Digital Communication Mr. Sajid Gul Khawaja

Overview Course Information Course Schedule Scoring\Grading Prerequisites Books Scoring\Grading Expectations Digital Systems Introduction to digital communication systems

Course Info Prerequisites Course materials Probability and random variables Digital Signal Processing Course materials Course text books: “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: “Digital Communications”, by Ian A. Glover and Peter M. Grant, Pearson, Prentice Hall, 2004, 2nd edition, ISBN: 0-13-089399-4

Course Schedule 14-16 lectures 2-4 Quizzes 2-4 home assignments Written assignments may not be graded 2 Sessional Exams Practical Work Final Exam

Score/Grading Tentative marks division 2 Sessional Exams 25~30% Reading Assignments 5~10% Quizzes 5% Practical 20~25% Lab Project Final Examination 40~45%

Expectations/Objectives Mine Deliver the concepts of digital communications Understand the following about the different blocks of digital communication What Why When How Eventually forming a prototype system Yours’ Getting through this course (majority) Getting an A Learn something new

Course Outline Introduction to DC Source Coding Some Probability Theory Probability space, random variables, density functions, independence Expectation, conditional expectation, Baye’s rule Stochastic processes, autocorrelation function, stationary, spectral density Source Coding Measuring information, entropy, the source coding theorem Huffman coding, Run-length coding, Lempel-Ziv etc. Analog-to-digital conversion Sampling (ideal, natural, sample-and-hold) Quantization, PCM

Communication channels Band-limited channels The AWGN channel, fading channels Receiver design General binary and M-ary signaling Maximum-likelihood receivers Performance in an AWGN channel The Chernoff and union/Chernoff bounds Simulation techniques Signal spaces Modulation: PAM, QAM, PSK, DPSK, coherent FSK, incoherent FSK

Signaling through bandlimited channels Channel coding Block codes, hard and soft-decision decoding, performance Convolutional codes, the Viterbi algorithm, performance bounds Trellis-coded modulation (TCM) Signaling through bandlimited channels ISI, Nyquist pulses, sequence estimation, partial response signaling Equalization

Signaling through fading channels Rayleigh fading, optimum receiver, performance Interleaving Synchronization or Link Estimation Symbol synchronization Frame synchronization Carrier synchronization

What is Digital Communication?

Digital Communications Enormous and normally rapidly growing industry Objective: 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 Information theory, developed in 1948 by Claude Shannon Reading Assignment A Mathematical Theory of Communication By C. E. SHANNON

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).

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.

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 Analog and RF Components A/D Converters Size, Power, Cost Multiple Antennas Multiradio Coexistance Cellular Apps Processor BT Media GPS WLAN Wimax DVB-H FM/XM A/D DSP These challenges may someday be completely solved by a software-defined radio

Design Challenges Hardware Design System Design Network Design Precise components Small, lightweight, low power Cheap High frequency operation System Design Converting and transferring information High data rates Robust to noise and interference Supports many users Network Design Connectivity and high speed Energy and delay constraints

Advantages of Digital Systems Error correction/detection Better encryption algorithms: Can not be done in analog communication More reliable data processing Easily reproducible designs Reduced cost Easier data multiplexing Facilitate data compression

Disadvantages: Heavy signal processing Synchronization is crucial Larger transmission bandwidth Non-graceful degradation

Goals in Communication System Design To maximize transmission rate, R 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 R U 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 Provide for electronic exchange of multimedia data Voice, data, video, music, email, web pages, etc. Communication Systems Today 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 Communication systems send information electronically over communication channels Many different types of systems which convey many different types of information Design challenges include hardware, system, and network issues Communication systems recreate transmitted information at receiver with high fidelity Focus of this class is design and performance of analog and digital communication systems

Information Source and Sinks Information Source and Input Transducer: The source of information can be analog or digital, 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: The signal in desired format analog or digital at the output

Channel Channel: The communication channel is the physical medium that is used for transmitting signals from transmitter to receiver 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 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 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 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: 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 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: 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 The point worth noting are : 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 Source coding includes Sampling Quantization Symbols to bits Compression Decoding includes Decompression Bits to symbols Symbols to sequence of numbers Sequence to waveform (Reconstruction)

Layering of Source Coding

Layering of Channel Coding Channel Coding is divided into Discrete encoder\Decoder Used to correct channel Errors Modulation\Demodulation Used to map bits to waveform for transmission

Layering of Channel Coding