MTech in Electronics and Electrical Engineering

(Specialization: Communication Engineering)

	Semester I			Semester II
Course No	Course Name	L-T-P-C	Course No	Course Name	L-T-P-C
EC 520	Linear Algebra and Random Processes	4-0-0-8	EC 636	Detection and Estimation Theory	3-0-0-6
EC 521	Signal Processing	3-0-0-6	EC 523	Digital Signal Processors	2-0-3-7
EC 530	Advanced Digital Communication	3-0-0-6	EC 6xx	Dept. Elective - I	3-0-0/2-6/8
EC 537	Information Theory	3-0-0-6	EC 6xx	Dept. Elective - II	3-0-0/2-6/8
			EC 697	Project Phase - I	0-0-6-6
	Total	13-0-0-26		Total	11-0-9/13-31/35
	Semester III			Semester IV
EC 698	Project Phase – II	0-0-24-24	EC 699	Project Phase - III	0-0-24-24
	Total	0-0-24-24		Total	0-0-24-24

EC 520 LINEAR ALGEBRA AND RANDOM PROCESSES (4-0-0-8) 
 

Linear Algebra: Basic analysis and topology. Vector spaces, linear operators and matrices. Decomposition theorems and eigen-analysis. Quadratic forms. Perron-Frobenius theorems. Probability: Spaces and random variables. Distributions. Transformations and moment analysis. Stochastic processes and covariance analysis. Estimation theory.

Texts/References:

1. K. Hoffman and R. Kunze, Introduction to Linear Algebra, 2^nd Ed, Prentice-Hall, 1996.

2. R. Horn and C. Johnson, Matrix Analysis; Cambridge, CUP, 1991

3. A. Papoulis, Probability, Random Variables and Stochastic Processes, 3^rd Ed, McGraw-Hill, 1991.

4. H. Stark and J. W. Woods, Probability, Random Variables and Estimation Theory for Engineers, Prentice Hall, 1994.

  
EC 521  Signal Processing  (3-0-0-6) 

Continuous-time and discrete-time signals and systems; Spectral analysis: CTFT and DTFT, DFT, FFT and STFT; Sampling, Quantization, Decimation and Interpolation; Z-transform: definition and ROC; Digital filters: FIR and IIR filters, Digital-filter realisations and design, Finite wordlength effects; Adaptive filtering: steepest-descent algorithm, LMS, variants of LMS, LS, RLS, blind algorithms.

Texts/References:

1. S. Haykin, Adaptive Filter Theory, PHI, 2001.

2. A.V. Oppenheim and R.W. Schafer, Discrete- Time Signal Processing, PHI, 2000.
3. S. K. Mitra, Digital Signal Processing, TMH, 3/e, 2006.
4. S. J. Orfanidis, Introduction to Digital Signal Processing, Prentice-Hall, 1996

 
EC 530 Advanced Digital Communication ( 3-0-0-6) 

Review of digital modulation schemes and receivers in additive white Gaussian noise channels, CPM, MSK, CPFSK; Intersymbol interference; Adaptive receivers and channel equalization: MMSE, ZFE, FSE; Carrier and clock synchronisation; Effects of phase and timing jitter; Block codes, Convolutional codes and their performance evaluation; Coded modulation schemes: TCM; Turbocodes; Digital transmission over fading channels.

Texts/References:

1. S Benedetto and E Biglieri, Principles of Digital Transmission with Wireless Applications,

Kluwer Academic, 1999.

2. R G Gallager, Principles of Digital Communication, Cambridge University Press, 2008

3. J G Proakis, Digital Communications, 4th edition, McGraw Hill, 2000

4. U Madhow, Fundamentals of Digital Communication, Cambridge University Press, 2008

 
EC 537 Information Theory (3-0-0-6) 

Information Thoery: Entropy and mutual information for discrete ensembles; Asymptotic equipartition property; Markov chains; Entropy Rates of a Stochastic Process; Shannon's noiseless coding theorem; Encoding of discrete sources;Universal Source Coding;Discrete memoryless channels; Shannon's noisy coding theorem and converse for discrete channels; Calculation of channel capacity and bounds for discrete channels; Differential entropy; Calculation of channel capacity for Gaussian chanels; Rate distortion function; Large Deviation Theory; Chernoff Information; Fisher Information and the CramerRao inequality; Network Information Theory Multiple-access Channel, Broadcast Channel, Relay Channel;Information Theory applications in Portfolio Theory;

Texts/References:

1. T. M. Cover and J. A. Thomas, .Elements of Information Theory, JohnWiley, New York, 1991

2. RW Yeung, Information Theory And Network Coding, Springer, 2008

3. RG Gallagar, Information Theory and Reliable Communication, John Wiley & Sons, 1976.

4. R.B. Ash, Information Theory, Prentice Hall, 1970

EC-636	DETECTION AND ESTIMATION THEORY	3-0-0-6
(Pre-requisite: EC 221 or EC 520 or equivalent) Review of random process, problem formulation and objective of signal detection and signal parameter estimation; Hypothesis testing: Neyman-Pearson, minimax, and Bayesian detection criteria; Randomized decision; Compound hypothesis testing; Locally and universally most powerful tests, generalized likelihood-ratio test; Chernoff bound, asymptotic relative efficiency; Sequential detection; Nonparametric detection, sign test, rank test. Parameter estimation: sufficient statistics, minimum statistics, complete statistics; Minimum variance unbiased estimation, Fisher information matrix, Cramer-Rao bound, Bhattacharya bound; Linear models; Best linear unbiased estimation; Maximum likelihood estimation, invariance principle; Estimation efficiency; Least squares, weighted least squares; Bayesian estimation: philosophy, nuisance parameters, risk functions, minimum mean square error estimation, maximum a posteriori estimation. Texts: 1. H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd edition, Springer, 1994. 2. S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, Prentice Hall PTR, 1998. 3. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall PTR, 1993. References: 1. H. L. Van Trees, Detection, Estimation and Modulation Theory, Part I, John Wiley, 1968. 2. D. L. Melsa and J. L. Cohn, Detection and Estimation Theory, McGraw Hill, 1978. 3. L. L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis, Addison-Wesley, 1990. 4. V. K. Rohatgi and A. K. M. E. Saleh, An Introduction to Probability and Statistics, 2nd edition, Wiley, 2000.

 
EC 523  Digital Signal Processors  (2-0-3-7)  

Introduction: Computational characteristics of DSP algorithms and applications; Techniques for enhancing computational throughput: Harvard architecture, parallelism, pipelining, dedicated multiplier, split ALU and barrel shifter; TMS320C64xx architecture: CPU data paths and control, general purpose register files, register file cross paths, memory load and store paths, data address paths, parallel operations, resource constraints; Assembly language: Programmers model, functional units, Fetch and execute packets, pipelining, linear and circular addressing, assembler directives, addressing modes, instructions; Memory: Program memory, data memory, memory configuration. External memory interface (EMIF), fixed point and floating point formats; Interrupts: Interrupt sources, interrupt control registers and interrupt acknowledgment; Peripherals: Timer, multi channel buffered serial port, DMA, general purpose IO; DSP Real Time system operating systems; Applications: a few case studies of application of DSPs in communication and multimedia.

 
Laboratory
 

Experiments: Familiarization to Code Composer Studio; development cycle on TMS320C64xx kit; finite impluse response filter; infinite impulse response filter; adaptive filter and experiments on communication such as generation of a n-tuple PN sequence, generation of a white noise sequence using the PN sequence and CLT, restoration of a sinusiodal signal embedded in white noise by Wiener

Filtering; speech and multi-media applications.

Texts/References:

1.Rulph Chassaing and Donald Reay, Digital signal processing and applications with Tms320C6713 and TMS320C6416, Wiley, 2008.

2.TMS320C64x Technical Overview, Texas Instruments, Dallas, TX, 2001.

3.TMS320C6000 Peripherals Reference Guide, Texas Instruments, Dallas, TX, 2001.

4.TMS320C6000 CPU and Instruction Set Reference Guide, Texas Instruments, Dallas, TX, 2000.

5.TMS320C6000 Peripherals Reference Guide, Texas Instruments, Dallas, TX, 2001.

6.IEEE Signal Processing Magazine : Oct 88, Jan 89, July 97, Jan 98, March 98 and March 2000.

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