Wireless Communications - ISS

safflowerpepperoniMobile - Wireless

Nov 24, 2013 (4 years and 7 months ago)


Wireless Communications

Maja Bystrom



A. History

The field of wireless communications has been in existence since the first humans
learned to communicate. In early days of civilization humans would transmit notices of
important events
, such as enemy invasions or royal births, through the sounding of horns
or the lighting of fires. While simple messages could be effectively transmitted in this
manner, in order to communicate over long distances the manpower expense was great,
since watc
htowers had to be built within sight of each other and continually manned, and
the number of messages was small. It was not until the 1800’s that wireless
communications became what we know it as today. Now we are able to use radio
frequencies to communic
ate information over long distances (think of the Cassini mission
to Saturn), we can send voice or video at rates of more than hundreds of megabits per
second, and the associated technology has become so inexpensive that many people are
able to afford a mo
bile phone in order to be in constant contact with others.

We often attribute the beginnings of wireless communication to Guglielmo Marconi
1937); however, to paraphrase Isaac Newton, “he stood on the shoulders of
giants”. In Marconi’s case these g
iants were scientists such as James Clerk Maxwell who
proved that radio waves existed, although he could not produce them, and Heinrich Hertz
whose name is now used as a unit of frequency, who transmitted the first man
made radio
waves. Besides being the f
irst to use the antenna, Marconi did not in fact invent anything
new. Instead, he was a remarkable engineer who combined the work of many others to
produce something that was known theoretically to be feasible. It took him through his
adolescence and into
his early twenties to develop a wireless system which would even
transmit as far as several miles, but after that point the scaling up of radio systems to
longer transmission ranges was rapid. By 1897 Marconi and his associates had
established a 14.5
fixed wireless link over water and the Italian navy had begun to
use his invention for ship
shore communication. These first communications were
digital, using Morse code, which was already widely established for wireline telegraphy.
However, the commun
ication rate was slow on the order of 12 words per minute (wpm).
The early transmission systems operated at wavelengths of few thousands of meters up to
10,000 meters; this corresponds to 3
30 kHz. At this time many of the great
communications companies, w
hich still exist today in various forms, were founded:
American Telephone and Telegraph, Marconi Company, Westinghouse, the Radio
Corporation of America.

At the same time as Marconi was laboring on his systems, others were racing to build
improved ones.
Development came quickly despite the setbacks of fierce storms that
repeatedly destroyed many of the transatlantic antennas. On Christmas Eve 1906
Reginald Fessenden transmitted the first voice and music that was heard by many
wireless operators in the nor
theast. He was then granted a US patent for voice
transmission. At the same time Lee DeForest developed his Audion tube, which could
amplify signals. Edwin Armstrong, a Columbia University graduate student, made use of
this vacuum tube to develop a system

that made long
distance voice transmission
possible. During the early 1900’s voice transmission across oceans and continents was
proven; however, many were in doubt of the usefulness of the “radio telephone”; since
there was no way of ensuring privacy, an
yone with a wireless receiver could listen in.
This remains one of the concerns with many of the wireless systems in use today.

Many of the developments of radio came during the two world wars. Spurred by the
necessity of creating effective military comm
unications, the U.S. government forced the
communications companies and scientists to work together. At the same time many
scientists in other countries were working to develop systems for their militaries. WW I
saw the first air
ground communication. M
arconi was the first to recognize the
usefulness of short waves, these 1
100 meter waves would use less power and travel less
far and thus could hide information from a distant enemy as well as reduce interference
with neighboring transmitters. Actually,
short waves ended up being much more efficient
than longer waves, used less power, and were not reflected off of the ionosphere (which
prohibits daytime transmission) but were reflected off of a higher layer. More
importantly, short waves could transmit in
formation faster than 100 wpm. Several
researchers also noted that ultrashort radio waves could be reflected from objects in their
path, thus laying the basis for radar, a technology perfected during WWII.

While there were sporadic radio broadcasts of m
usic and news to the public in both the
US and Europe prior to WW I, these broadcasts, and indeed all amateur radio operation
was shut down in the US during WW I for reasons of national security. Broadcasts were
resumed in the fall of 1919 and the first r
adio station KDKA in Pittsburgh opened on
Nov. 2, 1920 to begin daily broadcasts. The total number of amateur radio operators in
the US at that time was perhaps 30,000, so to ensure a listening audience the
Westinghouse company manufactured cheap radio set
s and when news of the broadcast
spread the general public hastily bought parts to build their own sets. After the rapid
success of broadcast radio, manufactures quickly improved their receivers, but yet, these
sets cost on the order of $25
$400, a month’
s wages, and needed frequent replacement of
vacuum tubes. Compare this to the price and quality of a Walkman today! Introduction
of the analog color and digital television sets saw the same problems. In 1954 the US saw
its first color TV sets for sale f
or $1300, which was near the price of a car at that time.
Currently, you can purchase an HDTV set for $5000
$7000, still a significant amount of

The number of telecommunications innovations grew rapidly during the last half of the

century. Cu
rrently there is widespread and growing use of cellular phones, cordless
phones, digital satellite systems, and personal mobile radio networks. Wireless
communications occurs at many different frequencies, from underwater communication
at extremely low fre
quencies on the order of tens or hundreds of Hertz, to infrared at

Hertz. See Fig. 1 for a partial diagram of the radio frequency (RF) spectrum. In the
United States the spectrum is allocated by the Federal Communications Commis

Fig. 1 A section of the RF spectrum showing some of the frequency assignments in

A significant development in telecommunications in the United States was the 1996
Telecommunications Act. This act was written in part to promote comp
(telecommunications had hitherto been controlled mainly by a group of monopolies),
promote integration of advanced services to all Americans and development of the
underlying infrastructure. Furthermore, it created measures, such as a rating code,
to deal
with violence and obscenities, and laid out punishments for misuse, such as harassing
phone calls, of the telecommunications systems.

The area of wireless communications will continue to grow for many reasons. People are
becoming accustomed to im
mediate access to information wherever their locations, and
technological improvements have made providing universal telecommunications access
feasible. There currently is an expansion in the number of personal mobile radio networks
that are the systems us
ed by law enforcement groups, ambulance services, and on the
floor of factories. The signals are meant to be relatively short
range and communication
takes place on designated frequency ranges where they will not interfere with other
applications such as w
ireless or mobile phones. In the near future there will be
significant growth in wireless for the office, such as wireless local area networks and
wireless private branch exchanges. New developments in personal communications
systems (PCS) include integra
ted phone/paging/email/data transmission. Currently
handheld units are offered by the major wireless industries with many of these features.
These units range from cell phones with email capability, wireless pen tablets (low
laptops without keyboards

interaction is via a pen), PDAs, and personal organizers, At
the moment these have low
rate internet service on the order of 10 kbps, however speed
and interconnectivity will be increased.

Television and radio broadcasts, while still in analog, are rapi
dly changing to digital. One
example of this is the direct broadcast satellite (DBS) systems that send a digital TV
signal from a satellite to an antenna at each subscriber’s home. The digital signal is a
composite of many television channels. The home ant
enna is connected to a set
top box
that extracts the desired channel and converts it to an analog signal for display on the
television set. Now the terrestrial broadcast of digital TV is mandated with switch to all
digital required in 2006. These signals a
re typically called digital television (DTV) or
definition television (HDTV). Similarly, digital radio is an area of recent significant
research and development. It is currently deployed and growing in popularity in Europe
and will likely become a sta
ndard in the U.S. The motivation behind digital transmission
is that the quality is better and there is no slow degradation as the receiver is moved
farther from the transmitter. The aspect ratio, ratio of width to height, is different than in
analog telev
ision, so that movies can be shown without truncation of the sides or being
displayed in “letterbox” format. Also, DTV allows for easier video manipulation such as
split screens or display of video in video. The drawbacks of digital systems are an
e in required bandwidth and the “cliff effect” in which either reception is good or
no reception is possible.

B. Communications Systems Overview

All of the systems mentioned previously, regardless of frequency or purpose, are
communications systems. A

communications system necessarily consists of three parts: a
transmitter, a receiver, and a channel. The transmitter takes a signal, whether analog or
digital, and formats it for transmission over the channel. A wireless channel can be water,
air, or vacu
um, and may contain obstructions such as buildings, terrestrial features, or
planets, depending on the medium. The receiver captures the transmitted signal and
performs signal processing, changing it from a form that can be transmitted over the
channel int
o a form that can be viewed, heard, or stored. All of these system components
introduce degradation to the transmitted signals; furthermore each system has a limit on
the number of signals that can be transmitted. By carefully studying and compensating

the degradation caused by the system components, and by carefully designing the
signal processing within a communications system, the number of signals that can be
transmitted at one time can be maximized while the signals’ degradation can be
minimized. I
n the following sections the signals and system design tradeoffs are briefly


Introduction to Signals

When we listen to radio or the telephone, or watch television, we are observing analog
signals, that is, signals that are continuous in amp
litude and time. Fig. 2 illustrates a
segment of speech. From this figure we can see how the signal is able to take on any
amplitude value within the range [

Fig.2 A sample of speech with a section extracted.

As was mentioned i
n the previous section, one of the fundamental constraints to our
transmission systems is the available bandwidth. The FCC only allocates a limited
amount of bandwidth for each application, and no one is allowed to exceed his or her
limitation. Therefore,
we need a method of determining the bandwidth or frequency
content of a signal; this bandwidth is measured in cycles per second, commonly called

One of the greatest mathematical discoveries of the 19

century was made by Jean
Baptiste Joseph Four
ier, who determined that most aperiodic signals could be represented
by summing their frequency components. That is, for most signals we are interested in
the equation

( ) ( )
j ft
V f v t e dt


holds. This means that a time
domain signal,
( )
v t
, such as our speech of Fig 2., can be
represented by the different frequencies in it. In the frequency domain our signal is
represented by
( )
V f
. This is perhaps best illustrated by an example. Fig. 3 shows the
transform of extracted segment of speech from Fig. 2. This figure demonstrates
that the speech sample is composed of frequencies between 0 Hz and 16 kHz. This
implies that if the channel or, correspondingly, the bandwidth allowance is greater than
16 kHz,
then the signal can be transmitted over the channel and not interfere with any
other transmitters. Thus, the Fourier transform is a very powerful tool in communications
system design. Note that this is not a typical speech sample, since it has high
noise; speech is typically bandlimited to 300
4000 Hz.

Fig 3. The discrete Fourier transform of the speech sample.

Rather than transmitting an analog signal, we may instead wish to transmit a digital
signal. Digital sig
nals are signals that are discrete in both time and frequency and may
arise in many ways. For instance, to transmit information stored in the memory of
computer such as an email, a stream of bits (1’s and 0’s), called a
, is formed.

We can also ch
ange our analog signals into digital signals through sampling and
quantization. Fig. 4 illustrates the process of converting the voice signal of Fig. 2 to a
digital signal. First, the signal is sampled periodically, that is every

seconds we
record the amplitude of the signal. Nyquist proved that the sampling frequency,

must be at least twice the maximum frequency in the signal. Typically, voice signals are
sampled at a rate of 8400 samples/second.

For compact
disk quality music, which is
typically limited to the range 0 to 15 kHz, the sampling rate is 44,100 samples/second.


Sampling and quantization (b) Reconstruction from sampled


an analog signal. values.

Fig. 4 Sampling, quantization and reconstruction of a signal.

Note that the resulting signal is discrete in the time domain, but each sample can take on
a continuous va
lue. For instance, if we look at the first sample taken at time

sample value

could be a number such as 0.01045972… with an infinite number of
digits after the decimal point. To represent even this

one sample as a series of bits would
obviously require an infinite
length bitstream, this would involve much more computer
memory or transmission bandwidth than we would be prepared to spend. Therefore we
need a way to decrease the size of our numbers and

we turn to quantization to represent
each sample by a fixed (and usually small) number of bits. In Fig. 4(a) the vertical axis is
divided up into 8 “bins”. Each quantized value is assigned the midpoint of the bin. For
example, any sample value falling in
the range [0.0 ,0.019) would be assigned the value
0.0095. Then to represent the sample efficiently, the bins are labeled with binary
sequences, and the sample falling in each bin is given the appropriate binary sequence. In
this case, 3
bit sequences are

employed, since it takes 3 bits to represent 8 numbers. Thus
we see that the first sample

is assigned the value 0.0095 corresponding to bits 100
while the second sample is assigned the value

0.0395 corresponding to bits 001.
herefore, quantization of the first two samples results in the bitstream 100001.

Since the samples are assigned to bins, obviously the bin size, or number of bins will
affect the quality of the quantized signal. Fig. 4(b) shows how a signal is reconstru
from quantized values. When the bits 100 are received after transmission, in order to be
fair, since it is impossible to know where in the range [0.0,0.019) the original sample fell,
the value 0.0095 is assigned to this sample. Therefore, the reconstr
ucted signal, which is
drawn in blue, becomes 0.0095 for the length of the sample interval
. It can be seen
that with this bin size the reconstructed signal is an approximation to the original signal.
To improve the quality of th
e reconstructed signal we could increase the number of bins,
and hence increase the number of bits required to represent a sample. However, this
increase in the number of bits/sample, although it results in a better reconstructed signal,
requires more stor
age or more transmission bandwidth. A typical number of bits per
sample for voice signals is 8, however, for compact
disk quality music 65,536
quantization levels are employed.

To further reduce the number of bits required to represent a signal, a compre
ssion scheme
can be utilized. Common image and video compression standards are JPEG and MPEG;
these are based on further quantization of signal components, and are used in digital
television. Speech and other signals rely on schemes such as linear predicti
on, which
relies on estimation of signals and transmission of the difference between the true signals
and their estimates. These types of compression are lossy; they discard information, and
thus reduce the quality of the signal. However, when carefully em
ployed, the degradation
may be kept to a minimum, or even be made not perceptually apparent, and the bitstream
size significantly reduced.


Signal Propagation and Channel Effects

Besides the lack of readily available bandwidth and the number of users who

access to wireless systems, the largest obstacle to building systems is that of noise and
fading. This was seen in the early transmission experiments, the Morse code dots and
dashes were hidden in noise, making long
distance transmission a challeng
e. Noise in
car radios is a familiar phenomenon, as you drive away from a transmitter, the station
becomes noisier until it finally drops out and all you can hear is static. This effect derives
from the decreasing power in a received signal as the transmi
receiver separation
increases. The power at the receiver is governed by the following equation

A A c





are the received and transmitter power, respectivel


represent the amplification of the receiver and transmitter antennas,
is the speed of light

3 10/
m s

is the signal freq
uency, and
is the distance between the transmitter
and receiver. We can see from this equation that the received power decreases with the
square of the distance from the radio to the transmitting antenna.

The electrical componen
ts of every communications system generate something called
thermal noise. Noise typically appears like a random (unpredictable) signal added to our
desired signal, and the noise increases with system temperature. A picture of a clean
signal, that is, our
transmitted signal, and the signal with noise,
( )
n t
, added to it is shown
in Fig. 5. According to equation (1) as the receiver and transmitter separation grows the
received signal power decreases. After a time the received power is

so low that the noise
becomes audible, and, if the separation is wide enough, the noise dominates, so that all
you can hear is static. This static is an example of the noise in the system, it is entirely
random so you cannot hear anything recognizable in
it. Because it is unpredictable, it
cannot easily be removed from the desired signal without degrading the desired signal.

Fig. 5 A signal with noise.

In addition to noise, signals are often subject to fading. Signals can be reflected off of the
nd, buildings, walls, trees or almost any object in their paths. One result of this is
that, on average, the signal strength may decrease by a factor greater than the square of
the distance. Furthermore, receivers at the same distance from the transmitter,

but in
different directions, may have greatly differing signal strengths. This phenomenon is
accounted for by a path loss exponent,
, which is a number computed by many
measurements in many areas and is the power to which the distance,
, in (1) is raise
d. It
is further accounted for by a random number for each location at radius

from the
transmitter. Thus, the power loss equation of (1) can be rewritten as

A A c
f d



In this equation,

is a noise factor, which is determined for

different terrains and can
capture some of the differences in signal strengths. The parameter n can take on values of
2 for free space loss (as in equation (1)), 4 for some urban cellular systems, and can range
as high as 6 for intrabuilding communication
. Naturally, this parameter will vary
depending on whether the signals can penetrate walls and how many buildings or other
obstacles there are in the neighborhood.

Fig. 6 Multipath


Another problem called multipath adds challenge to the signal transmission. Multipath is
illustrated in Fig. 6 where four signals are received at the transmitter. Each of the four has
traveled a different path and is received at a different stren
gth. Thus, the total received
signal is the sum of these four signals

( ) ( )
i i
r t a v t e

 


Each signal on each path is delayed by time

, and has amplitude and phase shifts

, respectively. One can imagine the difficulty in extracting an unknown signal
embedded in many others.

Finally, movement of the mobile will affect the received signal by producing a change in
frequency. This, as with noise is a familiar phenom
enon; think of the wail of an
ambulance approaching and then leaving, the frequency increases and then decreases. If a
sinusoid is transmitted, the received frequency is the sum of the transmitted frequency
and the Doppler shift

f f f
 


is the transmitted sinusoid. Assuming the receiver is co
linear with the mobile
the Doppler shift is given by


is the velocity of the mobile. As the
velocity increases (the mobile moves

toward the transmitter) the apparent frequency
increases, as it moves away the frequency decreases. Since mobile velocities are rarely
constant, this frequency can change quite a bit, making reception a difficult prospect.

Therefore, it is obvious that
there are many challenges in system design. Transmitters
must be closely spaced closely or use large enough powers so that receivers can
overcome the inherent system noise. If there is multipath, fading and Doppler shift, these
must be compensated for eith
er with careful signal design or intelligent receivers. One
method of protecting against poor channels is channel coding. Bits are added to the
bitstream in a controlled manner, so that if noise or fading degrades some bits, these lost
bits can be recovere
d from others. Because the size of the bitstream is increased, the
bandwidth must be increased proportionally in order to maintain the transmission rate.
Thus, there is another tradeoff between protection against channel errors and bandwidth


Modulation for Analog and Digital Transmission

In order to transmit a baseband signal, which is an analog signal composed of frequencies
near 0 Hz, at radio frequencies we need to change or

a high
frequency carrier
with our signal. There are two
primary methods of analog modulation, amplitude
modulation (AM) and frequency modulation (FM); this is where our radio transmission
schemes take their names.

In AM we change the amplitude of a carrier by our message. For AM radio the carrier or
al wave has frequencies in the range of 540
1700 kHz. The equation for AM is
given by

( ) [1 ( )]cos(2 )
c C c
v t A v t f t
 
 

( )
v t

is the modulated signal to be transmitted,
cos(2 )
c c
A f t

is a carrier of

and frequency
, and the baseband analog signal,
( )
v t
, is scaled by a
modulation index

. The construction of the AM signal is shown in Fig. 7. Fig. 7(a)
shows the
original signal, while Fig. 7(b) shows the signal scaled by the modulation
index and shifted by 1. Fig. 7(c) shows the original carrier, which has a frequency much
greater, typically by several orders of magnitude, than that of the baseband signal.
, Fig. 7 (d) shows the modulated signal. Observe how the message, the original
baseband signal
( )
v t
, is contained in the amplitude or envelope of the modulated signal
( )
v t

(a) Origin
al signal. (b) Scaled and raised signal.

(c) Carrier

(d) AM signal with information

mbedded in the

shape of the high
frequency carrier.

Fig. 7 Generation of an AM signal.

Frequency modulation is similar to amplitude modulation, except with FM the amplitude
of the carrier remains con
stant, but the frequency changes with the message. The FM
equation is

( ) cos(2 ( ) )
c C c
v t A f t v d
   
 


Now the integral of the message, scaled by a modulation index

, changes the phase or,
correspondingly, the freque
ncy of the carrier
cos(2 )
c c
A f t

. Fig. 8 shows the message
and the results of modulating the carrier. Observe how as the amplitude of the message
increases the frequency of the carrier increases.

(a) Original Signal.

(b) FM signal.

Fig. 8 Generation of an FM signal.

B. Digital Modulation

Since bitstreams are not continuous signals, the AM or FM modulation schemes
described above cannot be d
irectly employed, and different ways to modulate a high
frequency carrier must be found. One method is binary phase shift keying (BPSK). While
the name is complicated, building the modulated waveform is actually a very simple

To transmit a 1 the

( ) cos 2
v t f t

is transmitted for time
, while to send a 0
the signal

( ) cos 2
v t f t
 
 

would be transmitted. Note that these two signals are

out of phase with each other.

In Fig. 9 the BPSK signal corresponding to the
bitstream 10110 is shown. Observe that there is a phase change at every bit change, these
changes occur at times
1000,2000, and 4000


Fig. 9 A BPSK waveform.

In order to determine which b
it was transmitted in time interval
( 1)
b b
nT t n T
  
all we
have to do is to detect the phase of the received signal in that time interval. A more
common method of transmitting bits is to take two bits at a time from the bitstream and
use four
vel phase shift keying. Variations on this method are used in many of today’s
digital systems today. Since four signals are required to represent all of the possible
combinations of two bits, we then assign each combination the associated signal

( ) cos 2;1 4
i C
v t f t i

 
  
 
 


Again, we detect which of the four signals was sent by determining the phase of the
received signal.

Naturally, there are other methods of transmitting bits, we could instead choose between
two signals of different frequencies or amp
litudes to represent our two bits. Another
method is to use a combination of phase
shift keying and amplitude modulation; the
information is then contained in both the amplitude and phase of the signal.

The different modulation schemes are selected on th
e basis of their robustness in the face
of noise and fading, the power required to transmit each and the complexity of the
hardware required for transmission.


Multiple Access

Until this point we have discussed how to create a signal for one user, bu
t the available
bandwidth must be shared between many users. There are many methods of doing so;
two of these methods, frequency division multiple access (FDMA) and time division
multiple access (TDMA), are what is termed controlled multiple access, other
such as code division multiple access (CDMA) and carrier
sense multiple access
(CSMA) effectively permit users to access the channel whenever desired, under certain

(a) FDMA frequency division. (
b) TDMA time division.

Fig. 10 Division of frequency or time in two multiple
access schemes.

Frequency division multiple access implies splitting the available spectrum between the
users. This the method broadcast radio and television stations employ, ea
ch station is
assigned a band (a certain range of frequencies in which they can transmit) and there is a
short band in between the limits of each station called a
guard band
. The guard band is
used to protect against flaws in the system such as carrier dr
ift. A diagram of this is
shown in Fig. 10(a). In the U.S. analog cellular phone standard (AMPS) the channels are
30 kHz wide and there are a total of 832 channels in the system, each having a forward
and a reverse link.

For the TDMA system each user has

control of the total channel bandwidth for a short
amount of time, then the channel is handed off to the next user. Each waiting user has a
turn and then control is returned back to the first user. A diagram of a TDMA system is
shown in Fig. 10(b). TDMA i
s used in the European cellular phone standard Global
System for Mobile (GSM). Each channel supports 8 users; each user is allowed to
transmit for 577 microseconds before it is the next user’s turn.

Code division multiple access is a hybrid system, which


users to occupy the
same bandwidth and time simultaneously. Essentially, everyone transmits at the same
time; signals are differentiated at the receiver because they are orthogonal to each other.

The orthogonality and knowledge of characteristi
cs of the orthogonal signal make it
possible to extract one user’s signal from the entire transmission. The CDMA detection
process can be envisioned as a noisy room. Without concentrating it seems as if what you
hear is simply an unintelligible combination

of sounds. However, if you can focus on a
familiar voice, the words from this voice start to become distinguishable, and you can
block out the background noise in the room.

CSMA is a method used in many wireline communications that can also be used for
digital wireless communications. Each user’s voice or data is broken into packets. The
user then listens in on the channel in order to determine if anyone is transmitting. If the
channel is unoccupied, then the user transmits. However, a collision occurs i
f two more
users attempt to transmit at the same time. The receivers detect the collision and the users
must retransmit their information at a later time. This system is only efficient if there is
not a strict time constraint on the data, or if there are f
ew users who wish to
simultaneously transmit.


Cellular Systems, Frequency Reuse and Wireless Networks

Even with the multiple access schemes mentioned previously, only a small number of
users could be handled at the same time. Certainly, one system would

not be able to
handle an entire city, perhaps not even an entire building. Therefore, in order to
accommodate more than a small number of users, space is divided into cells as illustrated
in Fig. 11. Each cell contains a basestation that handles all mobil
es within the boundaries
of the cell. Cells do not necessarily have to be of the same size. In rural areas cells are
large, with radii of kilometers. On the other hand, consider a building such as the
Philadelphia Convention Center. During conventions ther
e are expected to be thousands
of people in the building; they may all access their mobile phones at the same time, for
instance, after the conclusion of a seminar. Thus, there may be tens or hundreds of
microcells, on the order of a few square meters with
in a building such as this. Each cell
is assigned its own frequency range, and neighboring cells will not be assigned the same
range. Thus, the spectrum is shared in a cellular system much as it is in the radio and
television system today. Cities which ar
e widely enough separated, for instance,
Philadelphia and New York, have radio and television stations at the same frequencies,
the frequency spectrum is re
used in space. In a cellular system if cells which are
assigned the same frequency range are widely

separated, then there will be little inter
interference. However, if there is to be wide cell separation, there must be a large number
of frequency ranges to assign, and thus each range is small. This limits the number of
calls that can be handled in

a cell, consider FDMA as an example.

Fig. 11 illustrates the interconnections of basestations, the transmitters/receivers of the
cells. Three cells of an
cell system are shown. The basestations are connected via a
landline or microwave link to the mobi
le telephone switching office (MTSO), which
controls all of the calls in this
cell region. The MTSO also routes calls to the public
telephone system, the traditional wireline system.

Fig. 11 An example of cell and basest
ation layout.

There are many important considerations in the design of cellular systems, ranging from
cell placement, to switching users between basestations as they move from cell to cell, to
providing service to users when they are roaming outside their

provider’s network. Each
of these is discussed briefly below.

Cell layout and system development is a difficult proposition. To determine the size of
cells required in a particular area, precise traffic models, models of the number of users at
a given t
ime, must be developed. A provider would like to be able to provide service to
all users within an area, but the cost of erecting basestations and expanding an existing
network is large. The space for a basestation must be bought or leased and it must be
connected, either through a wireline connection or a high
speed microwave or optical
link, to the network. Furthermore, space is not always available for basestations. In the
U.S. there are many service providers, in each area each provider establishes its

networks. There is continual competition for the best basestation locations, typically on
high buildings or hills. Often there is resistance from communities to new basestations in
their neighborhoods, either from dislike of the aesthetics of the towe
rs or from concerns
about the effect of radio waves on human health.

In a mobile system, since the users can travel between cells, the number of users within a
cell at a given time can never be known exactly. However, given the number of slots

in a cell,
, and the average usage within a cell of a selected size,
, the grade
of service providable can be calculated from

Pr{ }
T k


This is the probability that a call will be blocked, th
at the entire channel will be entirely
occupied by other users when the call is placed. If this number is greater than users will
tolerate, the parameters such as the cell size must be adjusted to compensate, balancing
the cost of cell placement with the d
esired grade of service.

The switching of a call from one basestation to another as a user travels through cells is
termed a
. Often, just turning a corner and moving out of sight of the basestation
can decrease the mobile received power enough to
require a handoff to a stronger, yet
more distant basestation. There are four types of handoffs varying on whether the handoff
is controlled by the mobile or the basestation or network. There are advantages to these,
first, if the mobile makes the decision
s, it can react quickly. However, if the network or
basestation makes

the decisions the delay can be long, since the basestation must judge
the received signal, therefore, there are advantages and disadvantages to all types of
handoffs. The main requiremen
t of handoffs is that they should be seamless, that is, the
user should have no awareness of the switch between basestations.

Roaming is placing a call from outside a home area, typically the roaming should be
seamless, and invisible to the user, that is,

the user should have no idea that the call is
being placed through a system other than the home system, with the possible exception of
a roaming light or indicator. Therefore, systems must have roaming agreements in place
and be compatible; it must be pos
sible to identify a user’s home system as the call is
being placed and the two systems must be able to exchange information for billing

There are many competing wireless systems and services in the U.S., some are all analog,
some are all digita
l, while others are hybrid systems which operate at times on either
digital or analog. There are also competing standards, which use different multiple
and modulation schemes. Therefore, if a user would like to change service providers he
or she mus
t purchase a new phone along with the service. Furthermore, many of the
European and Asian standards are different from the U.S. standards, thus phones for U.S.
systems may not work abroad. There is some movement in the U.S. to adopt the
European mobile ph
one standard; this will lay the basis for extension of global service.

Another increasingly employed solution to worldwide coverage is to manufacture a
mode phone, which is able to detect the type of system of the local provider and
adjust transmissi
on accordingly.

Up until this point we have considered mainly the mobile telephone networks; however,
paging, PCS and other mobile networks operate on the same principles. Furthermore,
although mobile wireless networks dominate the industry, there is a g
rowing market for
fixed wireless networks. This class of networks may have the same cellular layout as a
network for mobile communications, but has both fixed basestations and users. A simple
example is an infrared wireless link from computers to printers
in an office, while a much
more complex system could involve providing wireless internet access to a community.
The reason for the growth of interest in fixed wireless systems is the cost of installing
cable or fiber in established areas, and the ease of r
elocation possible with wireless.
Additionally, a fixed wireless network does not have the problems of time
varying fading
or Doppler shift and does not require the extensive processing for handoffs and roaming
as would a mobile network, and thus is easier

to implement.

In the future there will most likely be an increased number of wireless systems, but also a
move to standardize more so that these systems can intercommunicate. With an ever
increasing number of services and a desire for seamless handoffs
between providers,
developing the next generation of wireless networks will present significant challenges. A
lesson can be learned from one of the more spectacular failures in the
telecommunications industry, the Iridium project. Iridium was proposed as a

coverage cellular phone system for the business traveler and to provide telephone service
to areas such as Russia and India where there is currently a small market penetration. In
many ways it was an excellent engineering feat, the final design
consisted of 66 low earth
orbiting satellites built at a cost of about $30 million dollars each. With this many
satellites almost total coverage is possible and each satellite could handle 48*230 calls.
The goal of Iridium was to provide seamless service
in much of the world, since, for
reasons mentioned previously, equipment from one cellular provider in one geographical
area will not operate on the networks of competing providers in other geographical areas.
Unfortunately, the system has declared bankru
ptcy due to undersubscription and the
satellites will likely be discarded unless a purchaser is found. The service was not popular
for many reasons: the phones were large, much larger than the phones people are
accustomed to; the price per phone was on the

order of $3000; the phones did not
function well indoors; and, the cost per phone call was very high as compared with
terrestrial systems. Therefore, Iridium was obsolete almost before it was built. However,
this should not prohibit development of large o
r even worldwide systems, but instead
should serve as a caution of the care required in system development and marketing.
Others are currently meeting the challenge of providing worldwide coverage with a single
system; one example is the Globalstar system
which employs the European GSM
standard and both terrestrial and satellite coverage.



It is apparent that the design of communications systems is a complex process with a
large number of tradeoffs to deal with the limitations of channels an
d equipment. This
chapter began by examining signals and how to determine the bandwidth of signals, in
order to ensure that the transmitted signals had a small enough bandwidth to fit within
spectrum allocations. It was shown how analog signals can be samp
led and quantized,
and that there are size versus quality tradeoffs in this process. Next, channel effects are
studied. It was seen that there is inherent noise in systems, but that this noise can be
overcome by increasing the power at the transmitter. On
the other hand, large transmitter
power requires larger batteries, a stringent constraint in applications such as mobile
telephony. In wireless channels there can be multipath or time
varying fading and
Doppler shift to complicate reception.

Having discus
sed single signals, we then considered how to share channels and examined
four primary methods. Next, the further sharing of the spectrum was considered with an
examination of cellular systems and frequency re
use. Several of the inherent challenges
were d
iscussed, it was seen that systems must be carefully developed and synchronized in
order to provide for all users and to provide the services desired. Finally, as a case study,
the Iridium system was discussed.

Even with the enormous growth in wireless
communications systems within the past few
decades, there are constantly new advancements in research and development. Currently,
there are many concerns about the security and reliability of wireless systems, challenges
which are important but which are n
ot addressed here.

There is certainly much more to be done, frequencies to be explored and systems to be
developed, that a quotation by Marconi appropriately summarizes the current state of
wireless communications

It is dangerous to put limits on wirel

Further Reading

“Marconi Father of Radio”, D. Gunston, Crowell
Collier Press: NewYork. 1965.

“Digital and Analog Communication Systems”, L.W. Couch II, Fifth Edition, Prentice
Hall: NJ, 1997.

“Wireless Communications: Principles & Pr
actice”, T.S. Rappaport, Prentice Hall: Upper
Saddle River, NJ. 1996.

95 for Cellular and PCS”, L. Harte, M. Hoenig, D. McLaughlin, and R.
Kikta, McGraw
Hill: New York, 1999.

“Direct Broadcast Satellite Communications”, D.C. Mead, Addison
: Upper
Saddle River, NJ, 2000.

“DTV Survival Guide”, J. Boston, McGraw
Hill: New York, 2000.

“Wireless Personal Communications The Future of Talk”, R. Schneiderman, IEEE Press:
New York, 1994.

“Advances in Wireless Terminals”, P. Lettieri and M.B. Sr
IEEE Personal
, vol 6, no. 1, pp 6
19 Feb 1999.

Telecommunications Act of 1996, Pub. LA. No. 104
104, 100 Stat.56 (1996).


“Are mobile phones safe?”, K.R. Foster and J.E. Moulder,
IEEE Spectrum
, Augu
st 2000.



Consider the bitstream 101101… . Draw a BPSK waveform that could be used to
transmit the bits. Draw an FSK waveform for this bitstream. Compare the
bandwidth of these two modulation schemes.


Assume in a cellular syst
em that there are 10 channels (or slots) available and that
the average use of these slots is 50%. What is the blocking probability of this
system? Discuss whether this is a reasonable number.


Suppose you have been allocated 79 MHz of bandwidth by the FC
C for a wireless
system using FDMA with 25 KHz channels not including guard band. For a
telephone call with full
duplexing, both an uplink and downlink channel is
required. This means that at least 50 KHz would be required per call. If your
carrier can d
rift by 80 Hz, which means that a guard band is required between
each channel, how many users can the system support at any one time. Explain
your reasoning.


Consider the bitstream 101001… . This bitstream can be converted into a
waveform by holding the v
oltage at +1 for T seconds when a 1 appears in the
bitstream and then holding the voltage at

1 when a 0 appears. Draw the resulting
waveform. Now suppose amplitude modulation is to be used to transmit the
bitstream. Draw the modulated signal by using the
waveform from the first part in
Eq. (6). Show all steps.


Assume unit gain transmit and receive antennas, a transmission frequency of 900
MHz, and a transmission power of 1mW. Find the received power in Eq. (2) at a
receiver separation of 10m,

100m and 1km. Now, assume that the path
loss exponent is 4 (you may assume the noise figure is 1). Find the received
power in Eq. (3) at a transmitter
receiver separation of 10m, 100m and 1km.
Compare the six results.


Discuss the advantages and disadvan
tages of different cell layouts and degrees of
frequency use. Consider the performance, the cost, and the societal impact.

MATLAB Exercises

Project 1

AM, PM and Signal Bandwidth

The goal of this project is to discover the effect of amplitude
modulation and phase
modulation on the time

and frequency
domain representations of the modulated carrier.



Save the code given below for the first part of the project in a MATLAB directory
, save the second part of the project as


Run the first part by typing


Print the resulting plots.


Discuss the shape of the time
domain AM and PM signals. Consider where the
message is contained, and how it might be extracted from the modulated carrier.


Discuss the differences and simil
arities in the frequency
domain signal, the
magnitude spectrum of the modulated carrier.


Change the frequency of the message slightly by changing the parameter f0


Repeat steps 3
5 comparing the results of the new signal with the previous results.


Save the
PM signal by typing
save PM xpm


Run the second project by typing


Print the resulting plot


Discuss the resulting bandwidth occupied by the signal.


Comment out the lines generating the cosine and uncomment the lines for
loading a signal.




or the name of another signal file


Run the program and print the resulting plot.


Discuss the resulting bandwidth occupied by the signal. Compare this to the
results from step 11.


Submit all plots and written discussions.



% Project


% Part I

AM and PM Signal Generation

% Wireless Communications




% Generate a message which is a sinusoid

% First initialize variables

% N = number of samples

% f0 = cyclical frequency of message signal

% T0 = period of the mess
age signal

N = 1000;

f0 = 10;

w0 = 2*pi*f0;

T0 = 1/f0;

% Generate the message

n = 0:1:N

t = n*T0/N;

x = 0.75*cos(w0*t);






% Plot message signal

plot(t, x);

'Message signal v(t)'



% Generate carrier signal

fc = 100;

wc = 2*pi*fc;

Tc = 1/fc;

xc = cos(wc*t);
% Ac=1


plot(t, xc);

'Carrier signal'



% Generate AM signal

mu = 0.5;

xam = (1+mu*x) .* xc;

% Plot AM signal


plot(t, xam);

'AM signal'



% Find the spectrum of the AM signal

XAM = fft(xam);

XAMshift = 1/N*fftshift(XAM);

n1 =

freq_n1 = n1/T0;

% Plot the magnitude spectrum of the AM signal at frequencies of


plot(freq_n1, abs(XAMshift),

200 200 0 1]);

'Magnitude spectrum of the AM signal'






% Plot message signal this is the signal generated above



plot(t, x);

'Message signal v(t)'



% Generate PM signal

k_p = 3*pi/4;

xpm = cos(wc*t + k_p*x);

% Plot PM signal


plot(t, xpm);

'PM signal'



% Find spectrum of the PM s

XPM = fft(xpm);

XPMshift = 1/N*fftshift(XPM);

n2 =

freq_n2 = n2/T0;

% Plot spectrum of the PM signal


plot(freq_n2, abs(XPMshift),

200 200 0 1]);

'Magnitude spectrum of the PM signal'





% Project 1

% Part II

Signal Bandwidth

% Wireless Communications



% Either load in a previously stored signal or create a new

% uncomment the appropriate following section.


LOAD a signal

uncomment five command lines


replace <filename> the name of the stored file

% it must be in filename.mat (Matlab) form


%load <filename>;

%w = <filename>;

%N = length(w);



% CREATE a new signal

uncomment seve
n command lines

% the signal will be a cosine with variables


N = number of samples


fo = cyclical frequency of sinusoid


wo = angular frequency of sinusoid


To = period of sinusoid

N = 50;


= 10000;

wo = 2*pi*fo;

To = 1/fo;

n = 0:1:N

t = n*To/N;

w = 4*cos(wo*t);

% Find Fast Fourier Transform of the signal

W = fft(w);

% Convert samples 0,1,...,N
1 into positive and negative

Wshift = 1/N*fftshift(W);

% Plot signal vs. sample



'Sample Number'

'Sample Amplitude'

% Plot FFT vs sample



'Sample Number'

'Magnitude Spectrum'

% Shift the samples so that they are centered at 0

% S
cale the axis

n1 =

freq_n1= n1/To;

length(freq_n1) > length(Wshift)



% Plot the transform vs. cyclical frequency



'Cyclical Fr
equency (Hz)'

'Magnitude Spectrum'

Project 2

Noise in Baseband Signals

The goal of this project is to investigate the effect of varying degrees of noise on digital
baseband signals.



Save the code given below for the first part of th
e project in a MATLAB directory


Run the first part by typing


Print the resulting plot.


Record the noise standard deviation and the bit error count.


Discuss the appearance of the signal with noise as compared to the noise


nge the noise standard deviation to a value within the permitted range given in
the code and re
run the program


Record the noise standard deviation and the bit error count.


Repeat steps 6 and 7 for at least 10 values of the standard deviation.


As the nois
e standard deviation increases and decreases note the appearance of the
noisy signal. Discuss the changes in the signal.


Follow the directions in the code for the second part of the project.


Print the resulting plot.


Discuss the changes in the bit error ra
te as a function of the noise.


Submit all plots and discussions.



% Project 2

% Part I

Noise in Baseband Signals

% Wireless Communications



% Change this following line to change the standard deviation of the

% which will change th
e noise power. The valid range is [0.01,1]. A
large number

% indicates more noise



% Create an arbitrary bitstream


bitstream = [1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 1 0 1 0 1 1
1 1 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 0 1

0 0 1 1 0 0 0
1 1 0 1 1 0 1 01 0 0 1 1 1 1 1 0 0 1 0 1 0 1 0 1 0 0 1 1 1 1 0 0 1 0


% Generate the baseband signal,v[n], from the above bitstream with

% of width 5 and height 1/sqrt(5).



bitstream(i+1) == 1

v(i*5+1) = 1;

*5+2) = 1;

v(i*5+3) = 1;

v(i*5+4) = 1;

v(i*5+5) = 1;


v(i*5+1) = 0;

v(i*5+2) = 0;

v(i*5+3) = 0;

v(i*5+4) = 0;

v(i*5+5) = 0;




% plot the NRZ baseband waveform


subplot(3,1,1), plot(v,

axis([0 40 0 1.0]);


% add zero
mean Gaussian nois
e to the waveform



y(i) = v(i) + stdev*randn;



% plot the noisy waveform


subplot(3,1,2), plot(y,

axis([0 40
4 4]);


% create an integrate and dump filter with output z(i)



temp = y(i*5+1) + y(i*5+2)+ y(i*5+3) +
y(i*5+4) + y(i*5+5);

z(i*5+1) = temp;

z(i*5+2) = temp;

z(i*5+3) = temp;

z(i*5+4) = temp;

z(i*5+5) = temp;



% plot the matched filter output


subplot(3,1,3), plot(z,

axis([0 40
10 10]);


% sample and determine the number of bit errors


t_error_count = 0;


z(i*5+3) > 0.5

temp = 1;


temp = 0;


bitstream(i+1) ~= temp bit_error_count = bit_error_count +1;




% Run this program for many choices of the standard deviation and

% the sta
ndard deviation and the number of bit errors for each trial



% Project 2

% Part II

Bit Error Rate Curves

% Wireless Communications



% For each standard deviation compute the signal
noise ratio

% using the following. Replace stdev with yo
ur value and record

% the result

SNR = 10*log10(1/(stdev^2));

% Now plot the first point with the following three commands

% Replace SNR and bit_error_count with the appropriate values


axis([0 40 0.000001 0.1]);


% Now add the other points by repeating the following command

% Replace SNR and bit_error_count with the appropriate values


Project 3

Number of Users and Blocking Probability

The goal of this project is to determ
ine the average amount of traffic, effectively the
number of users, which can be served in a cell given a desired grade of service and a
number of available channels.



Save the code given below for the auxiliary function in a MATLAB directory as
save the code for the project as


Run the program by typing


Record the capacity


Change the number of channels, the parameter
, to a number between 2
and 100.


Repeat steps 2 through 4 for 10 channel values.


Change the desired
grade of service, the parameter
, to a value
between 0.1 and 0.0001


Repeat steps 2 through 6 four times.


Discuss the results. Consider the tradeoffs in desired grade of service and the
number of users in the system.


Submit the discussion.



% Project 3

Auxiliary function

% Number of Users and Blocking Probability

% Wireless Communications



[diff] = ErlangB(A,C,GOS)

sumA = 0;




GOS1 = (A^C/factorial(C))/sumA;




% Project 3 Main Routine

% Number of Users and Blocking Probability

% Wireless Communications



% Change the following two parameters and plot the number of

% users, the capacity, as a function of desired_GOS for a

% selected number of channels

channels = 10;

desired_GOS = 0.01;