An Alternative Technique for OFDM

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Otto-von-Guericke
Digital Communication Systems
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
International Master Studies
in
Electrical Engineering and Information Technology
Prof. Dr. A.S. Omar [email protected]
Tariq Jamil Saifullah,Khanzada
[email protected], [email protected]
FET,IESK,
http://www.iesk.unimagdeburg.de/hf_technik/hauptmenue/mitarbeiter_hf/prof__omar.html Omar
Unversity of Magdeburg , Germany
http://www.iesk.uni-magdeburg.de/en/microwave_eng_-p1903/Hauptmen%C3%BC/Mitarbeiter+HF/tariq_j__s__khanzada.html Khanzada
4/8/2015 3:56 PM
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Otto-von-Guericke
Outline
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• Objectives
• Contents
• Course Details
• Communication Systems & Basic Concepts
• Introduction to OFDM and its concept
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Otto-von-Guericke
Objectives
Uni-Magdeburg
In order to comprehend learning
Institute for Electronics, signal Processing and Communication (IESK)
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• Digitize and Code Information Signals
• Transmit Digital Signals over different types of Channels
• De-noise and Decode received Digital Signals
• Characterize Communication Channels
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Otto-von-Guericke
Contents
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
•Sampling and Source Coding
•Base-Band Techniques (DM, ADM, PCM, DPCM)
•Pass-Band Techniques (ASK, PSK, FSK, QAM, MSK, GMSK)
•Wideband Techniques (SS-DS, SS-FH, CDMA, WCDMA, OFDM)
•Noise Reduction Techniques
•Terrestrial, Mobile and Satellite Communication Networks
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Otto-von-Guericke
Course Details
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
Teaching
Lecture and exercises
Prerequisites
Bachelor in Electrical Engineering or related studies
Probability and Random Processes
Weight
Compulsory module for the Master Course
“Electrical Engineering and Information Technology”
Exam
Written test at the end of the course
Credit points
4 Credit points = 120 h
(42 h time of attendance and
78 h autonomous work)
Work load
2 hours/week - lecture
1 hours/week - exercises
Autonomous work
Post processing of lectures,
Preparation of exercises and exam
Responsibly
Lectures
Exercises
Location & Time
Lectures (Every Tuesday 9:15-10:45 Building 22a, Room 04)
Exercises (Every Alternate Thursday 9:15-10:45 Building 05,
Room 313)
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Uni-Magdeburg
Prof. Dr. A.S. Omar [email protected]
M. Eng. Tariq J.S. Khanzada [email protected]
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Otto-von-Guericke
Useful literature Textbook
Uni-Magdeburg
Textbook
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
Digital Communication, 4th Ed.
John Proakis, MCGraw Hill 2000
Textbook website
www.mhhe.com/engcs/electrical/proakis
Additional Books
Introduction to Digital Communication,
Rodger E. Zeimer and Roger L. Peterson,
Second Edition, Prentice Hall, 2001.
Communication Systems (4th ed.), A. B. Carlson
Digital Communications, by Bernard Sklar,
Second Edition, Prentice Hall, 2001
Communication Systems , Simon Haykin, 4th Ed.
Wiley, 2001, ISBN 0-471-17869-1
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Excercises & Resources
Uni-Magdeburg
Exercises
Theoritical problems about the topics covered in lectures
Institute for Electronics, signal Processing and Communication (IESK)
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Implementation of concepts in programming language
of the choice
Recommonded tool
Matlab, C++, Java
Some Learning Web Resources
http://www.mathworks.com/moler/intro.pdf
http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.html
http://jdsp.asu.edu/jdsp.html
www.complextoreal.com
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Otto-von-Guericke
Communication
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
Device transfer information from one location (time) to another location (time)
Digital:
Smoke, Morse Code Telegraph
Analog:
Commercial Radio, TV
Digital:
Data, Computer, HDTV
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Otto-von-Guericke
Communication Systems
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de

Systems communicate in order to share information.

To communicate means to pass information from one place to another.

It is more convenient to convert information into a signal. Your concern
as a communication engineer is with the transmission and reception of
these signals.
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Components of Communication System
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
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Block diagram of Communication System
Source
Transmitter
Channel
(distortion)
Receiver
Destination
Noise
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Types of Communication Systems
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
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• Point to Point:
Telephone, Fax
• Point to Multipoint:
Broadcast (Radio, TV)
• Simplex:
One Way
• Duplex:
Two Ways
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Design Consideration
Uni-Magdeburg
Cost/Performance Trade Off
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Cost
Performance
Data Rate
Power
Bit Error Probability
Transmission Range
Bandwidth
Fault Tolerance
Adaptive to Environment
Complexity
Security
Anti Jamming Capability
Low Probability of Interception
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Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
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Digital Communication System
Uni-Magdeburg
•Source are converted into a sequence of binary digits which is called information sequence
Represent the source by an efficient number of binary digits
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•Efficiently converting the source into a sequence of binary digits is a process, which is called source
encoding of data compression
•Channel encoder adds some redundancy into binary information sequence that can be used for
handle noise and interference effects at the receiver.
•Digital modulator maps the binary information sequence into signal waveforms.
•Communication channel is used to send the signal from the transmitter to the receiver. Physical
channels: the atmosphere, wireless, optical, compact disk,….
•Digital demodulator receives transmitted signal contains the information which is corrupted by noise
•Cannel decoderattempts the reconstruct the original information sequence from knowledge of the
code used by channel encoder.
•Source decoder attempts the reconstruct the original signal from the binary information sequence
using the knowledge of the source encoding methods.
•The difference between the original signal and the reconstructed signal is measured of the distortion
introduced by the digital communication system
•Estimate what was send, aiming at the minimum possible probability of making mistakes
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Communication Channels and their Characteristics
Institute for Electronics, signal Processing and Communication (IESK)
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Physical Channel Media
magnetic-electrical signaled wire channel
modulated light beam optical (fiber) channel
antenna radiated wireless channel
acoustical signaled water channel
• Virtual Channel
magnetic storage media
• Noise Characteristic
thermal noise (additive noise)
signal attenuation
phase distortion
multi-path distortion
• Limitation of Channel Usage
transmitter power
receiver sensitivity
channel capacity (such as bandwidth)
Uni-Magdeburg
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Communication Channels and their Characteristics
Uni-Magdeburg
Additive Noise Channel
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where α is the attenuation factor, s(t) is the transmitted signal, and n(t) is
the additive random noise process.
• Called Additive Gaussian noise channel if n(t) is a Gaussian noise
process.
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Overview of Wireless Systems
Uni-Magdeburg
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• Guglielmo Marconi invented the wireless telegraph in 1896
– Communication by encoding alphanumeric characters in analog
signal
– Sent telegraphic signals across the Atlantic Ocean
• Communications satellites launched in 1960s
• Advances in wireless technology
– Radio, television, mobile telephone, communication satellites
• More recently
– Satellite communications, wireless networking, cellular technology
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Broadband Wireless Technology
Uni-Magdeburg
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• Higher data rates obtainable with broadband wireless
technology
– Graphics, video, audio
• Shares same advantages of all wireless services:
convenience and reduced cost
– Service can be deployed faster than fixed service
– No cost of cable plant
– Service is mobile, deployed almost anywhere
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Limitations and Difficulties of Wireless
Technologies
Uni-Magdeburg
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• Wireless is convenient and less expensive
• Limitations and political and technical difficulties inhibit
wireless technologies
• Lack of an industry-wide standard
• Device limitations
– E.g., small LCD on a mobile telephone can only
displaying a few lines of text
– E.g., browsers of most mobile wireless devices use
wireless markup language (WML) instead of HTML
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Electromagnetic Signal
Uni-Magdeburg
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• Function of time
• Can also be expressed as a function of frequency
– Signal consists of components of different frequencies
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Time-Domain Concepts
Uni-Magdeburg
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• Analog signal - signal intensity varies in a smooth fashion over time
– No breaks or discontinuities in the signal
• Digital signal - signal intensity maintains a constant level for some
period of time and then changes to another constant level
• Periodic signal - analog or digital signal pattern that repeats over time
–
s(t +T ) = s(t )
-< t < +
• where T is the period of the signal
• Aperiodic signal - analog or digital signal pattern that doesn't repeat
over time
• Peak amplitude (A) - maximum value or strength of the signal over
time; typically measured in volts
• Frequency (f )
– Rate, in cycles per second, or Hertz (Hz) at which the signal
repeats
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Time-Domain Concepts
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
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• Period (T ) - amount of time it takes for one repetition of
the signal
– T = 1/f
• Phase () - measure of the relative position in time within
a single period of a signal
• Wavelength () - distance occupied by a single cycle of
the signal
– Or, the distance between two points of corresponding
phase of two consecutive cycles
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Sine Wave Parameters
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
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• General sine wave
– s(t ) = A sin(2ft + )
• Figure 2.3 shows the effect of varying each of the three
parameters
– (a) A = 1, f = 1 Hz,  = 0; thus T = 1s
– (b) Reduced peak amplitude; A=0.5
– (c) Increased frequency; f = 2, thus T = ½
– (d) Phase shift;  = /4 radians (45 degrees)
• note: 2 radians = 360° = 1 period
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Sine Wave Parameters
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Time vs. Distance
Uni-Magdeburg
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• When the horizontal axis is time, as in Figure 2.3, graphs
display the value of a signal at a given point in space as a
function of time
• With the horizontal axis in space, graphs display the value
of a signal at a given point in time as a function of distance
– At a particular instant of time, the intensity of the signal
varies as a function of distance from the source
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Frequency-Domain Concepts
Uni-Magdeburg
• Fundamental frequency - when all frequency components
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of a signal are integer multiples of one frequency, it’s
referred to as the fundamental frequency
• Spectrum - range of frequencies that a signal contains
• Absolute bandwidth - width of the spectrum of a signal
• Effective bandwidth (or just bandwidth) - narrow band of
frequencies that most of the signal’s energy is contained in
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Frequency-Domain Concepts
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
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• Any electromagnetic signal can be shown to consist of a collection of
periodic analog signals (sine waves) at different amplitudes,
frequencies, and phases
• The period of the total signal is equal to the period of the fundamental
frequency
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Relationship between Data Rate and Bandwidth
Uni-Magdeburg
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• The greater the bandwidth, the higher the information-carrying
capacity
• Conclusions
– Any digital waveform will have infinite bandwidth
– BUT the transmission system will limit the bandwidth that can be
transmitted
– AND, for any given medium, the greater the bandwidth
transmitted, the greater the cost
– HOWEVER, limiting the bandwidth creates distortions
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Data Communication Terms
Uni-Magdeburg
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• Data - entities that convey meaning, or information
• Signals - electric or electromagnetic representations of data
• Transmission - communication of data by the propagation and
processing of signals
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Examples of Analog and Digital Data
Uni-Magdeburg
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• Analog
– Video
– Audio
• Digital
– Text
– Integers
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Analog Signals
Uni-Magdeburg
• A continuously varying electromagnetic wave that may be
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propagated over a variety of media, depending on
frequency
• Examples of media:
– Copper wire media (twisted pair and coaxial cable)
– Fiber optic cable
– Atmosphere or space propagation
• Analog signals can propagate analog and digital data
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Digital Signals
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• A sequence of voltage pulses that may be transmitted over a copper
wire medium
• Generally cheaper than analog signaling
• Less susceptible to noise interference
• Suffer more from attenuation
• Digital signals can propagate analog and digital data
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Analog Signaling
Digital Signaling
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Reasons for Choosing Data and Signal
Combinations
Uni-Magdeburg
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• Digital data, digital signal
– Equipment for encoding is less expensive than digital-to-analog
equipment
• Analog data, digital signal
– Conversion permits use of modern digital transmission and
switching equipment
• Digital data, analog signal
– Some transmission media will only propagate analog signals
– Examples include optical fiber and satellite
• Analog data, analog signal
– Analog data easily converted to analog signal
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Analog Transmission
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• Transmit analog signals without regard to content
• Attenuation limits length of transmission link
• Cascaded amplifiers boost signal’s energy for longer
distances but cause distortion
– Analog data can tolerate distortion
– Introduces errors in digital data
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Digital Transmission
Uni-Magdeburg
• Concerned with the content of the signal
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• Attenuation endangers integrity of data
• Digital Signal
– Repeaters achieve greater distance
– Repeaters recover the signal and retransmit
• Analog signal carrying digital data
– Retransmission device recovers the digital data from analog signal
– Generates new, clean analog signal
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About Channel Capacity
Uni-Magdeburg
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• Impairments, such as noise, limit data rate that can be achieved
• For digital data, to what extent do impairments limit data rate?
• Channel Capacity – the maximum rate at which data can be transmitted
over a given communication path, or channel, under given conditions
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Concepts Related to Channel Capacity
Uni-Magdeburg
• Data rate - rate at which data can be communicated (bps)
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• Bandwidth - the bandwidth of the transmitted signal as constrained by
the transmitter and the nature of the transmission medium (Hertz)
• Noise - average level of noise over the communications path
• Error rate - rate at which errors occur
– Error = transmit 1 and receive 0; transmit 0 and receive 1
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Spread-Spectrum
•
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•
•
•
Uni-Magdeburg
Spread-spectrum techniques are methods by which energy generated in a
particular bandwidth is deliberately spread in the frequency domain, resulting
in a signal with a wider bandwidth. These techniques are used for a variety of
reasons, including the establishment of secure communications, increasing
resistance to natural interference and jamming, and to prevent detection
Direct-sequence spread spectrum (DSSS) is a modulation technique. As with
other spread spectrum technologies, the transmitted signal takes up more
bandwidth than the information signal that is being modulated. The name
'spread spectrum' comes from the fact that the carrier signals occur over the
full bandwidth (spectrum) of a device's transmitting frequency.
Frequency-hopping spread spectrum (FHSS) is a method of transmitting
radio signals by rapidly switching a carrier among many frequency channels,
using a pseudorandom sequence known to both transmitter and receiver.
Code division multiple access (CDMA) describes a communication channel
access principle that employs spread-spectrum technology and a special coding
scheme (where each transmitter is assigned a code). By contrast, time division
multiple access (TDMA) divides access by time, while frequency-division
multiple access (FDMA) divides it by frequency. CDMA is a form of "spreadspectrum" signaling, since the modulated coded signal has a much higher
bandwidth than the data being communicated.
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Introduction to OFDM
Uni-Magdeburg
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
Orthogonal Frequency Division Multiplexing;

Part of xDSL, IEEE 802.11a standards

Improves Data rates, such as 56Mbps in IEEE 802.11a
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OFDM Concept
Uni-Magdeburg
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The information bit stream of high data rate r is Subdivided into M bit
blocks that are mapped onto symbols of a lower transmission rate rs = r /
M
Time Domain
Freq Domain
Bit stream
rs
M
Each Symbol has Duration Ts
and separated by guard intervals of duration Tg
Ts
Tg
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OFDM Concept
Uni-Magdeburg
• OFDM as multicarrier system uses Discrete Fourier
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Transform/Fast Fourier Transform (DFT/FFT)
• Sin(x)/x spectra for subcarriers
• Available bandwidth is divided
into very many narrow bands
~2000-8000 for digital TV
~48 for Hiperlan 2
• Data is transmitted in parallel
on these bands
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How are Signals transmitted in
parallel without interference?
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First three Subcarriers
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• Each subcarrier has a different
frequency
• Frequencies chosen so that
an integral number of cycles in
a symbol period
• Signals are
Orthogonal
mathematically
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How is data carried on the Subcarriers?
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•Data is carried by varying the phase
or amplitude of each subcarrier
QPSK, 4-QAM, 16-QAM, 64-QAM
Two possible subcarrier values
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What is Multipath?
Uni-Magdeburg
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• More than one transmission path
between transmitter and receiver
BS
D
ire
ct
p
at
h
• Received signal is the sum of
many versions of the transmitted
signal
with varying delay and
attenuation
MS
L 1
h( , t ) =
l 0
g l (t )
h( , t )   g l (t ) (t   l )
Chnnel
Impulse
Response
= Path Gain
delay
 l = Time
of path l
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Symbol Generation in MultiPath
Uni-Magdeburg
i = Symbol Iindex
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N = no of SubCarriers
N 1
xn(i )   sm(i ) e
j
2nm
N
l = multipath index
, 0  n  N 1
m 0
Sample of ith
xn(i ) = nth
OFDM Symbol
sm =
L 1
yn   hn(l ) xnl  wn  hn(0) xn  hn(1) xn1  ..... hn( L1) xn L1  wn
l 0
Trnsmitted Symbol on
mth Sub carrier
(l )
=
n
h
wn
=
Complex Random
variable for lth path of
channel
Additive White
Guassian Noise at
time n
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Effect of Multipath on received
Baseband Signal
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Received signal at any time depends on a number of transmitted bits
•Inter Symbol Interference (ISI)
•Need equalizer to recover data
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Overlapping the delayed multipath
signal with the following symbols
causes Inter-Symbol-Interference ISI.
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ISI gets Worse as Data Rate Increases
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ISI covers more symbol periods
•
Equalizer becomes too complicated
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Dealing with ISI in OFDM
Uni-Magdeburg
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OFDM is the most powerful
technique to combat ISI
because of the long symbol
duration

Symbol duration
GI
FFT Interval
ISI is almost completely
eliminated using a guard
interval

Extract a portion of an OFDM symbol at the end and append it to
the beginning to maintain the subcarriers orthogonal.

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Cyclic Prefix
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Each
Symbol
is
Cyclically Extended
Some loss in efficiency
as cyclic prefix carries
no new information
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Cyclic Prefix: Comparision
Uni-Magdeburg
To combat the time dispersion: including ‘special’ time guards in the symbol transitions
CP functions:
•It acomodates the decaying transient of the previous symbol
•It avoids the initial transient reachs the current symbol
CP

T
Tc
Including the Cyclic Prefix
Without the Cyclic Prefix
Symbol: 8 periods of fi
Initial transient
Channel: h(n)=(1) –n / n
n=0,…,23
Loss of orthogonality
Symbol: 4 periods of fi
Symbol: 8 periods of fi
Passing the channel h(n)
CP
Passing the channel h(n)
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copy
Decaying transient
Initial transient
remains within
the CP
The inclusion of a CP
maintains the orthogonality
Final transient
remains within
the CP
Symbol: 4 periods of fi
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Otto-von-Guericke
ICI effect on one Subcarrier
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
•Received signal in one
symbol period is not a
sinusoid
•Causes InterCarrier
Interference (ICI)
4/8/2015 3:56 PM
53
Otto-von-Guericke
ICI Elimination
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• ORTHOGONALITY :
Sub Carrier Orthogonality eliminates the ICI (Time-Invariant
Channels)
ICI (Time-Variant Channels)
Additional Signal Processing at receiver side is required to
eliminate ICI
….necessitate continuous monitoring of the
Channel
4/8/2015
3:56 PM
54
Otto-von-Guericke
Cyclic Prefix: Multipath
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
Tg >> τc
τc
Guard time Tg is chosen longer than the Channel Delay Spread τc
eliminates Inter Symbol Interference,
Tg >> τc
If multipath delay τc is less than the Cyclic Prefix
• No InterSymbol or InterCarrier Interference
• Amplitude may increase or decrease
4/8/2015 3:56 PM
55
Otto-von-Guericke
OFDM System Model
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
Transmitter
.....Bit Stream
Coding
/
Interle
aving
QAM /
PSK
Modul
ation
IFFT
P/S
GI
D/A
RF
Front
End
Channel
AWGN
.....to OFDM Reciever
4/8/2015 3:56 PM
56
Otto-von-Guericke
Modulation
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
•Varying the complex
numbers at the IFFT
input
results
modulation
of
in
the
subcarriers
4/8/2015 3:56 PM
57
Otto-von-Guericke
Spectrum of Received Signal
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
•Multipath
fading
causes
frequencies to be attenuated
some
•Fading is approximately constant over
narrow band
•This is corrected in the receiver
4/8/2015 3:56 PM
58
Otto-von-Guericke
Amplitude and Phase Change
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
•Multipath delay causes change in
amplitude and phase of each
subcarrier
•Change depends on subcarrier
frequency
•Corrected
complex
subcarrier
in receiver by
multiplication
one
per
4/8/2015 3:56 PM
59
Otto-von-Guericke
Advantages
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
High Spectral Efficiency
Simple Implementation
Tolerant to ISI
4/8/2015 3:56 PM
60
Otto-von-Guericke
Applications of OFDM
Uses of OFDM
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
OFDM was exploited for the following applications
• Wideband Data Communications over Mobile Radio FM Channels
• High-Bit-Rate Digital Subscriber Lines (HDSL; 1.6 Mbps)
• Asymmetric Digital Subscriber Lines (ADSL; up to 6 A4bps)
• Very-High-Speed Digital Subscriber Lines (VDSL; 100 Mbps)
4/8/2015 3:56 PM
61
Otto-von-Guericke
Problems with OFDM
High Peak-to-Average Power
Uni-Magdeburg
• High Peak to Average Power Ratio…caused by the constructive
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
interference between many carriers…
may occur at few time
instants within the symbol duration.
• OFDM signal is sum of
many separate sinusoids
• In worst case may all add
constructively
• High peaks occur rarely
4/8/2015 3:56 PM
62
Otto-von-Guericke
Problems with OFDM
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• Due to PAPR necessity of having very wide linearity dynamic-range
for the power amplifiers at the transmitter RF stage
4/8/2015 3:56 PM
63
Otto-von-Guericke
Problems with OFDM
Frequency Offset Sensitivity
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• Individual subcarriers have sin(x)/x spectrum
• Large sidelobes result in sensitivity to frequency offset
• Subcarriers no longer orthogonal
• Tight specifications on local oscillators
4/8/2015 3:56 PM
64
Otto-von-Guericke
Problems with OFDM
ICI in Time Variant Channels
Uni-Magdeburg
Non-orthogonality in time view
1
0.5
Magnitude
0
-0.5
-1
0
2
4
6
8
10
12
14
16
18 18.87
time
Non-orthogonality in frequency view
Amplitude
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• OFDM is sensitive to frequency offsets and phase noise
• Doppler shifts cause ICI
Frequency
4/8/2015 3:56 PM
65
Otto-von-Guericke
Solutions to ICI
Channel Estimation Techniques
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• Channel Estimation Techniques are applied to
overcome ICI
• The estimated channel transfer function ( regularly
updated by sending pilots signals) is used to recover the
ICI free signal at the receiver
4/8/2015 3:56 PM
66
Otto-von-Guericke
Solutions to Peak-to-Average Power
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• Clipping the peaks
• Coding to avoid the peaks
• Peak windowing
• Predistort the signal to compensate for the amplifier nonlinearity
• But most of them are unable to achieve large rduction in
PAPR with low complexity
These Techniques FAIL when the Channel
Characteristics Change considerably within the
Symbol Duration
4/8/2015 3:56 PM
67
Otto-von-Guericke
A BETTER SOLUTIONAN ALTERNATIVE PROPOSED TECHNIQUE
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de

The Alternative Technique maintains the Major Advantages of
OFDM
  Keeping global OFDM Signal Structure
Transmitting Symbol duration Ts separated by guard
intervals Tg being longer than Maximum Channel Delay
Spread τc to eliminate ISI
SLTDM: Global OFDM
structure
Guard Time
…
…
…
Sub-Symbol
4/8/2015 3:56 PM
Delay Spread
Symbol Time
…
…
…
No ISI
68
Otto-von-Guericke
PROPOSED TECHNIQUE
Different with OFDM
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
OFDM :
Uni-Magdeburg
Maps a data block onto corresponding Symbol using FD
SLTDM : uses Time Division Multiplexing Scheme …
suggests Symbol Level Time Division Multiplexing Techn
SLTDM
The bit of M-bit block would be firstly Scrambled
Then subdivided into N equal Sub-Blocks, that would be mapped onto N SubSymbols
Sub-Symbol
1
...
Symbol
N
PSK / QAM
fo
PSK / QAM
fo
Delay
:
fo
Delay 2*Ts/N
:
PSK / QAM
4/8/2015 3:56 PM
Ts/N
:
fo
Delay (N-1)*Ts/N
Same Carrier fo
69
Otto-von-Guericke
PROPOSED TECHNIQUE
Model of SLTDM
Uni-Magdeburg
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
Symbol Level TDM
.....bit Stream
Coding
/
Interle
aving
Sub
Symbol
Seperation
S/P
QAM /
PSK
Modul
ation
P/S
GI
D/A
Sub
Symbo
l
Time Division Multiplexing Scheme …
RF
Front
End
Channel
suggests Symbol Level Time Division Multiplexing Technique
SLTDM
AWGN
.....to SLTDM Reciever
4/8/2015 3:56 PM
70
Otto-von-Guericke
PROPOSED TECHNIQUE
Expected ISSI and its Resolution
Uni-Magdeburg
To be Done
Institute for Electronics, signal Processing and Communication (IESK)
http://iesk.et.uni-magdeburg.de
• The problem of ISSI (Inter Sub Symbol Interference) would occur
• ISSI can be eliminated, if enough information about the Channel
Characteristics is available to the receiver
• These can be gained using well known Channel Characterizing
and Channel Estimation
Techniques
SLTDM benefit :
All symbol have the same Envelop WaveForm
Can lead to very moderate PAPR
If the Sybmol Envelop WaveForm is properly
designed
4/8/2015 3:56 PM
71
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