CT516 Advanced Digital Communications Lecture 1: …intranet.daiict.ac.in/~yash_vasavada/Courses/Spring2017/CT516/...CT516 Advanced Digital Communications Lecture 1: Introduction

Introduction Course Mechanics Course Overview Why Digital Communications?

CT516 Advanced Digital CommunicationsLecture 1: Introduction

Yash M. Vasavada

Associate Professor, DA-IICT, Gandhinagar

2nd January 2017

Yash M. Vasavada (DA-IICT) CT516: Adv. Digital Comm. 2nd January 2017 1 / 23


Overview of Today’s Talk1 Introduction2 Course Mechanics3 Course Overview4 Why Digital Communications?












About Myself

Instructor: Yash M. Vasavada

→ B.E. in Electronics and Communications from L. D. EngineeringCollege, Gujarat University, Ahmedabad, India

→ M.S. and Ph.D. in Electrical Engineering from Virginia PolytechnicInstitute and State University, Blacksburg, VA, USA

→ I was with Hughes Network Systems in Germantown, MD, USA until2016

→ Beginning of the year 2016, I have been with DA-IICT



My Research

I am interested in

the confluence of Machine Learning with Digital and WirelessCommunications

development of new algorithms, a study of their properties, and astudy of their applications in communications, signal processing andartificial intelligence



Overview

This is a L-T-P-Cr: 3− 0− 2− 4 Course.

Lecture venue: CEP 104. Lecture time:1 Mondays, 9 to 9:55 am.2 Wednesdays, Noon to 12:55 am.3 Fridays, 8 to 8:55 am.

Lab Sessions: TBD



Methods of Communicationsduring the Semester

You can contact me at:

Office: FB-1, Room 1211Phone: 30510634Email: yash [email protected]

Assignments will be through the Moodle course page

Announcements and other communication will be through Moodle aswell as through DA-IICT email



Textbook

Main Textbook: John G. Proakis, Digital Communications, Fourth Edition.

Supporting Textbooks: 1 B. Sklar, Digital Communications, SecondEdition.

2 S. Haykin, Digital Communications3 S. Wilson, Digital Modulation and Coding4 L. W. Couch, Digital and Analog Communication

Systems

Software: Matlab, Python



Grading

Homework (20%): Around five assignments. You can work in groups, butthe final submitted solutions should be your own.

Midterm Exams (2× 20%): Two midterm exams. One sheet of notes isallowed.

Final Exam (20%): One sheet of notes is allowed.

Project (20%): You will propose and carry out a semester-long project.

Typical examples: comparison of vector quantizationtechniques, comparison of modulation types formultipath channels, simulation of CDMA systems,simulation of convolutional encoding and Viterbidecoding, etc.Project will be documented with a written report andwill have an oral presentation component.



Honor Code

Please submit the following pledge along with the submission of eachhomework and project.

I, [place your name and student ID here], declare that

→ the work that I am presenting is my own work→ I have not copied the work (Matlab code, results, etc.) that someone

else has done→ Concepts, understanding and insights I will be describing are my own→ Wherever I have relied on an existing work that is not my own, I have

provided a proper reference citation→ I make this pledge truthfully. I know that violation of this solemn

pledge can carry grave consequences



Prior Background of Relevance

Following prior background will be of relevance, but it is not necessary.

A course on analog and digital communications.

→ A test: do you know what the terms ASK, FSK and QPSK mean?

A course of probability and random process theory.

→ A test: how is the autocorrelation function of a random process relatedto its power spectral density?



Relation to Other Courses

Analog and Digital Communications: Designed to follow on from A&D.Several lectures may have some overlap with A&D. Thiscourse is a generalization of A&D and has a deepertreatment of Digital techniques.

Coding Theory: we will cover block codes, convolutional codes, andpossibly Turbo and LDPC codes. We will look at theperformance analysis and their application in the systemdesign.

Wireless Communications: this course provides a detailed study on thestatistical description of the wireless channel. The coursealso covers multiple wireless (2G/3G/4G) standards. Thisclass will provide an overview of the channel models, and willallow you to understand the PHY layer of these variouswireless standards.



Relation to Other Courses

Information Theory: emphasizes the fundamental limits on digitalcommunications. In CT516, we will study the algorithmsused at the transmitter and the receiver of the digitalcommunication system that attempt to approach theselimits.

Estimation and Detection Theory: provides a detailed mathematicalbackground on the algorithms used at the receiver of digitalcommunication systems. In this class, we will cover a part ofthis material in depth.

Probability and Statistics for Engineers: this class serves as foundationcourse for all of the courses listed above. We will utilizemany statistical concepts in CT516.



A Model of Digital Communication SystemsA Representative Block Diagram



A Model of Digital Communication SystemsAnother Representative Block Diagram

→ http:

//ntrg.cs.tcd.ie/mobile/digital_radio_tutorial.html


http://ntrg.cs.tcd.ie/mobile/digital_radio_tutorial.html

http://ntrg.cs.tcd.ie/mobile/digital_radio_tutorial.html


A Model of Digital Communication SystemsA Representative Block Diagram

→ https://fypfpga.wordpress.com/2011/07/05/background/


https://fypfpga.wordpress.com/2011/07/05/background/


Functionalities ofDigital Communication System

Input to digital communication systems is often analog; and it is oftenfrom voice, video, image. Continuous in time and in amplitude.Sampling makes the signal discrete in time domain. If the signal isbandlimited, there is a theorem (Sampling Theorem) that says thatperfect reconstruction of the original signal is possible just from itssamples.Quantization makes the signal discrete in amplitude, typically resultsin some distortion. The greater the number of quantization levels L(which translates to the bit size of the quantizer log2(L)), the smallerthe distortion. Good designs of the quantizer ensure that thedistortion is small while using a small number of bits. We will studyboth scalar and vector quantizers.Digital data: In case the input to the communication systems isalready in digital format, the above two functions (sampling andquantization) are not needed.




Source coding entails compression of digital data to eliminateredundancies. Can by lossy or lossless compression. Lossycompression is like quantization, introduces distortion but achieves areduction in the bit rate. Lossless compression schemes reduce the bitrate without introducing the distortion.Encryption provides data privacy. This course does not talk aboutencryption in detail.Channel Encoding provides protection against transmission errors byselectively inserting redundant bits. While quantizer and sourceencoder work to squeeze out the redundant information, channelencoder inserts redundant information in a selective manner.Modulator converts digital data to a continuous waveform, usually asinusoidal wave, suitable for transmission over a channel. Informationis transmitted by varying one or more parameters of the waveform.




Parameters that can be modulated:

Amplitude: this is called On Off Keying (OOK) or Amplitude ShiftKeying (ASK).

1⇒ s(t) = A cos(2πfct)

0⇒ s(t) = 0

Frequency: this is called Frequency Shift Keying (FSK).

1⇒ s(t) = A cos(2πfc,1t)

0⇒ s(t) = A cos(2πfc,2t)

Phase: this is called Phase Shift Keying (PSK).

1⇒ s(t) = A cos(2πfct)

0⇒ s(t) = A cos(2πfct + π) = −A cos(2πfct)




Parameters that can be modulated:

Amplitude and Phase: this is called Quadrature AmplitudeModulation (QAM).Modern Communications Theory views channel coding andmodulation as single operation. This results in Trellis codedmodulation.Choice of modulation greatly affects the system performance




Channel. carries the signal - could be a telephone wire, free space,etc. Presents distorted signal to the receiver. Effects includeattenuation, fading, noise, etc. We will often assume that channel issimple and introduces just additive white Gaussian noise or AWGN.Receiver. This has functionalities that are mathematical inverses ofthe transmitter functionalities: demodulation, channel decoding,Decryption, Source Decoding, Digital to Analog Conversion. We willspend a lot of time on the receiver functionalities.



Bandwidth Efficiency ηB

As data rate R increases, the pulse width of transmitted signalreduces and therefore the bandwidth B, which is inverselyproportional to the transmitted pulse width, increases.This cannot be avoided; however some schemes use the availablebandwidth more efficiently than the othersWe will denote the ratio R/W as the bandwidth efficiency ηB .It is obviously better to have ηB as large as possible. However, thereis a cost associated to making ηB large.



Energy Efficiency ηE

Digital communication systems are characterized by the ratio Eb/N0

of required energy per bit Eb to the thermal noise floor N0 that isrequired to attain a certain performance, e.g., bit error probability Pb

that is below certain threshold 10−6.Typically making ηB large requires Eb/N0 to be large; this entails acorresponding increase in transmit energy Eb.

We will define energy efficiency ηE = 1− Eb

Eb,min. Greater the required

energy Eb compared to the minimum required energy Eb,min, thesmaller the energy efficiency.



Fight between ηE and ηB

As it often is the case in the life, it is hard to get best of both theworlds. Typically an increase in ηB translates to a decrease in ηE andvice versa.Binary modulation sends only one bit per use of the channel. M−arymodulation sends multiple bits for each use of the channel andprovides for an increase in the bandwidth efficiency ηB . However,M−ary modulation schemes are typically energy inefficient and havelow ηE .Channel coding increases redundant bits to provide better bit errorrate for a given Eb/N0 or a reduced Eb/N0 for a given BER. Thus,channel coding improves ηE . However, the redundant bits requiregreater bandwidth for transmission, and therefore, channel codingreduces ηB .



Why Digital CommunicationsInstead of Analog

1 Any noise introduces irrecoverable distortion in the analog signal. Incomparison, the a digital receiver needs to distinguish only a finitenumber of transmitted data. Thus, it is possible to completelyremove the effect of noise.

2 Many performance enhancing signal processing techniques, such assource coding, channel coding, encryption, etc., require digitalprocessing.

3 Digital Integrated Circuits (ICs) have become very powerful and areinexpensive to manufacture.

4 Digital Communications allows integration of voice, video and data ona single system.

5 Compared to analog communication systems, digital communicationsprovides a better tradeoff of bandwidth efficiency against energyefficiency.


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CT516 Advanced Digital Communications Lecture 1: …intranet.daiict.ac.in/~yash_vasavada/Courses/Spring2017/CT516/...CT516 Advanced Digital Communications Lecture 1: Introduction