eMasters in Communication Systems

Core Modules

• Digital Communication Systems
  The module covers the fundamentals of digital communication systems with an emphasis on physical layer aspects of communications. It will give tools to analyze and characterize the performance of digital communication systems.
• Wireless Communication
  The module covers the fundamental concepts and insights behind the development of modern wireless communication technologies. As part of the module, precise analytical models will be presented for various wireless systems followed by detailed performance analysis of several key 4G/ 5G wireless technologies such as OFDM, MIMO, Multi-user MIMO.
• Probability and Random Processes
  The module focuses on strengthening the foundation of probability, keeping its application into signal processing and communications in mind. It discusses the concepts of probability space, random variables, their CDF and PDF/PMF, functions of random variables, random variable transformations, Law of Large Number and random processes.
• Applied Linear Algebra for Wireless Communication
  The module provides the Linear Algebra background necessary for various communication, signal processing, and machine learning modules.

Elective Modules

• Advanced Probability
  The module develops on the foundations laid by the 'Probability and Random Processes' module and provides advanced tools in probability theory. It takes a measure theoretic approach to probability theory. It also discusses concentration inequalities, and estimation of RVs. Various random processes including Markov chains are discussed along with their applications into linear systems. After completing the module, the program participants should be able to strengthen their base in probability theory and stochastic processes and apply these tools in their own research.
• Machine Learning basics
  The module aims at introducing the students to machine learning (ML) techniques used for various signal processing applications. The module will be focused on applications in digital signal processing, and the theory will be tailored towards that end.
• Deep Learning for Communication
  Machine learning (ML) is now routinely being applied to design state-of-the-art 5G wireless systems. This module aims to bridge the gap between the theory of machine learning and its applications to 5G systems.
• Detection and Estimation Theory
  The goal of this module is to introduce the fundamentals of detection and estimation. It will also cover several applications from signal processing and communications.
• Speech and Audio Coding for Communication
  The module aims at introducing the program participants to topics in automatic speech and audio processing.
• Basics of Convex Optimization
  Convex optimization has recently been applied to a wide variety of problems in EE, especially in signal processing, communications, and networks. The module aims to train the program participants in applying and analyzing convex optimization problems in signal processing and wireless communications. At the end of this module, the students are expected to: 1. Be able to recognize convex optimization problems arising in these areas. 2. Be able to recognize ‘hidden’ convexity in many seemingly non-convex problems; formulate them as convex problems.
• Optimization in SPCOM
  The module aims to train the program participants in applying and analyzing convex optimization problems in signal processing and wireless communications. At the end of this module, the students are expected to: 1. Know about convex optimization applications in signal processing, wireless communications, and networking research. 2. Be able to develop low-complexity, approximate solutions for complex non-convex problems.
• Data Communication Networks
  The module gives a first introduction to networked systems and the Internet. The goal is to provide some insight into the reasons behind the architecture of modern- day networks and the principles of designing reliable networked systems.
• Simulation Techniques for Modern Communication Systems
  Numerical evaluation is a quick way to evaluate complex systems where analysis is complicated. Most of the telecommunication industry relies on simulations to test their methodology. Academicians use simulation to validate their analysis and extend their results for complex systems. This module will focus on simulation methodologies in the field of communication with a significant focus on their actual implementations. The module is a balanced version of theory and implementation. It will discuss fundamental tools in numerical techniques and their application to communications.
• Foundations of Information Theory and Data Compression
  The module will answer two fundamental questions in communications that information theory answers, namely, what is the ultimate data rate at which we can reliably communicate over a channel, and what is the ultimate data compression that we can achieve. In addition to theory, we will also cover practical compression algorithms.
• Error Control Codes: Theory and Practice
  The module focuses on the design of error-correcting codes for applications in communication systems. In particular, we will study the theory of design of linear block codes and convolutional codes with examples from the current state of the error-correcting codes such as turbo codes, LDPC codes, and polar codes.
• MIMO Wireless Communication
  The module will cover state-of-the-art multiple-input multiple-output (MIMO) wireless transmitter and receiver designs used in the 5G cellular systems.
• Simulation-Based Design of 5G-NR Wireless Standard
  Some professionals usually have a solid theoretical background in wireless communications systems but negligible exposure in designing practical wireless systems. This module aims to bridge the gap between theory and practice of 5G wireless communication systems.
• Analysis of Wireless Systems
  The module will cover tools from stochastic geometry to model and analyze modern wireless systems being used in 4G and 5G systems. After completing the module, the program participants should be able to apply mathematical tools from stochastic geometry in their own research to analyze modern wireless systems.
• Advanced Modulation and Multiple Access for Next Generation Wireless Systems
  The module covers modern modulation and multiple access schemes that are potential candidates for futuristic communication systems.
• RF systems for Communication
  RF front end is an essential part of the wireless communication system. A system designer needs to understand the performance of various components such as transmission lines, matching, filters, amplifier, oscillator, antennas, etc., so that they can be integrated efficiently. This course introduces various parameters, such as, scattering matrix, 1 dB compression, third-order intercept point, noise figure, etc., which are used to specify the performance of RF components. The course then focuses on block-level description of RF system, system calculations, and tradeoff in block-level specifications to achieve the overall performance. Finally, the course ends with an exposure to some of the RF measurements using vector network analyzer and spectrum analyzer.