Transcript Channel

Digital Communication
Mr. Sajid Gul Khawaja
Overview
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Course Information
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Course Schedule
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Prerequisites
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Books
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Scoring\Grading
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Expectations
Digital Systems
Introduction to digital communication systems
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Course Info
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Prerequisites
 Probability and random variables
 Digital Signal Processing
Course materials
 Course text books:
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“Communication Systems Engineering”, by John G. Proakis and
Masoud Salehi, Prentice Hall, 2002, 2nd edition, ISBN: 0-13-095007-6
“Principles of Digital Communications”, Gallager
“Digital Communications: Fundamentals and Applications” by
Bernard Sklar,Prentice Hall, 2001, ISBN: 0-13-084788-7
“Communication Systems” by Simon Haykin 4th Edition
Additional recommended books:
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“Digital Communications”, by Ian A. Glover and Peter M. Grant,
Pearson, Prentice Hall, 2004, 2nd edition, ISBN: 0-13-089399-4
Course Schedule
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14-16 lectures
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2-4 Quizzes
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2-4 home assignments
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Written assignments may not be graded
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2 Sessional Exams
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Practical Work
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Final Exam
Score/Grading
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Tentative marks division
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2 Sessional Exams
25~30%
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Reading Assignments
5~10%
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Quizzes
5%
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Practical
20~25%
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Lab
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Project
Final Examination
40~45%
Expectations/Objectives
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Mine
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Deliver the concepts of digital communications
Understand the following about the different blocks of
digital communication
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What
Why
When
How
Eventually forming a prototype system
Yours’
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Getting through this course (majority)
Getting an A
Learn something new
Course Outline
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Introduction to DC
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Some Probability Theory
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Source Coding
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Probability space, random variables, density functions,
independence
Expectation, conditional expectation, Baye’s rule
Stochastic processes, autocorrelation function, stationary,
spectral density
Measuring information, entropy, the source coding
theorem
Huffman coding, Run-length coding, Lempel-Ziv etc.
Analog-to-digital conversion
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Sampling (ideal, natural, sample-and-hold)
Quantization, PCM
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Communication channels
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Band-limited channels
The AWGN channel, fading channels
Receiver design
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General binary and M-ary signaling
Maximum-likelihood receivers
Performance in an AWGN channel
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The Chernoff and union/Chernoff bounds
Simulation techniques
Signal spaces
Modulation: PAM, QAM, PSK, DPSK, coherent FSK,
incoherent FSK
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Channel coding
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Block codes, hard and soft-decision decoding,
performance
Convolutional codes, the Viterbi algorithm,
performance bounds
Trellis-coded modulation (TCM)
Signaling through bandlimited channels
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ISI, Nyquist pulses, sequence estimation, partial
response signaling
Equalization
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Signaling through fading channels
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Rayleigh fading, optimum receiver, performance
Interleaving
Synchronization or Link Estimation
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Symbol synchronization
Frame synchronization
Carrier synchronization
What is Digital
Communication?
Digital Communications
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Digital Communication:
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Enormous and normally rapidly growing industry
Objective:
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Study those aspects of communication systems
unique to those systems. Little focus on hardware
or software
Hardware and software are similar to other
systems.
Basis of Digital Communication
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Information theory, developed in 1948 by
Claude Shannon
Reading Assignment
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A Mathematical Theory of Communication By C.
E. SHANNON
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Complex relationship between modeling, theory,
exercises, and engineering/design.
Use very simple models to understand ideas. This
generates powerful general theorems plus insights
into more complex models and thus reality.
Exercises aimed at understanding the principles
getting the right answer is not the point since the
model is oversimplified.
Engineering deals with approximations and
judgment calls based on multiple simple models
(insights).
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Since the exercises apply only to simple
models, they don’t apply directly to real
systems.
You have to understand the exercise at a gut
level to see how to use the idea.
This is why you should discuss the exercises
with other students –getting the correct
answer by pattern matching and manipulation
is not the point.
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Everyday communication systems (the
telephone system, the Internet) have
incredible complexity.
Must be designed and understood based on
simple architectural principles.
Standardized interfaces and layering are key.
Why Digital Communication?
Device Challenges
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Analog and RF Components
A/D Converters
Size, Power, Cost
Multiple Antennas
Multiradio Coexistance
These challenges may
someday be completely
solved by a
software-defined radio
A/D
A/D
A/D
A/D
DSP
BT
Cellular
FM/XM
GPS
DVB-H
Apps
Processor
WLAN
Media
Processor
Wimax
Design Challenges
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Hardware Design
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System Design
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Precise components
Small, lightweight, low power
Cheap
High frequency operation
Converting and transferring information
High data rates
Robust to noise and interference
Supports many users
Network Design
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Connectivity and high speed
Energy and delay constraints
Advantages of Digital Systems
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Error correction/detection
Better encryption algorithms: Can not be
done in analog communication
More reliable data processing
Easily reproducible designs
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Reduced cost
Easier data multiplexing
Facilitate data compression
Disadvantages:
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Heavy signal processing
Synchronization is crucial
Larger transmission bandwidth
Non-graceful degradation
Goals in Communication System Design
To maximize transmission rate, R
R
U
To maximize system utilization, U
To minimize bit error rate, Pe
To minimize required systems bandwidth, W
To minimize system complexity, Cx
To minimize required power, Eb/No
23
Pe
w
cx Eb/No
Where is Digital Communication
Embedded in a Digital System?
Data Network Protocols
and the OSI Model
Examples of Digital System
from everyday life
Communication Systems
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Provide for electronic exchange of multimedia data
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Voice, data, video, music, email, web pages, etc.
Communication Systems Today
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Radio and TV broadcasting
Public Switched Telephone Network (voice,fax,modem)
Cellular Phones
Computer networks (LANs, WANs, and the Internet)
Satellite systems (pagers, voice/data, movie broadcasts)
Bluetooth
An Overview of the Digital
System
Main Points
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Communication systems send information
electronically over communication channels
Many different types of systems which convey
many different types of information
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Design challenges include hardware, system, and
network issues
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Communication systems recreate transmitted
information at receiver with high fidelity
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Focus of this class is design and performance of
analog and digital communication systems
Information Source and Sinks
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Information Source and Input Transducer:
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The source of information can be analog or digital,
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Analog: audio or video signal,
Digital: like teletype signal.
In digital communication the signal produced by
this source is converted into digital signal consists
of 1′s and 0′s.
Output Transducer:
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The signal in desired format analog or digital at
the output
Channel
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Channel:
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The communication channel is the physical
medium that is used for transmitting signals from
transmitter to receiver
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Wireless channels: Wireless Systems
Wired Channels: Telephony
Channel discrimination on the basis of their
property and characteristics, like AWGN channel
etc.
Source Encoder and Decoder
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Source Encoder
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In digital communication we convert the signal from source into
digital signal. The point to remember is we should like to use as
few binary digits as possible to represent the signal. In such a
way this efficient representation of the source output results in
little or no redundancy. This sequence of binary digits is
called information sequence.
Source Encoding or Data Compression: the process of
efficiently converting the output of wither analog or digital source
into a sequence of binary digits is known as source encoding.
Source Decoder
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At the end, if an analog signal is desired then source decoder
tries to decode the sequence from the knowledge of the encoding
algorithm. And which results in the approximate replica of the
input at the transmitter end
Channel Encoder and Decoder
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Channel Encoder:
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The information sequence is passed through the channel encoder. The
purpose of the channel encoder is to introduce, in controlled manner,
some redundancy in the binary information sequence that can be used
at the receiver to overcome the effects of noise and interference
encountered in the transmission on the signal through the channel.
e.g. take k bits of the information sequence and map that k bits to unique
n bit sequence called code word. The amount of redundancy introduced
is measured by the ratio n/k and the reciprocal of this ratio (k/n) is known
as rate of code or code rate.
Channel Decoder:
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Channel decoder attempts to reconstruct the original information
sequence from the knowledge of the code used by the channel encoder
and the redundancy contained in the received data
Digital Modulator and Demodulator
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Digital Modulator:
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The binary sequence is passed to digital modulator
which in turns convert the sequence into electric
signals so that we can transmit them on channel. The
digital modulator maps the binary sequences into
signal wave forms , for example if we represent 1 by
sin x and 0 by cos x then we will transmit sin x for 1
and cos x for 0.
Digital Demodulator:
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The digital demodulator processes the channel
corrupted transmitted waveform and reduces the
waveform to the sequence of numbers that represents
estimates of the transmitted data symbols.
The Main Points
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The point worth noting are :
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The source coding algorithm plays an important role
in higher code rate
The channel encoder introduce redundancy in data
The modulation scheme plays important role in
deciding the data rate and immunity of signal towards
the errors introduced by the channel
Channel can introduce many types of errors due to
thermal noise etc.
The demodulator and decoder should provide high
Bit Error Rate (BER).
Block Diagram of a Digital
System
Step Wise
Layering of Source Coding
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Source coding includes
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Sampling
Quantization
Symbols to bits
Compression
Decoding includes
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Decompression
Bits to symbols
Symbols to sequence of numbers
Sequence to waveform (Reconstruction)
Layering of Source Coding
Layering of Channel Coding
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Channel Coding is divided into
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Discrete encoder\Decoder
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Used to correct channel Errors
Modulation\Demodulation
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Used to map bits to waveform for transmission
Layering of Channel Coding