Practical Wireless Data Modem Design (Artech House Mobile Communications Library)

380 Pages·1999·13.15 MB·English

by  Jonathon Y. C. Cheah

Chapter 1
Introduction
Without doubt, the age of information communications is upon 11s. The rapid
pace of technological advancement in digital data communications can be wit-
nessed in a multitude of applications in our day-to-day existence. In recent
years, the widespread proliferation of wireless digital cornmunications hass been
readily accepted by the general population worldwide; this is nearly unpa~rallcled
in few other human scientific achievements in terms of scope and speed of devel-
opment. The intense quest for new personal wireless communications products,
fueled by strong consumer demands, has required today’s practicing design c11-
gineers to address a broad range of engineering disciplines in depth, previously
only encountered in institutional wireless commiinications products. These are
the products that are used by, for example, Post and Telephone and Telegraph
(PTT) organizations or military establishments. There is a distinct difference in
the engineering design and development philosophy behind institutional products
and consumer products. For institutional products, the design cycle is typically
very long. Years of development effort are not uncommon. Every aspect of the
design element is likely to be serviced by specialists from that particular disci-
pline. By contrast, most consumer products have very short design cycles where
“time to market” is the prevailing gospel. In this environment, design engineers
are confronted with a wider variety of design issues not necessarily within their
area of expertise.
Engineers who have been working in the consumer product area during re-
cent times can perhaps attest to the difficulty of crossing the technology chasm of
digital communications engineering designs as a consequence of their t radit iorial
electrical engineering education. The level of mathematical sophistication in basic
electrical engineering education in the treatment of thcoretical stochastic random
processes is, in general, not sufficient. Today, newly minted electrical engineers
1
2
are still trained iri the specific traditional disciplines. Radio frequency (RF) engi-
iiccrs who specialized in radio design discipline have been solely coric-erned with
the design of conventional radio transceiver circuitry. There is a tiriique set of RF
terrriiriology, forrriulations, and design practices that RF engineers are farriiliar
with. Those who specialized in digital designs are mainly concerned with the
logic truth tables, the setup arid propagation times of a hi-level logic signal, arid
they too are trained in design practices unique to the digital erigirieeririg disci-
pline. The new brecd of engineers who are much closer to the corrirriunicatioris
theory discipline arc the digital sigrial processing (DSP) engineers. These cngi-
iiccrs are, by arid large, prograrrirriers who design with fast microproc-essors that
have embedded mathematical tools suitable for irnplemeritirig complex signal pro-
(-(wing.T he basic acactcrriic erigirieering curricula, in gerieral, do riot provide t he
basic corrirriuriicatioris theory skills necessary for these engiricers to tackle with
c*orifidcricet lie challcriges of information age technology.
Thus, here lies the kriowlcdge chasm where wireless communication designs
arc cwricerned. The principle of wireless modern design is fixidamentally rooted
in statistical comniuriicatioris theory. It also encorripasses, in depth, the elernerits
of DSP, forward-error coding, signal-detection theory, and statistical over-the-air
signal-propagation rriodeling. Traditionally, these are riot the main fort6 of a
typical hardware or software design engineer, irrespective of individual scliolastic
spccializatiori. Thus, a good wireless rriodeni design is particularly difficult to
ac.c.orriplish because its irriplerrieritation is not readily intuitive in the conventional
hardware circuits or software design sense. For instance, a radio engineer who is
very familiar with the arriplitude passband response of a filter and its stopband
rejection may be less at ease with the concept of differential group delay. Irideed,
o~ico f the most irriportarit factors in perforrnance degradation of a mocterri is
thc effect of iritcrsyrribol iritcrference (ISI), which can be directly attributable
to the high group delay of a filter. Similarly, the sanic kriowledge deficioricy
applies across all other related engirieeririg disciplines. This book will show that
the iise of a simple rorribinatiori of read-only rrierriories (ROM) arid colinters to
for111 high-speed DSP erigiries may not riecessarily be intuitive to most digital
designers, either.
Ari cxarriplc of this knowledge gap in designing cornrnunic,atioris equiprricnt
follows. Often, yourig RF erigiriecrs try very hard to calculate the bit crror rate
(BER) versus Eb/N, relationship with a known carrier phase noise variance 02,
hit leave in frustration. If those RF engineers are concerned with how rriiich
rioisc a local oscillator (LO) in a corrimunicatioris system could tolerate, there is
no easy means to achieve the answer. Without a secure mathematical background
iri statistical analyses, most digital comrnunications textbooks arc of little value
to tlitsc engineers. It is riot iidikc working out a filter design for example. It
is very satisfying to know all about Chebyshev polynomials, but if a particular
Chebyshev filter response is needed, the designer will most likely look up the
Chebyshev coefficients from a table. The equivalent of a Chebyshev coefficient
table is hard to find or nonexistent in digital communications textbooks. Being
originally trained as an RF engineer, the author can vividly remember these
frustrating experiences.
From standard communications textbooks, such as the excellent textbook by
Stiffler as listed in the selected bibliography at the end of this chapter, the added
white Gaussian noise (AWGN) conditional probability of symbol error P, as a
function of AWGN noise variance No and phase noise variance u2 relationship is
stated as:
Then, from this equation, where Q function is integrated over the effect of
noise variance, one would naturally expect that as u2 approaches zero, Pe should
approach the result of a Q function. In this equation, however, when u2 ap-
proaches zero, the calculator gets stuck with some very large numbers. A radio
engineer who practices within the fast-moving wireless communications industry
needs to get these answers and others as easily ass looking up a Chebyshev coef-
ficient from a table. A simple answer to this problem lies in the availatlility of
practical digital modem design examples. Perhaps among the great many excel-
lent communications theory textbooks already in existence, there is room for a
book with practical implementation in mind.
Therefore, this book is concerned with the theory and the implementation
of wireless modem design intended for engineers from all engineering disciplines.
Its emphasis is to provide a handy reference where an engineer, irrespective of
individual basic discipline, can obtain the information necessary to participate in
this exciting field. To achieve this goal, this book presents the specific mathemat-
ical derivations and implementation methodology related only to a few specific
wireless modem design examples and their application environments. This book
also presents common tidbits of wisdom and ‘‘rules of thumb” well known in
modem design but not explicitly mentioned in the textbooks available to date.
To the design engineers who toll the workbench daily, the wealth and depth of
these bits and pieces of information constitute what is deemed ass “practical ex-
perience.” Although most of the rigorous mathematical derivations that can be
found in most digital communications textbooks are omitted from this book, the
ready-twuse mat hernatical expressions presented are included to pave the way to
an understanding of theoretical statistical communications theory. All equations
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and expressions contained in this book are set up so that the results that they
represent can be easily reproduced by Mathcad, which is an excellent low-cost
software that presents mathematical expressions in the way that they are writ-
ten. Therefore, Mathcad is perfect for the purposes of this book. Other similar
leading mathematical software packages, such as MatLab and Mathematica, can
be easily adapted to reproduce these results, although their printed program list-
ings are not as comprehensive as actually running the programs on a personal
corriputer (PC).
The mathematical expressions used in this book are chosen specifically riot to
frustrate the uninitiated. The results of (1.1) with a = O.O,O.l, 0.2,0.3,0.4, and
0.5 radians for &,/Noo f 2 to 10 dB are obtained using the Mathcad file listing
provided in Figure 1.1.
Because of the long computation time required to evaluate P(y,a ),i t is inore
convenient to compute it only once and store the results in a file. y is defined
as Eb/No. Manipulation of the data and the presentation of the curves can
be done at leisure later using a plot routine as shown in Figure 1.2. Plotting
PO,, pin, PZn, p3n, ~ 4and~ p5,,, versus, n with the y-axis set to log scale and
the z-axis set to linear scale, this plot will provide BER results identical to that
of the classical Q function for Eb/NO range of 2 to 10 dB with a = 0.0 radian.
The resultant family of curves with a equal to various values in radians can be
seen in Figure 1.2.
The young RF engineers mentioned previously would have been very pleased
to know the tradeoffs in BER performance when they optimized on the phase
noise contents of the LOS.
Naturally, the full details of (1.1) and its evaluation above will be discussed in
a later chapter. It needs to be emphasized that most of the computations shown
here and in later parts of this book can be painfully slow even if a very-high-
performance PC is used. Therefore, any PC that has less than a Pentiiirn 120
MHz processor may not be suitable for this use.
1.1 Summary of Common Modulation Techniques
It is difficult to cover all possible modulation and demodulation techniques. There
are many excellent text books that cover various modulation techniques in great
technical and theoretical detail. Because most of these techniques have a lot in
COII~OiIn~ t heir implementation and operation, this book will focus only on the
description and discussion of a few representative theoretical analysis tools and
irnplementation techniques suitable for practical engineering use.
Even so, practicirig engineers should be aware of the existence of other more
cornmon modulation techniques that are in use today. Table 1.1 lists (*ornnioIi
5
Listing for evaluation of BER probability function:
TOL=0.0000001
n
( X
Factorial definition: F(x)=if e 0 , n ,I)
n=l
50
2 (- 1)”.X(2.n+l)
Complementary error function: erfc(x)= 1 - -. C
pn).(2m+ 1)
.J;;
n=O
n :=2..9 M :=2 :=0,0.1..0.5
(T
-n
Define Eb/No: E, := 10”
This computation takes some time. It is useful to save the results for use later.
WRITEPRN(”plot1001.pm”) :=P(En,~)
Figure 1.1 Mathcad listing for computing bit error probability function with noise
variance 0.
6
Listing of plot of family of curves with specified 0:
p := READPRN( "plot1 00 1. prn" )
Figure 1.2 A family of curves for (J = 0.0, 0.1,0.2,0.3,0.4 and 0.5 radians is plotted
based on precalculated results.
7
modulation techniques that are used in the modern applications of wireless dig-
ital communications. They are categorized into two main groups. The main
distinguishing feature is whether or not the signal waveform amplitude carries
modulation information. Of course, the advantage of constant amplitude rnodu-
lation is that it does not cause intermodulation when passing through a nordin-
ear system. Therefore, it can tolerate the nonlinear passband and amplification
characteristics frequently encountered in circuit designs. This practical irriple-
mentation advantage becomes a very important consideration in the design of
digital wireless communications products for consumers. The full significance of
this attribute will be discussed in later chapters.
Constant amplitude modulation is also commonly known as constant envelope
modulation, unicircle modulation, and angular modulation.
While on the topic of various modulation techniques, it is pertinent to digress
a little in an amusing light. In the communications market circle, interestingly
enough, one often comes across very convicted arguments for the choice of one
modulation technique over another. The most popular one about the choice of
PSK is along the line that PSK is a constant envelope modulation because only
phase shifting takes place in the signal. One may also come across a further
argument that because only the phase of the signal is shifted, PSK is therefore
a single-tone modulation and it resembles a continuous wave (CW) signal. Of
course, PSK is not a constant envelope modulation, neither is it a CW signal.
Based on fundamental engineering intuitions, these are very good examples of
the “logical mind-share.” These examples, however, also support the knowledge
chasm contention elaborated earlier in this chapter.
Nevertheless, the correct choice of a modulation technique very much de-
pends on the wireless network design requirements and constraints. For example,
in a very-small-aperture terminal (VSAT) satellite network design, the binary
phase-shift-keying (BPSK) modulation technique is the modulation technique of
choice. This is because in a VSAT satellite network, the satellite downlink from
the network hub is typically power limited by the total effective isotropic ra-
diated power (EIRP) offered by the transponder. On satellite uplink from the
VSAT, it is noise limited because of the low EIRP of the ground stations from
low antenna gain. Therefore, bandwidth efficiency is less important than the
robustness and power efficiency of the modulation. On the other hand, in a large
satellite network where the physical dimensions of the antenna aperture are riot
in question, quadrature phase-shift keying (QPSK) is the modulation technique
of choice. In another case, where antenna aperture dimensions are required to be
small, but the downlink EIRP limitation is not a problem, then QPSK is once
again the modulation technique of choice. Such is the case with the high-power
Hughes Aircraft Ku-band US-DBS satellites. The EIRP per transponder of this
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Table 1.1
Digital modulation schemes
Acronym Constant envelope modulation descriptions
FSK Frequency-shift keying. Mainly refers to binary levels
hlFSK hl-ary frequency-shift keying
CPhl Continuous phase modulation
SHPM Single modulation index phase modulation
hlHPM Multiple modulation index phase modulation
LREC Rectangular pulse of length L
CPFSK Continuous phase frequency shift keying
MSK Minimum shift keying
LRC Raised cosine pulse of length L
LSRC Spectrally raised cosine pulse of length L
GMSK G aussi an mini mum-shi ft keying
TFM Tamed frequency-shift keying
CORPSK Correlative PSK
Acronym Nonconstant envelope modulation descriptions
BPSK Binary phaseshift keying
QPSK Quadrature phaseshift keying
OQPSK Offset QPSK
n/4 QPSK n/4 shifted quadrature phaseshift keying
MPSK M- ary phasesh i ft keying.
QAM Quadrature amplitude modulation
ASK Amplitude shift keying
QORC Quadrature overlapped raised cosine modulation
QOSRC Quadrature overlapped squared raised cosine modulation
SQORC Staggered QORC
Q~PSK Quadrature-quadrature phaseshift keying
I JF-OQPSK Intersymbol interference and jittersfree OQPSK
TSI-OQPSK Two symbol interval OQPSK
SQAM Superposed QAM
XPSK Cross-correlated QPSK
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series of direct broadcast satellites (DBS) is greater than 51 dBW. Apart from
the additional relative implementation simplicity advantage, the main reason for
using QPSK is that it has optimal power arid bandwidth efficiency.
The power and bandwidth or spectral efficiency tradeoff is obviously bounded
by the Shannon channel capacity derivation:
c +
= B, log, (1 SNR) (1.2)
C is the channel capacity in hits per second
where
B, is the channel bandwidth in Hz
Equation ( 1.2) determines the necessary signal-to-noise ratio (SNR) to achieve
full channel capacity.
Given that the signal power is S, bit energy is Eb, and noise power is No in a
normalized 1 Hz bandwidth
S
SNR =
No Bw
-S
E, =
C
Equation (1.2) can be expressed in a more familiar form akin to the basic
“reversed biased diode current to voltage I-V equation” to the hardware circuit
designers:
By using error probability expressions available for a few modulation tech-
niques that will be discussed in later parts of this book, the bandwidth efficiency
performance can be conveniently plotted in Figure 1.3.
Using Mathcad, the AWGN error probability expressions used in Figure 1.3
can be directly evaluated and are presented below. The points plotted for various
modulation techniques shown in Figure 1.3 are for BER = lOV3. This is a common
specification for voice coder-decoder (vocoder) related applications.
Thus, using the definition of y = Eb/No and that the bandwidth B, is
heuristically approximated as the inverse of the symbol time, then the AWGN
probability of symbol error arid the capacity versus bandwidth ratio for coherent
PSK are