison of transmitted and received bits, was collected. Next digitized speech and image

information was distorted by the error sequence. Last, the quality of the distorted speech and image

information was subjectively evaluated. This process was repeated for a large number of

interference conditions. The same process can be used to show how multipath and non-Gaussian

noise affects speech and image quality. Other wireless applications such as personal

communications service and wireless local area networks can be analyzed in the same way using

radio channel measurements and models made by the Institute for Telecommunication Sciences.

16. Key Words (Alphabetical order, separated by semicotons)

Keywords: Digital modulation, radio link, radio channel, multipath, non-Gaussian noise,

interference, bit error, speech coding, image coding, speech quality, image quality, software

simulation.

17. AVAILABILITY STATEMENT 18. Security Class. (This report) 20. Number of pages

0 UNLIMITED.

69

19. security Class. (This page) 21 Price:

0 FOR OFFICIAL DISTRIBUTION.

*USGPO 1999-0-573-020/40154