Telecommunication Systems
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Prof. Dr. Tayfun Akgül
COMMUNICATION ENGINEERING
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Course Code : ISE301
Course title
: Telecommunication Systems
Credit Hours : 3
Semester
: Fall 2009
Instructor
: Prof. Dr. Tayfun AKGÜL
Course Page : http://atlas.cc.itu.edu.tr/~akgultay/
Refernece Book : A. B. Carlson, P.B. Crilly, J.C.
Rutledge, “Communication Systems,” McGraw-Hill,
4th Edition, 2002.
Syllabus - I
• Introduction to Signals
• General Topics in Communications and Modulation
• Spectral Analysis
– Fourier Series
– Fourier Transform
– Frequency Domain Representation of Finite Energy
Signals and Periodic Signals
– Signal Energy and Energy Spectral Density
– Signal Power and Power Spectral Density
• Signal Transmission through a Linear System
– Convolution Integral and Transfer Function
– Ideal and Practical Filters
– Signal Distortion over a Communication Channel
Syllabus - II
• Amplitude (Linear) Modulation (AM)
– Amplitude Modulation (AM)
– Double Side Band Suppressed Carrier (DSBSC)
– Single Side Band (SSB)
– Vestigial Side Band (VSB)
• AM Modulator and Demodulator Circuits
– AM transmitter block diagram
• Angle (Exponential) Modulation
– Phase Modulation (PM)
– Frequency Modulation (FM)
– Modulation Index
– Spectrum of FM Signals
– Relationship between PM and FM
• FM Modulator and Demodulator Circuits
• FM Transmitter Block Diagram
• FM Receiver
Outline
• Signals and Systems
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Signals and Systems
What is a signal?
Signal Basics
Analog / Digital Signals
Real vs Complex
Periodic vs. Aperiodic
Bounded vs. Unbounded
Causal vs. Noncausal
Even vs. Odd
Power vs. Energy
• What is a communications
system?
– Block Diagram
– Why go to higher frequencies?
• Telecommunication
• Wireless Communication
• Another Classification of
Signals (Waveforms)
• Power, Distortion, Noise
• Shannon Capacity
• How transmissions flow over
media
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Coaxial Cable
Unshielded Twisted Pair
Glass Media
Wireless
Connectors
The Bands
Signal and System
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Signals are variables that carry information
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System is an assemblage of entities/objects, real or abstract,
comprising a whole with each every component/element
interacting or related to another one.
Systems process input signals to produce output signals
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Examples
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ii.
Motion, sound, picture, video, traffic light…
Natural system (ecosystem), human-made system
(machines, computer storage system), abstract system
(traffic, computer programs), descriptive system (plans)
Signal Examples
• Electrical signals --- voltages and currents in a
circuit
• Acoustic signals --- audio or speech signals
(analog or digital)
• Video signals --- intensity variations in an image
(e.g. a CAT scan)
• Biological signals --- sequence of bases in a
gene
• Noise: unwanted signal
:
Measuring Signals
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0.8
0.6
0.4
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1
22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400 421 442 463 484 505 526 547 568 589 610 631 652 673 694 715
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-0.8
-1
Period
Amplitude
0
Definitions
• Voltage – the force which moves an electrical current
against resistance
• Waveform – the shape of the signal (previous slide is a
sine wave) derived from its amplitude and frequency
over a fixed time (other waveform is the square wave)
• Amplitude – the maximum value of a signal, measured
from its average state
• Frequency (pitch) – the number of cycles produced in a
second – Hertz (Hz). Relate this to the speed of a
processor eg 1.4GigaHertz or 1.4 billion cycles per
second
Signal Basics
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Continuous time (CT) and discrete time (DT) signals
CT signals take on real or complex values as a function of an independent
variable that ranges over the real numbers and are denoted as x(t).
DT signals take on real or complex values as a function of an independent
variable that ranges over the integers and are denoted as x[n].
Note the subtle use of parentheses and square brackets to distinguish between
CT and DT signals.
Analog Signals
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Human Voice – best example
Ear recognises sounds 20KHz or less
AM Radio – 535KHz to 1605KHz
FM Radio – 88MHz to 108MHz
Digital signals
• Represented by Square Wave
• All data represented by binary values
• Single Binary Digit – Bit
• Transmission of contiguous group of bits is a bit
stream
• Not all decimal values can be represented by
binary
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Analogue vs. Digital
Analogue Advantages
• Best suited for audio and video
• Consume less bandwidth
• Available world wide
• Less susceptible to noise
Digital Advantages
• Best for computer data
• Can be easily compressed
• Can be encrypted
• Equipment is more common and less expensive
• Can provide better clarity
Analog or Digital
• Analog Message: continuous in amplitude and over
time
– AM, FM for voice sound
– Traditional TV for analog video
– First generation cellular phone (analog mode)
– Record player
• Digital message: 0 or 1, or discrete value
– VCD, DVD
– 2G/3G cellular phone
– Data on your disk
– Your grade
• Digital age: why digital communication will prevail
A/D and D/A
• Analog to Digital conversion; Digital to
Analog conversion
– Gateway from the communication device to the
channel
• Nyquist Sampling theorem
– From time domain: If the highest frequency in the
signal is B Hz, the signal can be reconstructed
from its samples, taken at a rate not less than 2B
samples per second
A/D and D/A
• Quantization
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From amplitude domain
N bit quantization, L intervals L=2N
Usually 8 to 16 bits
Error Performance: Signal to noise ratio
Real vs. Complex
Q. Why do we deal with complex signals?
A. They are often analytically simpler to deal with than real signals,
especially in digital communications.
Periodic vs. Aperiodic Signals
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Periodic signals have the property that x(t + T) = x(t) for all t.
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The smallest value of T that satisfies the definition is called the
period.
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Shown below are an aperiodic signal (left) and a periodic signal
(right).
Causal vs. Non-causal
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A causal signal is zero for t < 0 and an non-causal signal is zero
for t > 0
Right- and left-sided signals
A right-sided signal is zero for t < T and a left-sided signal is zero
for t > T where T can be positive or negative.
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Bounded vs. Unbounded
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Every system is bounded, but meaningful signal is always
bounded
Even vs. Odd
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Even signals xe(t) and odd signals xo(t) are defined as
xe(t) = xe(−t) and xo(t) = −xo(−t).
Any signal is a sum of unique odd and even signals. Using
x(t) = xe(t)+xo(t) and x(−t) = xe(t) − xo(t), yields
xe(t) =0.5(x(t)+x(−t)) and xo(t) =0.5(x(t) − x(−t)).
Signal Properties: Terminology
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Waveform
Time-average operator
Periodicity
DC value
Power
RMS Value
Normalized Power
Normalized Energy
Power and Energy Signals
• Power Signal
– Infinite duration
– Normalized power
is finite and nonzero
– Normalized energy
averaged over
infinite time is
infinite
– Mathematically
tractable
• Energy Signal
– Finite duration
– Normalized energy
is finite and nonzero
– Normalized power
averaged over
infinite time is zero
– Physically
realizable
• Although “real” signals are energy signals, we
analyze them pretending they are power signals!
The Decibel (dB)
• Measure of power transfer
• 1 dB = 10 log10 (Pout / Pin)
• 1 dBm = 10 log10 (P / 10-3) where P is in Watts
• 1 dBmV = 20 log10 (V / 10-3) where V is in Volts
Communication System
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Engineering System
Social System
Genetic System
History and fact of communication
What is a communications
system?
• Communications Systems: Systems
designed to transmit and receive
information
Info
Source
Comm
System
Info
Sink
Block Diagram
Info
Source
m(t)
message
from
source
n(t)
noise
Transmitter
Channel
Tx
s(t)
transmitted
signal
Receiver
Rx
r(t)
received
~ (t )
signal
m
received
message
to
sink Info
Sink
Telecommunication
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Telegraph
Fixed line telephone
Cable
Wired networks
Internet
Fiber communications
Communication bus inside computers to
communicate between CPU and memory
Wireless Comm Evolution:
UMTS (3G)
http://www.3g-generation.com/
http://www.nttdocomo.com/reports/010902_ir_presentation_january.pdf
Wireless Communications
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Satellite
TV
Cordless phone
Cellular phone
Wireless LAN, WIFI
Wireless MAN, WIMAX
Bluetooth
Ultra Wide Band
Wireless Laser
Microwave
GPS
Ad hoc/Sensor Networks
Comm. Sys. Bock Diagram
Noise
m(t)
Tx
Baseband
Signal
• “Low” Frequencies
• <20 kHz
• Original data rate
s(t)
Channel
r(t)
Bandpass
Signal
Rx
~ (t )
m
Baseband
Signal
• “High” Frequencies
• >300 kHz
• Transmission data rate
Modulation
Formal definitions will be provided later
Demodulation
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Detection
Aside: Why go to higher
frequencies?
Half-wave dipole antenna
Tx
l/2
c=fl
c = 3E+08 ms-1
Calculate l for
f = 5 kHz
f = 300 kHz
There are also other reasons for going from baseband to bandpass
Another Classification of Signals
(Waveforms)
• Deterministic Signals: Can be modeled as a
completely specified function of time
• Random or Stochastic Signals: Cannot be
completely specified as a function of time; must be
modeled probabilistically
• What type of signals are information bearing?
Power, Distortion, Noise
• Transmit power
– Constrained by device, battery, health issue, etc.
• Channel responses to different frequency and different time
– Satellite: almost flat over frequency, change slightly over time
– Cable or line: response very different over frequency, change
slightly over time.
– Fiber: perfect
– Wireless: worst. Multipath reflection causes fluctuation in
frequency response. Doppler shift causes fluctuation over time
• Noise and interference
– AWGN: Additive White Gaussian noise
– Interferences: power line, microwave, other users (CDMA
phone)
Shannon Capacity
• Shannon Theory
– It establishes that given a noisy channel with information
capacity C and information transmitted at a rate R, then if
R<C, there exists a coding technique which allows the
probability of error at the receiver to be made arbitrarily
small. This means that theoretically, it is possible to
transmit information without error up to a limit, C.
– The converse is also important. If R>C, the probability of
error at the receiver increases without bound as the rate is
increased. So no useful information can be transmitted
beyond the channel capacity. The theorem does not
address the rare situation in which rate and capacity are
equal.
• Shannon Capacity
C  B log 2 (1  SNR) bit / s
How transmissions flow over
media
• Simplex – only in one direction
• Half-Duplex – Travels in either direction,
but not both directions at the same time
• Full-Duplex – can travel in either direction
simultaneously
Coaxial Cable
•First type of networking
media used
•Available in different
types (RG-6 – Cable TV,
RG58/U – Thin Ethernet,
RG8 – Thick Ethernet
•Largely replaced by
twisted pair for networks
Unshielded Twisted Pair
 Advantages
 Inexpensive
 Easy to terminate
 Widely used, tested
 Supports many
network types
 Disadvantages
 Susceptible to interference
 Prone to damage during
installation
 Distance limitations not
understood or followed
Glass Media
• Core of silica, extruded glass or plastic
• Single-mode is 0.06 of a micron in diameter
• Multimode = 0.5 microns
• Cladding can be Kevlar, fibreglass or even steel
• Outer coating made from fire-proof plastic
 Advantages
 Can be installed over long
distances
 Provides large amounts of
bandwidth
 Not susceptible to EMI RFI
 Can not be easily tapped (secure)
 Disadvantages
 Most expensive media to
purchase and install
 Rigorous guidelines for
installation
Wireless
Wireless (2)
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Radio transmits at 10KHz to 1KHz
Microwaves transmit at 1GHz to 500GHz
Infrared transmits at 500GHz to 1THz
Radio transmission may include:
– Narrow band
– High-powered
– Frequency hopping spread spectrum (the hop is
controlled by accurate timing)
– Direct-sequence-modulation spread spectrum (uses
multiple frequencies at the same time, transmitting
data in ‘chips’ at high speed)
Connectors
Fibre Optic
RJ45
Token Ring
Thicknet
T-Piece
The Bands
ELF VLF
LF
MF
HF VHF UHF SHF EHF
Radio
Submillimeter
Range
3KHz 30KHz 300KHz 3MHz 30MHz300MHz 3GHz 30GHz 300GHz 3THz
Far
InfraRed
Optical
300mm 1500nm
1PetaHz
Near
InfraRed
R
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700nm
1ExaHz
O
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Y
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600nm
G
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B
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500nm
I
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V
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Ultraviolet
400nm
X-Ray