Overview

This specialization aims to encourage students and working professionals to develop an in-depth knowledge on theoretical as well as practical aspects of modern communications and signal processing (CSP) signals and systems. Students are provided with the specialist knowledge and skill-set needed in this industry. They are prepared to undertake research and development work in academic or industrial environment in this field.

Program Structure

(Students are also advised to go through the M.Tech. (ECE) specific regulation and PG Regulations to know other requirement of the M.Tech. degree.)

As per M.Tech. regulations, a student is required to complete 12 credits (all the courses) from core bucket and 8 credits to get specialization in Communications and Signal Processing. Overall 48 credits must be completed to get M.Tech. degree with CSP specialization.

Core Courses:

Three courses constitute the core of this stream. These are:

Probability Theory and Random Processes:

The course will provide students with an in depth introduction to stochastic processes with applications in electrical engineering. A review of axioms of probability, single and multivariate distributions, and functions of random variables will be followed by study of fundamental theorems like Markov’s inequality, Chebyshev’s inequality, Chernoff’s Bound, weak and strong law of large numbers (convergence in probability and almost sure convergence), mean-squared convergence, convergence in distribution, the central limit theorem, random waveforms, stationarity, ergodicity, linear systems with stochastic inputs, autocorrelation and the power spectrum. Along the course we will also look at examples like the Weiner process, Poisson process and Markov Chains.

Principles of Digital Communication System:

The course is meant for graduate level students and covers in detail receiver designs for digital communications using statistical communication theory principles and Signal Space concepts for optimum receiver design. It will also involve analyzing advance digital communication system analytically and using MATLAB simulation. The course also covers digital wireless multiple access systems techniques like CDMA & OFDM. MIMO systems will also be discussed to achieve receive & transmit diversity and multiplexing gains.

Statistical Signal Processing:

This post graduate course is designed to cover techniques for statistical signal processing, detection and parameter estimation. It will briefly review the preliminaries on linear algebra and statistics. The rest of the course is broadly divided into three parts. The first part will deal with the design, implementation and performance evaluation of detectors; this would cover composite and M-ary hypothesis testing. The second part of the course deals with estimation techniques like Maximum Likelihood, MAP and MMSE estimation. The third part introduces adaptive filtering approaches; this will cover stochastic and data-driven approach with emphasis on least squares based techniques. Homework will be a mix of theory and programming assignments.