Master of Technology (Communication Engineering)

(To be applicable from July 2013-batch onwards)

 

Semester I

Code

Course Name

L–T-P

Credit

EE 501

Linear Algebra and Optimization

3-0-0

6

EE 504

Probability and Stochastic Processes

3-0-0

6

EE 531

Communication System Theory

3-0-0

6

EE 532

Information and Coding Theory

3-0-0

6

EE 5/6xx

Elective I

3-0-0

6

EE 538

Communication System Simulation Lab

0-0-3

3

 

 

15-0-3

33

 

Semester II

Code

Course Name

L-T-P

Credit

EE 533

Wireless Communication

3-0-0

6

EE 534

Data Communication Networks

3-0-0

6

EE 636

Detection and Estimation Theory

3-0-0

6

EE 5/6xx

Elective II

3-0-0

6

EE 5/6xx

Elective III

3-0-0

6

EE 539

Communication System Design Lab

0-0-3

3

 

 

15-0-3

33

Semester III

Code

Course Name

L-T-P

Credit

EE 698

Project Phase I

0-0-24

24

Semester IV

Code

Course Name

L-T-P

Credit

EE 699

Project Phase II

0-0-24

24

 

Credits: Course – 66, Project – 48, Total – 114

 

 

Syllabi for M.Tech (Communication Engineering)

 

 

EE 501                             Linear Algebra and Optimization              (3-0-0-6)

 

Preamble:

 

The objective of this course is to provide a firm foundation in linear algebra and optimization appropriate at the graduate level. The focus is both on theoretical developments of ideas as well as algorithms.

 

Course Contents:

 

Linear Algebra - vector spaces, linear independence, bases and dimension, linear maps and matrices, eigenvalues, invariant subspaces, inner products, norms, orthonormal bases, spectral theorem, isometries, polar and singular value decomposition, operators on real and complex vector spaces, characteristic polynomial, minimal polynomial; optimization - sequences and limits, derivative matrix, level sets and gradients, Taylor series; unconstrained optimization - necessary and sufficient conditions for optima, convex sets, convex functions, optima of convex functions, steepest descent,  Newton and quasi Newton methods, conjugate direction methods; constrained optimization - linear and non-linear constraints, equality and inequality constraints, optimality conditions, constrained convex optimization, projected gradient methods, penalty methods.

 

Texts / References:

  1. S. Axler, Linear Algebra Done Right, 2nd Edn., Springer, 1997.
  2. E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 2nd Edn., Wiley India Pvt. Ltd., 2010.
  3. G. Strang, Linear Algebra and Its Applications, Nelson Engineering, 2007.
  4. D. C. Lay, Linear Algebra and Its Applications, 3rd Edition, Pearson, 2002.
  5. D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming, 3rd Edn., Springer, 2010.

 

 

EE 504                   Probability and Stochastic Processes      (3-0-0-6)

 

Preamble:

 

The objective of this course is to provide a solid foundation in probability and stochastic processes appropriate at the graduate level. The examples will emphasize applications in engineering, especially in signal processing and communication engineering.

 

 

Course Contents:

 

Axiomatic definitions of probability; conditional probability, independence and Bayes theorem, continuity property of probabilities, Borel-Cantelli Lemma; random variable: probability  distribution, density  and mass functions, functions of a random variable; expectation, characteristic and moment-generating functions; Chebyshev, Markov and Chernoff bounds; jointly distributed random variables: joint  distribution and density functions, joint moments, conditional distributions and expectations, functions of random variables; random vector- mean vector and covariance matrix, Gaussian random vectors; sequence of random variables: almost sure and mean-square convergences, convergences in probability and in distribution, laws of large numbers, central limit theorem; random process: probabilistic structure of a random process; mean, autocorrelation and autocovariance functions; stationarity - strict-sense stationary and wide-sense stationary (WSS) processes: time averages and ergodicity; spectral representation of a real WSS process-power spectral density, cross-power spectral density, linear time-invariant systems with WSS process as an input- time and frequency domain analyses; examples of random processes: white noise, Gaussian, Poisson and Markov processes.

 

Texts / References:

 

  1. H. Stark and J. W. Woods, Probability and Random Processes with Applications to Signal Processing, Prentice Hall, 2002.
  2. A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Processes, 4th Edn., McGraw-Hill, 2002.
  3. B. Hajek, An Exploration of Random Processes for Engineers, ECE534 Course Notes, 2011. (http://www.ifp.illinois.edu/~hajek/Papers/randomprocesses.html )

 

 

 

 

EE 531                           Communication System Theory                     (3-0-0-6)

 

Preamble:

 

This course is intended for graduate students. It envisages that student would be well equipped for research or cutting edge development in communication systems.

 

Course Contents:

 

Review of digital modulation schemes and receivers in additive white Gaussian noise channels: Probability of Error Calculation, CPM, MSK, CPFSK; intersymbol interference; Adaptive receivers and channel equalization: MMSE, ZFE, FSE; Carrier and clock synchronization; Effects of phase and timing jitter; Coded modulation schemes: TCM; Digital transmission over fading channels.

 

Texts / References:

 

  1. U. Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008.
  2. J. G. Proakis, Digital Communications, 4th Edn., McGraw Hill, 2000.
  3. S. Benedetto and E. Biglieri, Principles of Digital Transmission with Wireless Applications, Kluwer Academic, 1999.
  4. R. G. Gallager, Principles of Digital Communication, Cambridge University Press, 2008.

 

 

EE 532                               Information and Coding Theory         (3-0-0-6)

 

Preamble:

 

This course is mainly divided into information theory as well as coding theory. Topics in information theory address the two fundamental questions in communication theory: ultimate date compression and ultimate transmission rate.  The topics in the coding theory cover the   theoretical framework upon which the error-control codes are built.

 

Course Contents:

 

Information Theory: Entropy, relative entropy and mutual information for discrete ensembles; Asymptotic equipartition property; Markov chains; Shannon’s noiseless coding theorem; Encoding of discrete sources. Discrete memoryless channels; Shannon’s noisy coding theorem and converse for discrete channels; Differential entropy; Calculation of channel capacity for Gaussian channels. Coding Theory: Linear Codes,  distance bounds, generator and parity check matrices, error-syndrome table;  Cyclic codes, generator and parity check polynomials; BCH codes and Reed-Solomon Codes; An  overview of convolutional codes; Maximum likelihood decoding; MAP decoder; Introduction to turbo codes and LDPC codes.

 

Texts / References:

 

  1. T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley, New York, 1991.
  2. R. H. Morelos-Zaragoza, The Art of Error Correcting Coding, John Wiley, New York, 2006.
  3. R. W. Yeung, A First Course in Information Theory, Kluwer Academic, 2002.
  4. R. G. Gallager, Information Theory and Reliable Communication, John Wiley, 1968.
  5. R. B. Ash, Information Theory, Dover Publications, 1990.
  6. D. J. Mackay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.
  7. W. Ryan and S. Lin, Channel Codes: Classical and Modern, Cambridge University Press, 2009.

 

 

EE 538                      Communication System Simulation Lab      (0-0-3-3)

 

Preamble:

 

The objective of this lab is to introduce students to the fundamental ideas in simulating point-to-point communication systems in MATLAB/ SCILAB/ OCTAVE. The lab will cover topics related to the foundational course EE 531 which will be taken concurrently by students.

 

Course Contents:

 

Simulation experiments are based on the following topics: Different modulation schemes such as CPM, MSK, CPFSK, intersymbol interference; Adaptive receivers and channel equalization: MMSE, ZFE, FSE; Carrier and clock synchronization.

 

Texts / References:

 

  1. U. Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008.
  2. J. G. Proakis, Digital Communications, 4th edition, McGraw Hill, 2000.
  3. S. Benedetto and E. Biglieri, Principles of Digital Transmission with Wireless Applications, Kluwer Academic, 1999.

 

 

EE 533                                    Wireless Communication                    (3-0-0-6)

 

Preamble:

 

This course envisages providing an introduction to the fundamental principles involved in wireless communication systems and mobile radio communication. Topics also cover the fundamental concepts of mobile cellular communications.

 

Course Contents:

 

Overview of current wireless systems and standards; wireless channel models- path loss and shadowing models; statistical fading models; narrowband and wideband fading models; MIMO channels. Diversity in wireless communications - Non-coherent and coherent reception; error probability for uncoded transmission; realization of diversity: time diversity; frequency diversity: DSSS and OFDM; receiver diversity: SC, EGC and MRC; transmit diversity: space-time codes; Information theory for wireless communications-  Capacity of fading channels: ergodic capacity and outage capacity; high versus low SNR regime; waterfilling algorithm; capacity of MIMO channels; Multiuser wireless communications: multiple access: FDMA, TDMA, CDMA and SDMA schemes; interference management: power control; multiuser diversity, multiuser MIMO systems.

 

Texts / References:

 

  1. J. Goldsmith, Wireless Communications, Cambridge University Press, 2005.
  2. D. Tse and P. Viswanath, Fundamentals of Wireless Communications, Cambridge University Press, 2005.
  3. A. Molisch, Wireless Communications, John Wiley & Sons, 2005.
  4. S. Haykin and M. Moher, Modern Wireless Communications, Pearson Education, 2005.
  5. T. S. Rappaport, Wireless Communications, Prentice Hall, 1996.
  6. G. L. Stuber, Principles of Mobile Communications, Kluwer, 1996.
  7. T. Cover and J. Thomas, Elements of Information Theory, John Wiley & Sons, 1991.

 

 

EE 534                             Data Communication Networks                 (3-0-0-6)

 

Preamble:

 

This course aims to provide an analytical perspective on the design and analysis of the traditional and emerging date communication networks.

Course Contents:

 

Introduction to Computer Networks -Store-and-forward and circuit switching, layered network architecture, the OSI network model, Internet architecture;   Data Link Layer and Peer to Peer protocols -  Encoding (NRZ, NRZI, Manchester, 4B/5B), HDLC, Error detection, ARQ – SW, GBN, SR;  Delay models in Data Networks-Traffic multiplexing on a communication link, Little’s theorem, The M/M/1 Queueing System, M/G/I Queues with Vacations, Priority Queues; MAC protocols and LAN-  Polling and Reservations, ALOHA, Slotted ALOHA, CSMA-CD, Ethernet and IEEE 802.3, Wireless LAN and IEEE 802.11.Routing in packet networks-IP, shortest-path routing, intra-domain routing (OSPF, RIP), inter-domain routing (BGP), routing for mobile hosts;  End-to-End Protocols- UDP and TCP;  Congestion Control and Resource Allocation -Resource Allocation, TCP Congestion Control, Congestion-avoidance mechanisms, QoS; Internetworking using TCP/IP - Network programming using socket API, client/server communication.

 

 

Texts / References:

 

  1. D. Bertsekas and R. Gallager, Data Networks, 2nd Edn., Prentice Hall, 1992.
  2. L. Peterson and B. Davies, Computer Networks: A Systems Approach, 4th Edition, Elsevier (Morgan Kaufmann), 2007.
  3. A. Leon-Garcia and I. Widjaja, Communication Networks, 2nd Edn., McGraw Hill, 2009.
  4. A. Kumar, D. Manjunath and J. Kuri, Communication Networking: An Analytical Approach, Elsevier (Morgan Kaufmann), 2004.

 

 

                                                                

EE 539           Communication System Design Lab          (0-0-3-3)

 

Preamble:

 

The objective of this lab is to introduce students to system design and hardware implementation issues in building a point-to-point communication system. The lab will cover topics related to the foundational course EE 531 and also complement the course EE 538 which students have completed.

 

Course Contents:

 

Laboratory experiments are based on the following topics:  Design and system level implementation of different modulation techniques (CPM, MSK, CPFSK); adaptive receivers; channel equalizers (MMSE, ZFE, FSE).

 

Texts / References:

 

  1. U. Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008.
  2. J. G. Proakis, Digital Communications, 4th Edition, McGraw Hill, 2000.
  3. S. Benedetto and E. Biglieri, Principles of Digital Transmission with Wireless Applications, Kluwer Academic, 1999.

 

                                                        

LIST OF ELECTIVES FOR MTECH (COMMUNICATION ENGINEERING)

 

Electives

Code

Course Name

L–T-P

Credit

EE 562

Fundamentals of VLSI CAD

3-0-0

6

EE 621

Advanced Topics in Random Processes

3-0-0

6

EE 623

Advanced Topics in Signal Processing

3-0-0

6

EE 624

Image Processing

3-0-0

6

EE 625

Computer Vision

3-0-0

6

EE 626

Biomedical Signal Processing

3-0-0

6

EE 627

Speech Signal Processing and Coding

3-0-0

6

EE 628

Speech Technology

3-0-0

6

EE 632

Mobile Communications

3-0-0

6

EE 633

Queuing Systems

3-0-0

6

 

EE 635

Advanced Topics in Communication Systems

3-0-0

6

 

EE 637

Error Control Codes

3-0-0

6

 

EE 638

Multimedia Security: Methodologies for Content Access Control, Tracking and Authentication

3-0-0

6

 

EE 639

Sparse Representations & Compressive Sensing: Theory & Applications

3-0-0

6

 

EE 651

Multivariable Control Theory

3-0-0

6

 

EE 653

Nonlinear  Systems and  Control

3-0-0

6

 

EE 657

Pattern Recognition and Machine Learning

3-0-0

6

 

EE 659

Modeling and Simulation of Dynamic Systems

3-0-0

6

 

EE 672

Intelligent Sensor and Actuator

3-0-0

6

 

EE 673

Synchrophasor  Technology

3-0-0

6

 

EE 674

High Voltage Transmission

3-0-0

6

 

EE 680

Electric and Hybrid vehicles

3-0-0

6

 

EE 682

Advanced Electric Drives

3-0-0

6

 

EE 684

Numerical Methods in Electromagnetics

3-0-0

6

 

EE 685

Generalized Theory of Electrical Machines

3-0-0

6