Course Curriculum and Syllabus for M.Tech Program in Communications Engineering
(Program Code: M0201)

The department had started this M.Tech. programme from July 2010. The curriculum is revised from July 2023. The latest revised curriculum is given below.

For the curriculum till July 2022 batch please visit the link Communication Engineering (Program Code: M0201)

Semester I:

Code Course Name L-T-P Credits
EE 530 Digital Communications 3-0-0 6
EE 531 Random Processes for Communications 4-0-0 8
EE 532 Matrix Theory for Communications 4-0-0 8
EE 534 Simulation Methods for Communications 3-0-0 6
XX 5/6/7XX Elective 1(1) 3-0-0 6
Total credits 17-0-0 34

Semester II:

Code Course Name L-T-P Credits
EE 533 Wireless Communication 3-0-0 6
EE 535 Data Communication Networks 3-0-0 6
EE 536 Wireless Communications Design Lab 0-0-3 3
XX 5/6/7XX Elective 2(1) 3-0-0 6
EE 5/6/7XX Elective 3(2) 3-0-0 6
EE 5/6/7XX Elective 4(2) 3-0-0 6
Total credits 15-0-3 33

Semester III:

Code Course Name L-T-P Credits
EE 698 Project Phase-I 0-0-24 24
Total credits 0-0-24 24

Semester IV:

Code Course Name L-T-P Credits
EE 699 Project Phase-II 0-0-24 24
Total credits 0-0-24 24

(1) Electives-1 and 2 can be from CSE/MFSDAI/EEE department, but must be selected from the pool of electives provided.

(2) Electives-3 and 4 must be EEE Departmental electives.


Digital Communications (EE 530)
L-T-P-C : 3-0-0-6
Course Contents:

Pulse modulations- amplitude, and position; quantization process-scalar and vector quantization; PCM, DM, and ADM; linear prediction, DPCM, noise considerations in PCM; Signal space concepts- linear and inner product spaces, Fourier space, Hilbert space, Gram-Schmidt orthogonalization, sampling, baseband signaling; Baseband pulse transmissions- common modulated signals and their power spectral densities (e.g., memoryless modulation, multi-dimensional modulation etc.); Review of random variables and random processes; Optimum receivers for Gaussian channels- Coherent and non-coherent receivers and their performance (evaluating BER performance through software tools); ISI, Nyquist criteria for distortion-less transmission; bandlimited channels, equalization; Passband Transmission-Modulations with memory, e.g., CPSK, CFSK; DPSK, NCBFSK, Non-coherent orthogonal modulations, signal detection with unknown phase, discrete multitone; synchronization; Fundamentals of Information Theory, source coding theorem, discrete memoryless channels, channel coding theorem.

  1. S. Haykin, Communication Systems, 4th Ed., Wiley, 2008.
  2. J. G. Proakis and M. Salehi, Digital Communications, 5th Ed., McGrawHill. ,2008.
  3. R. G. Gallager, Principles of Digital Communication, 1st Ed., Cambridge University Press, 2008.
  4. J. R. Barry, E. A. Lee, and D. G. Messerschmitt, Digital Communication, 3rd Ed., Springer, 2012.
  5. J.G. Proakis, Communication Systems Engineering, 2nd Ed., Prentice-Hall India Learning Pvt. Ltd., 2005.
  6. J. Das, S. K. Mullick, and P.K. Chatterjee, Principles of Digital Communication, 2nd Ed., New Age Intl. Publishers, 2012.
  7. U. Madhow, Fundamentals of Digital Communication, 1st Ed., Cambridge University Press, 2008.

Random Processes for Communications (EE 531)
L-T-P-C : 4-0-0-8
Course Contents:

Definitions of Probability, Theorems on probability- Bayes' Theorem, conditionality, independence; Random variables: types and their properties- PDF, CDF, MGF, CF etc.; discrete random variables: Bernoulli, Binomial, Poisson, Geometric, Negative Binomial etc.; continuous random variables: uniform, gaussian, exponential, Rayleigh, Rician, Gamma, chi-squared, Laplacian, multi-variate Gaussian, Nakagami etc.; functions and sequences of random variables; Whitening of Gaussian random vectors- Eigen value decomposition, Cholesky decomposition; complex Gaussian random vectors, circular symmetry; joint, and conditional distributions; moments; Convergence and continuity, laws of large numbers, central limit theorem, inequality theorems; Random processes- stationarity, ergodicity, mean, correlation and covariance, and PSD functions; spectral representation of WSS process; transmission of a random process through a linear filter; white random process; additive white Gaussian noise; Wiener Process and Brownian Motion; Poisson Process, point process; Markov Chains; Detection, decision and hypothesis testing: Binary MAP detection, min-max and Neyman-Pearson rule, sufficient statistics, finitely many hypothesis testing, examples- radar and cognitive radio; LMSE, MVUE and MAP estimation, vector space of random variables and orthogonality, Cramer Rao bound and Fisher information matrix; Applications- block transmission in memoryless channels, fading and shadowing in wireless channels, PSD in bandwidth requirements in wireless channels, queueing in communication networks.

  1. R. G. Gallager, Stochastic Processes: Theory for Applications, 1st Ed., Cambridge University Press, 2013.
  2. A.-L. Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3rd Ed., Pearson, 2011.
  3. S. Ross, A First Course in Probability, 10th Ed., Pearson, 2021.
  4. A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Processes, 4th Ed., McGraw-Hill, 2002.
  5. J. Das, S. K. Mullick, and P.K. Chatterjee, Principles of Digital Communication, 2nd Ed., New Age Intl. Publishers, 2012.

Matrix Theory for Communications (EE 532)
L-T-P-C : 4-0-0-8
Course Contents:

Vector spaces, linear independence, basis and dimension, linear maps and matrices, fundamental subspaces, rank and its properties, rank-nullity theorem, eigenvalues and eigenvectors, invariant subspaces, inner products; Norms- vector norms, matrix norms, dual norms, Gram-Schmidt process, orthonormal basis; determinant and their properties, spectral theorem, unitary and orthogonal transformations, operators on real and complex vector spaces; singular value decompositions (SVD), properties of SVD, system of linear equations, QR decomposition, LU decomposition, Cholesky decomposition, whitening methods, least squares, constrained least squares, positive definite and semidefinite matrices, positive and nonnegative matrices, derivative of multivariable function, Taylor's series, gradient, Hessian, Applications-signal spaces, MIMO beamforming, OFDM, Equalization.

  1. S. Boyd, and L. Vandenberghe, Introduction to Applied Linear Algebra- Vectors, Matrices, and Least Squares, 1st Ed., Cambridge University Press, 2018.
  2. G. H. Golub and C. F. Van Loan, Matrix Computations, 4th Ed., John Hopkins University press, 2013.
  3. R. A. Horn and C. R. Johnson, Matrix Analysis, 2nd Ed., Cambridge University Press, 2012.
  4. G. Strang, Linear Algebra and Its Applications, 4th Ed., Cengage India Pvt. Ltd., 2005.
  5. S. Axler, Linear Algebra Done Right, 3rd Ed., Springer, 2015.
  6. L. N. Trefethen and D. Bau, III, Numerical Linear Algebra, Society for Industrial and Applied Mathematics (SIAM), 1997.
  7. S. R. Ghorpade and B. V. Limaye, A Course in Calculus and Real Analysis, 2nd Ed., Springer, 2018.
  8. K. Hoffman, and R. Kunze, Linear Algebra, 2nd Ed., Prentice-Hall Pvt. Ltd., 1971.
  9. D. Tse, and P. Vishwanath, Fundamentals of Wireless Communications, 1st Ed., Cambridge University Press, 2005.

Simulation Methods for Communications (EE 534)
L-T-P-C : 3-0-0-6
Course Contents:

Introduction to the programming platform (MATLAB/ Python)- Application on deterministic and captured signals; Random variables- their significance, generation and Monte-Carlo simulations; Baseband signal models and channels- concept, AWGN channel, simulation of performance of digital modulations in AWGN channel; Markov chains, hidden Markov models, modulation with memory, ISI and bandlimited channels, Detection/ Decoding based on Viterbi Algorithm; equalization, MMSE equalization, adaptive equalization, Stochastic Gradient descent; Object oriented programming-class, objects, methods, inheritance etc.; Heuristic Techniques- genetic Algorithms, Tabu search, Particle Swarm algorithm etc

  1. J. G. Proakis, M. Salehi, and G. Bauch, Modern Communication Systems Using MATLAB, 3rd Ed., Cengage Learning, 2012.
  2. S. M. Ross, Introduction to Probability Models, 12th Ed., Academic Press, 2019.
  3. R. Pratap, Getting Started with MATLAB: A Quick Introduction for Scientists and Engineers, 7th Ed., Oxford University Press, USA, 2017.
  4. W. H. Tranter et al., Principles of Communication Systems Simulation with Wireless Applications, Prentice-Hall Pvt. Ltd., 2003.
  5. J. W. Leis, Communication Systems Principles using MATLAB, 1st Ed., Wiley, 2018.
  6. Paul Deitel, Harvey Deitel, Python for Programmers: With Introductory AI Case Studies, Deitel developer series, Pearson Education, 2019.

Wireless Communication (EE 533)
L-T-P-C : 3-0-0-6
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; water-filling 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.

  1. A. 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.

Data Communication Networks (EE 535)
L-T-P-C : 3-0-0-6
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.

  1. D. Bertsekas and R. Gallager, Data Networks, 2nd Ed., Prentice Hall, 1992.
  2. A. Leon-Garcia and I. Widjaja, Communication Networks, 2nd Ed., McGraw Hill, 2009.
  3. A. Kumar, D. Manjunath and J. Kuri, Communication Networking: An Analytical Approach, Elsevier, 2004.
  4. L. Peterson and B. Davies, Computer Networks: A Systems Approach, 4th Ed., Elsevier, 2007.
  5. F. Gabeli, Analysis of Computer and Communication Networks, 1st Ed., Springer, 2008.

Wireless Communications Design Laboratory (EE 536)
L-T-P-C : 0-0-3-3
Course Contents:

Simulation/ laboratory experiments are based on the following topics: Error Performance Simulation for SISO systems over independent Rayleigh and Rician fading channels for different baseband modulation schemes, Simulation of space diversity techniques- receiver combining (SC, EGC, MRC etc.); beamforming techniques (EGT, MRT etc.); performance of MIMO correlated channels, simulation of channel capacity of MIMO channels, power control- uniform power distribution, water-filling; space-time block codes and their performance simulation; Simulation of equalization techniques- amplify and forward, decode and forward etc.; rate analysis of differential space-time modulation; OFDM signal transmission/reception (text message/ image) performance simulation; Hardware Experiments.

  1. E. G. Larsson and P. Stoica, Space-Time Block Coding for Wireless Communications, 1st Ed., Cambridge University Press, 2003.
  2. D. Tse and P. Viswanath, Fundamentals of wireless communication, 1st Ed., Cambridge University Press, 2005.