Name: Department of Communications and Computer Engineering Faculty of Engineering – Cairo University ELC N316 - Communications II
Uploaded: 2021-10-22
Duration: 37 min 23 s
Description: Department of Communications and Computer Engineering Faculty of Engineering – Cairo University ELC N316 - Communications II.

Department of Communications and Computer Engineering Faculty of Engineering – Cairo University ELC N316 - Communications II. 

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Department of Communications and Computer Engineering Faculty of Engineering – Cairo University ELC N316 -  Communications II

Course Logistics. Instructor: Dr. Ahmed Khattab E-mail: ahmed.khattab@cu.edu.eg Office Hours: Wednesday 3:30-5:00 or by email appointment. Office: 4th floor, Electronics and communications engineering building. Grade Distribution: Assignments (10%): 3 assignments that are turned in every 4 weeks. Quizzes (10%): 3 quizzes, best 2 out of 3 are counted. Midterm (20%) Lab Work (10%) Project(s) (10%): MATLAB-based project(s). Final (40%) Textbooks: “Communication Systems”, Simon Haykin , 4th , 5th editions. “Modern Digital and Analog Communication Systems”, B. P. Lathi , 3rd edition.. 

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Instructor: Dr. Ahmed  Khattab E-mail: ahmed.khattab@cu.edu.eg Office Hours:  Wednesday 3:30-5:00  or by email appointment. Office: 4th floor, Electronics and communications engineering building. Grade Distribution: Assignments (10%): 3 assignments that are turned in every 4 weeks. Quizzes (10%): 3 quizzes, best 2 out of 3 are counted. Midterm (20%) Lab Work (10%) Project(s) (10%): MATLAB-based project(s). Final (40%) Textbooks: “Communication Systems”, Simon  Haykin , 4th , 5th editions. “Modern Digital and Analog Communication Systems”, B. P.  Lathi , 3rd edition.

Course Outline. Baseband Data Transmission Matched filters and their properties. Error rate due to noise. Signal constellation and Gram-Schmidt orthogonalization procedure. MAP and ML decoding rules. Correlator receiver and probability of error calculation. Passband Data Transmission Digital Modulation schemes - Description of ASK, FSK, PSK and DPSK modulations. Their implementation , PSD c/ cs , B.W efficiency (spectral efficiency (. QAM system. Noise in digital modulation - Coherent detection of signals in noise-Probability of error. Non-coherent orthogonal modulation.. 

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Baseband Data Transmission Matched filters and their properties. Error rate due to noise. Signal constellation and Gram-Schmidt  orthogonalization  procedure. MAP and ML decoding rules. Correlator receiver and probability of error calculation. Passband Data Transmission Digital Modulation schemes - Description of ASK, FSK, PSK and DPSK modulations. Their implementation , PSD c/ cs , B.W efficiency (spectral efficiency (. QAM system. Noise in digital modulation - Coherent detection of signals in noise-Probability of error. Non-coherent orthogonal modulation.

Introduction. Communication is a process by which information is exchanged between individuals through a common system of symbols, signs or behavior. Examples: Public switched telephone network (PSTN), mobile telephone communications (GSM, 3G, 4G…), WiFi , WiMAX, broadcast radio or television, navigation systems, …. 

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Communication is a process by which information is exchanged between individuals through a common system of symbols, signs or behavior.  Examples: Public switched telephone network (PSTN), mobile telephone communications (GSM, 3G, 4G…),   WiFi , WiMAX, broadcast radio or television, navigation systems, …

Today’s Class. What is a Digital Comminutions System (DCS)? DCS advantages and disadvantages Preliminaries and necessary review Classification of signals Random processes Noise in communication systems Signal transmission through linear systems Bandwidth of signals. 

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What is a Digital Comminutions System (DCS)? DCS advantages and disadvantages Preliminaries and necessary review Classification of signals Random processes Noise in communication systems Signal transmission through linear systems Bandwidth of signals

Digital Communications Systems. The course is aiming at introducing the fundamental issues in designing and analyzing a digital communication system.. 

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The course is aiming at introducing the fundamental issues in designing and analyzing a  digital  communication system.

N Oise
Inn
Transmitter
Source
Transmitted
signal
Received
signal
Received
info.
Receiver
Source
Formatter
encoder
Source
Formatter
decoder
Transmitter
Channel
Modulator
encoder
Receiver
Channel
Demodulator

Digital Communications Basics. A transmitter (TX) sends a waveform (symbol) from a finite set of possible waveforms during a finite time (symbol time). How does this compare to analog comm.? A channel distorts, attenuates, and adds noise to the transmitted waveform. A receiver (RX) should decide which waveform was transmitted from the noisy received signal. Probability of erroneous decision is an important measure of the DCS performance. 

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A  transmitter  (TX) sends a waveform (symbol) from a finite set of possible waveforms during a finite time (symbol time). How does this compare to analog comm.? A  channel  distorts, attenuates, and adds noise to the transmitted waveform. A  receiver  (RX) should decide which waveform was transmitted from the noisy received signal. Probability of erroneous decision is an important measure of the DCS performance

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DCS Advantages. Robustness: through error control coding Regenerative repeaters. Immunity to noise A bit is a bit (voice, data, video, ….) Efficient storage and retrieval. Better security (encryption) TDM Compression and Source Coding Adaptive Signal Processing Flexibility(Software-Defined Radio). 

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Robustness: through error control coding  Regenerative repeaters. Immunity to noise A bit is a bit (voice, data, video, ….) Efficient storage and retrieval. Better security (encryption) TDM Compression and Source Coding Adaptive Signal Processing Flexibility(Software-Defined Radio)

DCS Disadvantages. Quantization noise Complexity Higher bandwidth requirements. 

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Quantization noise Complexity Higher bandwidth requirements

Classification of Signals. Deterministic Signal No uncertainty about the signal value at any time Random Signal Some degree of uncertainty in the signal values (a different signal each time) Thermal noise in electronic circuit due to the random movement of electrons. Reflections of radio waves from different layers of ionosphere. 

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Deterministic Signal No uncertainty about the signal value at any time Random Signal Some degree of uncertainty in the signal values (a different signal each time) Thermal noise in electronic circuit  due to the random movement of electrons. Reflections of radio waves from different layers of ionosphere

https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcTXgiPGSNmy7g_Aob6E_kgXpNcQwN6YmirGXBVQ4iPiRytaA4nb6A

Classification of Signals. 12. Periodic and non-periodic signals x(t) t A periodic signal A non-periodic signal t a Analog and discrete signals x(t) x(t) t A discrete signal Analog signals. 

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Periodic and non-periodic signals
x(t)
t
A periodic signal
A non-periodic signal
t
a Analog and discrete signals
x(t)
x(t)
t
A discrete signal
Analog signals

Classification of Signals. Energy Signal A signal is an energy signal if, and only if, it has a finite but nonzero energy for all the time Power Signal A signal is a power signal if, and only if, it has a finite but nonzero power for all the time General Rule: Periodic and random signals are power signals. Signals that are both deterministic and non-periodic are energy signals.. 

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Energy Signal A signal is an energy signal if, and only if, it has a finite but nonzero  energy  for all the time Power Signal A signal is a power signal if, and only if, it has a finite but nonzero  power  for all the time General Rule:   Periodic and random signals are power signals. Signals that are both deterministic and non-periodic are energy signals.

Random Processes. A random process is a collection of time functions, or signals, corresponding to various outcomes of a random experiment. For each outcome, there exists a deterministic function, which is called a sample function or a realization. 

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A random process is a collection of time functions, or signals, corresponding to various outcomes of a random experiment.   For each outcome, there exists a deterministic function, which is called a sample function or a realization

Xl(t)
X2(t)
XN(t)
Random
variables
Sample functions
or realizations
(deterministic
function)
time (t)

The Autocorrelation Function. Autocorrelation of an energy signal Autocorrelation of a power signal Autocorrelation of a random signal For a WSS process (does not depend on the time origin):. 

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Autocorrelation of an energy signal Autocorrelation of a power signal Autocorrelation of a random signal For a WSS process (does not depend on the time origin):

Autocorrelation Function Properties. For a real valued (and WSS in case of random signals): Autocorrelation functions and spectral density form a Fourier transform pair. Autocorrelation is symmetric around zero. Its maximum value occurs at the origin. Its value at the origin is equal to the average power or energy.. 

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For a real valued (and WSS in case of random signals): Autocorrelation functions and spectral density form a Fourier transform pair. Autocorrelation is symmetric around zero.  Its maximum value occurs at the origin.  Its value at the origin is equal to the average power or energy.

Spectral Density. 17. Energy signals Energy spectral density (ESD): a Power signals 1 To/2 = föJ-To/2 = EN lcn12 Power spectral density (PSD): x(f) = Vx(f) = E lcn126(f-nf0) fo=1/T0 Random process Power spectral density (PSD):. 

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Energy signals
Energy spectral density (ESD):
a Power signals
1 To/2
= föJ-To/2 = EN
lcn12
Power spectral density (PSD):
x(f) =
Vx(f) =
E lcn126(f-nf0) fo=1/T0
Random process
Power spectral density (PSD):

Noise in Communications Systems. Thermal noise is described be a zero-mean Gaussian random process: n(t) Its PSD is flat, and hence, it is called white noise. 

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Thermal noise is described be a  zero-mean Gaussian random process: n(t) Its PSD is flat, and hence, it is called  white noise

1
exp
27T
0.4
0.3.
0.2
-4
a
-2
712
4
Power spectral
density
rtn(T)
Autocorrelation
function
[w/Hz]
= N2Q6(T)
2
Probability density function

Signal Transmission Through Linear Systems. Ideal distortion-less transmission All the frequency components of the signal does not only arrive with an identical time delay , but also are equally amplified or attenuated. 

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Signal Transmission Through Linear Systems

Ideal distortion-less transmission All the frequency components of the signal does not only arrive with an  identical time delay , but also are  equally  amplified or attenuated

h(t)
Input
X(f)
y(t)
Y (f) Output
Linear system
Deterministic signals: Y (f) =
Random signals: Gy(f) = Gx

Ideal Filters. 20. Low-pass Band-pass h(t) o Non-causal! t High-pass. 

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Low-pass
Band-pass
h(t)
o
Non-causal!
t
High-pass

Bandwidth of a Signal. Baseband versus bandpass Bandwidth Dilemma Band-limited signals are not realizable! Realizable signals have infinite bandwidth!. 

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Baseband versus  bandpass Bandwidth Dilemma Band-limited signals are not realizable! Realizable signals have infinite bandwidth!

gsan,
i(JYxl
leu6!S
ssedpueg
assm
umell!oso poon
(Pfuc)S03 leu6!S
pueqeseg
(70fuz)soo
= (DOT

Different Definitions of Bandwidth. 22. Half-power bandwidth Noise equivalent bandwidth Null-to-null bandwidth Fractional power containment bandwidth Bounded power spectral density Absolute bandwidth f Gz(f) (a) (b) (d) (e)50dB. 

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Half-power bandwidth
Noise equivalent bandwidth
Null-to-null bandwidth
Fractional power containment bandwidth
Bounded power spectral density
Absolute bandwidth
f
Gz(f)
(a)
(b)
(d)
(e)50dB

Formatting and Transmission of Baseband Signals. To transform an analog waveform into a form that is compatible with digital communications, the following steps are to be taken: Sampling Quantization and encoding Baseband transmission. 

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Formatting and Transmission of Baseband Signals

To transform an analog waveform into a form that is compatible with digital communications, the following steps are to be taken: Sampling Quantization and encoding Baseband transmission

source
sink
Dig
Tex
info.
info.
Analog
Textual
Sample
Quantize
Encode
Pulse
modulate
Bit stream
wavefo
Demodulate/
Detect
Transmit
Channel
Receive
ow-pass
Decode
filter
info. -
Digital info.

The Sampling Process. 24. x(t) domain = x txxt Ts 2Ts3Tst I's 2T.3T8t Frec uencv dotnain o. 

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x(t)
domain
= x txxt
Ts 2Ts3Tst
I's 2T.3T8t
Frec uencv dotnain
o

Aliasing Effect. 25. s s s LP filter Nyquist rate S fs = 2fm aliasing fs > 2fm fs f. 

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s
s
s
LP filter
Nyquist rate
S
fs = 2fm
aliasing
fs > 2fm
fs f

Quantization. We are still not “digital” yet, why? Amplitude quantization is the process of mapping samples of a continuous amplitude waveform to a finite set of amplitudes.. 

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We are still not “digital” yet, why? Amplitude quantization is the process of mapping samples of a continuous amplitude waveform to a  finite  set of amplitudes.

Ll
—Ä/2
—V'p + 3A/2
-V; + A/2
els
So, what is the quantizer
characteristic function?!
Out
A stair case function!

Formatting and Transmission of Baseband Signals. 27. 

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Digital info.
Textual -
source info. :
Analog :
Sample
I Samphng at rate
1
Bit stream
ata bits)
Pulse waveforms
(baseband signals)
uantiz
Encode
Pulse
Inodulate
Encoding each quantized value to
R = log2Lbits
(Data bit
Quantizing each sampled I
I value to one of the I
I L levels m quantizer.
Information (data) rate:
0
o
I Mapprng every m = log: M data bits to a
I symbol out of M symbols and transmitting
a baseband waveform with duration T
Tb [bits/sec]
Symbol rate: Rs = I T [symbols see]
For real time transmission: Rb = mRs

Line Codes for Binary Transmission. 28. Binarydata 0 11 11 10 11 10 1 0 1 1 Unipolar NRZ Polar NRZ Unipolar RZ Bipolar RZ Split Phase - Manchester .Time. 

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Binarydata 0 11 11 10 11 10 1 0 1 1
Unipolar NRZ
Polar NRZ
Unipolar RZ
Bipolar RZ
Split Phase - Manchester
.Time

Criteria for Comparing Line Codes. Spectral Characteristics Power spectral density and bandwidth efficiency Bandwidth should be as small as possible No DC component Power Efficiency The transmitted power should be as small as possible for a given BW and error probability Error detection/correction probability Should be easy to detect errors and possibly correct them Bit Synchronization Should be able to extract time (clock) information from the line code Implementation cost and complex ity. 

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Spectral Characteristics  Power spectral density and bandwidth efficiency Bandwidth should be as small as possible No DC component Power Efficiency The transmitted power should be as small as possible for a given BW and error probability  Error detection/correction probability Should be easy to detect errors and possibly correct them Bit Synchronization Should be able to extract time (clock) information from the line code Implementation cost and complex ity

Spectra of Line Codes. 30. •met 112 Unipolar NRZ Unipolar RZ 0.5 Polar NRZ 0.9 Bipolar RZ. 

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•met 112
Unipolar NRZ
Unipolar RZ
0.5
Polar NRZ
0.9
Bipolar RZ

Example of M - ary PAM. 31. (rectangular pulse) '01' 3B iiiiiiiiiii. 

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(rectangular pulse)
'01'
3B
iiiiiiiiiii