Teaching
Aim of the course:
Communication systems are basic workhorses behind the information age. This course aims to introduce the underlying principles behind the design and analysis of communication systems. The labs will be conducted using Matlab and FM radio experiments will be conducted using Raspberry Pi.
Course overview:
Key components of the communication system designer's toolbox are mathematical modeling and signal processing. Beginning with various basic tools such as Fourier Series/Transform and complex baseband representations of passband signals, the course will cover several important analog communication techniques for Amplitude Modulation, Frequency Modulation, and Phase Modulation. It will also cover superhet receiver and the core concept of phase-locked loop (PLL) and its applications in system design. The later part of the course is focused on digital modulation techniques such as ASK, QAM, PSK, and orthogonal modulation. Nyquist criterion for avoiding intersymbol interference will also be dealt with in the course. Thereafter, the course will cover review of probability, random variables, and random processes with the application in noise modelling. These techniques will then be used in analyzing digital communication performance metric such as bit error probability.
Prerequisite:
Signals and Systems
Reference book:
Upamanyu Madhow, "Introduction to Communication Systems" Cambridge University Press
Course Overview:
This course is a sequel to Principles of Communication Systems (EC-303) course and covers fundamental concepts of modern digital communication systems. The mathematical background necessary to understand communication theory often intimidates the undergraduate students. The purpose of this course is to provide such a lecture style exposition to provide an accessible, yet rigorous, introduction to the subject of digital communication with its practical applications. Beginning with Nyquist sampling theorem, pulse code modulation, and delta modulation, the course will introduce the foundation of information theory, source coding, and source compression algorithms. It will cover several channel coding schemes such as linear block codes, cyclic codes, and convolution code in detail. The later part of the course is focused on optimal receiver design for additive white Gaussian noise (AWGN) channels and their error rate performance considering digital modulation techniques such as Binary Phase Shift Keying (BPSK), Frequency Shift Keying (FSK), Quadrature Amplitude Modulation (QAM), M-ary Phase Shift Keying (MPSK). Spread-spectrum techniques will be dealt with in the course with focus on its anti-jamming property. Finally, the course will treat communication through fading channels, including the characterization of fading channels and the key important parameters: path loss, shadowing, multipath effect, coherence time, coherence bandwidth, and Doppler spread. Link budget analyses for wireline and radio communication systems will also be treated.
Prerequisite:
Principles of communication systems (EC-303)
Reference books:
- Bernard Sklar and Pabitra Kumar Ray, "Digital Communication", Pearson Education
- Simon Haykin, “Digital communication systems”, Wiley Edition
Course Overview:
The course is designed to help students get an in-depth grasp of the fundamentals of wireless technologies, and gain a better understanding of modern 5G wireless communication systems from physical layer perspective, and its extension towards 6G. While the potential benefits of such technologies are promising, there are numerous challenges in the design and implementation of such wireless systems. The course will address the following topics: wireless channel modeling, fading and its countermeasures, diversity techniques, channel coding schemes, orthogonal frequency division multiplexing (OFDM), space-time coding, and MIMO systems. This will also lay the foundation for advanced wireless communication techniques such as Cooperative Communication, Massive MIMO, and Millimeter Wave Communication. Finally, students are expected to prepare a mini project that will focus on an in-depth study and analysis of any cutting-edge wireless technology of their choice.
Prerequisite:
- Digital Communication (EC-306)
- Basics of Probability and Random Variables
Reference books:
- David Tse and P. Viswanath, "Fundamentals of Wireless Communication", Cambridge University Press
- Andrea Goldsmith, "Wireless Communication", Cambridge University Press
- Aditya K. Jagannatham, "Principles of Modern Wireless Communications Systems: Theory and Practice", Mc Graw Hill Education
Course Overview:
This course intends to provide the advanced mathematics background essential for Machine Learning and other advanced courses, and can be viewed as a combination of three main topics: Advanced Linear Algebra, Convex optimization, and Advanced Probability. This course is an essential prerequisite to advanced Machine Learning theory and practice, including domain specific areas such as networking and communication, visual-recognition, automatic speech recognition, and natural language processing.
Prerequisite:
Basics of mathematics and probability theory
Reference books:
- Kevin Murphy, "Machine Learning A Probabilistic Perspective", The MIT Press, 2012
- John E Hopcroft and Ravindran Kannan, "Foundations of Data Science", 2013
Special Topics - Detection and Estimation Theory (CS/NC 297E)
Course Overview:
In many electronic signal processing systems are designed to decide when an event of interest occurs and then extract more information about that event. Detection and Estimation theory can be found at the core of those systems. Some typical applications involving the use of the detection and the estimation theory principles include: Radar Systems, Communication Systems, Image Processing and Pattern Recognition. In detection theory, we first describe different types of detection criteria using hypothesis testing framework. It is followed by brief introduction to non-parametric detection methods. Finally, we describe a detection of deterministic and random signals in white Gaussian noise. In estimation theory, we begin with classical estimation methods which include: minimum variance unbiased estimator (MVUE), best linear unbiased estimator (BLUE), maximum likelihood estimator (MLE) and least squares estimator (LSE). It is followed by Cramer-Rao bound and the Bayesian estimation methods.
Prerequisite:
Basics of probability theory and signal and systems
Reference books:
- Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, S.M. Kay, Prentice Hall 1993
- Fundamentals of Statistical Signal Processing, Volume II: Detection Theory, S.M. Kay, Prentice 1993
- An Introduction to Signal Detection and Estimation, H.V. Poor, Springer, 2nd edition, 1998