Programme Name: Next Generation Wireless Technologies (NGWT)
*Initially Communication System (COMM)
Module
ID
Module Title
Credit
Description
Content
EE900
Applied Linear
Algebra for
Wireless
Communications
5
Linear Algebra for communication, signal
processing and ML modules are required to
design, analyze and optimize state-of-the-art
wireless systems. The objective of this module is
to teach linear algebra concepts which are
applicable to such wireless communication
systems.
● Introduction to Vectors
○ Vectors and Linear Combinations
○ Length and Dot Products
○ Matrices
● Solving Linear Equations
○ Vectors and Linear Equations
○ Idea of Elimination
○ Elimination Using Matrices
○ Rules for Matrix Operations
○ Inverse Matrices
○ Transposes and Permutations
● Vector Spaces and Subspaces
○ Spaces of Vectors
○ Nullspace of a matrix
○ Complete solution of a system of
equation
○ Independence, Basis and Dimensions
○ Dimensions of the Four Subspaces
● Orthogonality
○ Orthogonality of the Four Subspaces
○ Projections
○ Least Squares Approximations
● Determinants
○ Properties of Determinants
○ Permutations and Cofactors
● Eigenvalues and Eigenvectors
○ Introductions to Eigenvalues
○ Diagonalizing a matrix
○ Symmetric matrices
○ Positive Definite matrices
● Singular Value Decomposition
○ Bases and matrices
EE901
Probability and
Random
Processes
5
This module will focus on strengthening the
foundation of probability keeping its application in
communications in mind. It discusses the
concepts of probability space, random variables,
their CDF and PDF/PMF, functions of random
variables, random variable transformations, the
Law of Large Numbers and random processes.
● Introduction to probability theory
● Introduction to Probability and Probability
Space
● Properties of Probability Measure
● Random Variables
● Distribution of Random Variables
● CDF and PDF/PMF, Continuous and Discrete
Random Variables, Examples of Random
Variables
● Expectation and Moments
● Variance, MGF
● Functions of Random Variables
● Transformation of discrete random variables
● Transformation of continuous random variables
● Multiple Random Variables
● Random Variable Transformation
● Sampling of random variable and empirical
statistics using computer simulations
● Conditional Expectation Distribution
● Limit Theorems
● Law of Large Numbers, Central Limit Theorem,
Deviations
● Introduction to Random Processes and
Examples
● Distribution of Random Processes
● Random Processes via Linear Systems
EE902
Advanced ML
Techniques for
Wireless
Technology
5
This module will cover advanced Machine
Learning (ML) algorithms for Wireless
Communication. A variety of Machine Learning
tools such as the Linear Regression, Logistic
Regression, Support Vector Machines,
Discriminant Analysis and several others will be
studied, followed by their rigorous analysis.
Another important aspect of the program is to
study data pre-processing techniques such as
Principal Component Analysis for feature
selection. Furthermore, other schemes will also
be discussed for clustering, such as K-means,
Probabilistic Clustering, Naïve Bayes and
Decision Tree Classifiers. It is also intended to
cover algorithms from modern Probabilistic
Inference, Online Learning and Probabilistic
Graphical Models to comprehensively analyze
their performance. These will involve concepts
such as Likelihood Maximization, Bayesian
Learning, and Independent Component Analysis.
● Linear Regression
○ Regression applications,
○ Nomenclature,
○ Problem formulation and solution,
○ Online learning
● Logistic Regression
○ Logistic function,
○ Parametric modeling,
○ Likelihood maximization,
○ Online learning for parameter
estimation
● Support Vector Machines
○ SVM applications,
○ Parallel hyperplanes,
○ Maximum margin classifier,
○ Soft classifier
● Linear Discriminant Analysis
○ Multivariate Gaussian modeling,
○ Likelihood Ratio test,
○ Discriminant function
● Naïve Bayes
○ Discrete feature vectors, Naïve
○ Bayes assumption,
○ Calculation of posterior probabilities,
○ Laplacian smoothing
● Decision Tree Classifiers(DTC)
○ DTC structure,
○ choice of best attribute,
○ Concept of Entropy,
○ Mutual Information or Information Gain
● K-Means and Probabilistic Clustering
○ Unsupervised learning,
○ K-Means procedure,
○ EM Algorithm,
○ Soft clustering
● Dual SVM, Probabilistic Graphical Models
○ Dual SVM,
○ Kernel SVM,
○ Bayesian networks, Factorization of
PDF,
○ Bayesian inference over graphs
EE903
Machine
Learning for
Signal
Processing
5
This module aims at introducing machine learning
(ML) techniques used for various signal
processing applications. There will be spectral
processing methods for the analysis and
transformation of signals. The lectures will focus
on mathematical principles, and there will be
coding-based assignments for implementation.
Prior exposure to ML is not required. Intuitive
understanding and illustrative examples will be
provided for an easy grasp of the principles.
● Digital Signal Processing basics
● Machine Learning basics
○ Supervised Machine Learning
○ Model Evaluation
● Linear Regression and Classification
● Neural Networks
● Programming Tools: Tensorflow and Keras
● Unsupervised Machine Learning
● Gaussian Mixture Models
● Some Applications in Signal Processing
(time permitting)
EE904
Deep Learning
for
5
Recently, the deep learning techniques have
become popular and widely used in industrial
● Introduction and applications of AI, ML and DL
● DL applications and concepts in
Communications
applications, autonomous driving, robotics and
automation, healthcare, disease diagnosis and
finding its applications in communication
engineering. Its impressive image generation
ability has found application in art, paintings and
ancient image recovery. This module will cover
deep learning, machine learning methods and
their applications in communications. Deep
learning for communications is a novel field that
offers many attractive interdisciplinary research
areas at the interface between information theory,
machine learning and communications
engineering.
communications
● Mathematical basics for ML
● Regression and Classification
● Neural networks and optimization algorithms
● Convolutional neural networks
● State of the art CNN architectures
● Feature representation and learning
● Programming demo application (python)
● Ground penetrating radars and applications
● Input signals representation and classification
(audio, image)
● Millimeter-waves and object detection
● Load balancing and optimal resource allocation
● Optical communication and pattern recognition
● Wi-Fi and indoor localisation
● AI for satellite communication
EE905
Detection and
Estimation
Theory
5
The goal of this module is to introduce the
fundamentals of detection and estimation. The
module will cover several applications from signal
processing and communications, also.
● Structure of statistical reasoning, Introduction
to Estimation theory,
● Review of Random variables, vectors,
processes, and their statistical description,
● Estimation: Minimum Variance Unbiased
Estimator, Cramer Rao Lower Bound (CRLB)
for scalar and vector parameters, Estimation:
Maximum Likelihood Estimation (MLE),
Maximum Aposteriori Estimation (MAP), Linear
Least Squares (LLSE) with examples of
Gaussian mixture modeling (GMM) and Hidden
Markov Modeling (HMM),
● Detection: Introduction, Neyman Pearson
theorem, Binary and Multiple hypothesis
testing, Examples, Spectrum Estimation: Non-
Parametric (Periodogram, Welch methods) and
Parametric (MVDR method).
EE906
Speech and
Audio Coding for
Communication
5
This module aims to introduce the students to
topics in automatic speech and audio processing.
● Linear Algebra Refresher
● Probability Theory Refresher
● Digital Signal Processing Refresher
● Psychoacoustic principles
● Linear Predictive Coding
● Filter Bank Representations
● Cepstral Representations
● Audio quantization and bit allocation
● Audio coding standards: MPEG
EE907
Basics of Convex
Optimization
5
Convex optimization has recently been applied to
a wide variety of problems in EE, especially in
signal processing, communications, and
networks. The aim of this module is to train the
students in application and analysis of convex
optimization problems in signal processing and
wireless Communications. At the end of this
module, the students are expected to:
● Be able to recognize convex optimization
problems arising in these areas.
● Be able to recognize ‘hidden’ convexity in
many seemingly non-convex problems;
formulate them as convex problems.
● Background on Linear Algebra
● Convex Sets
● Convex functions
● Convex Optimization Problems, Linear
Programs, Quadratic Programs, SOCP
● Duality theory, KKT conditions
● Semidefinite Programming
EE908
Convex
Optimization in
5
Convex optimization has recently been applied to
a wide variety of problems in EE, especially in
signal processing, communications and networks.
The aim is to train the students in the application
● Background on Linear algebra (Inner Product,
Norm, EVD, SVD)
● Affine sets, convex sets, cones
● Convex functions, zeroth, first and second
SPCOM
and analysis of convex optimization problems in
signal processing and wireless communications.
order conditions for convexity
● Convex optimization problems, change of
variables, LP, QP
● Second order cone programming, Robust
optimization
● Lagrange duality, KKT conditions
● Conjugate functions, Linear Fractional
Programming
● Zero Sum Games
● Geometric Programming and applications in
power control
● Schur’s complement, Linear matrix inequality,
SDP
● Semidefinite relaxation
EE909
Estimation for
Wireless
Communications
5
This module covers principles of estimation
theory and algorithms for wireless communication
systems. Estimation theory provides a large
variety of tools and techniques that are widely
applied in the design and implementation of
4G/5G wireless systems. Various signal
processing procedures in communication
systems, such as channel estimation,
equalization, synchronization etc. which are also
employed in MIMO (Multiple-Input Multiple-
Output) and OFDM (Orthogonal Frequency
Division Multiplexing) based 3G/ 4G wireless
systems, are based on fundamental concepts in
estimation theory. Further, recent research
developments in areas such as Wireless Sensor
Networks (WSNs) also employ several tools from
estimation theory towards distributed parameter
● Introduction and Maximum Likelihood
○ Basics of Estimation,
○ Maximum Likelihood (ML)
○ Application: Wireless Sensor Network
○ Reliability of Estimation
● Application in wireless systems channel
estimation
○ Application: Wireless Fading Channel
○ Estimation, Cramer-Rao Bound for
Estimation
● ML for vector parameters
● Vector ML applications
● MMSE Principle for scalar parameters
● MMSE for vector parameters
● Application of MMSE for OFDM channel
● Application of MMSE for MIMO Channel
estimation, etc. Therefore, principles of estimation
are naturally of significant interest in research and
industry, which will be introduced in this module.
EE910
Digital
Communication
Systems I
5
The fundamentals of digital communication
systems, emphasizing the physical layer aspects
of communications. The module discusses,
among other topics, modulation techniques and
optimum receivers for the AWGN channel. The
module will give tools to analyze and characterize
the performance of digital communication
systems.
● Introduction
○ An introduction to digital
communication
○ Communication channels and models
○ Review of signals
○ Representation of lowpass and
bandpass signals
● Mathematical preliminaries
○ Signal space representation of
waveforms
○ A brief introduction to random variables
○ Complex Random variables
○ A brief introduction to random
processes
● Memoryless modulation
○ Digital Modulation: An Introduction
○ Pulse Amplitude Modulation, Phase
Shift Keying, and Quadrature Amplitude
Modulation
○ Orthogonal, bi-orthogonal, and simplex
signaling
● Modulation with memory
○ Continuous Phase Frequency Shift
Keying
○ Continuous Phase Modulation
● Optimum receivers for AWGN channels
○ Optimal Detection fort a vector AWGN
channel
○ Waveform and vector AWGN channels
○ Optimal Detection for Binary Antipodal
Signaling
○ Correlation Receiver, Matched Filter
Receiver
● Probability of error computation for
coherent detection
○ Optimal detection and error probability
for ASK or PAM, and PSK signaling.
○ Optimal detection and error probability
for QAM signaling
○ Optimal Detection and error Probability
for Orthogonal,Bi-Orthogonal and
Simplex Signaling . .
● Noncoherent detection
○ Noncoherent detection of carrier
modulated signals
○ Error Probability of Orthogonal
signaling with noncoherent detection
○ Differential Phase Shift Keying
● Detection of signals with memory
○ Maximum likelihood sequence
estimator: .viterbi Algorithm
○ Optimum receivers for CPM signals
EE911
Data
Communication
Networks
5
This 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 the modern-day networks and
the principles of designing reliable networked
systems.
● Computer networks and the Internet,
fundamentals of circuit and packet switching,
● Network simulation using Netsim, packet
capture using Wireshark,
● Application layer, Transport layer, Network
layer, Routing algorithms, Link layer, ARQ
protocols, Error detection and correction,
Medium access control protocols,
● Wireless networks and mobility
EE912
Simulation
Techniques for
Modern Wireless
5
Numerical evaluation is a quick way to evaluate
complex systems where analysis is difficult. 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 great focus on their actual implementations.
The module is a balanced version of theory and
implementation. It would discuss fundamental
tools in numerical
techniques and their applications to
communications.
● Introduction to
○ Introduction to
○ Introduction to Simulation Methodology
● Signal/Systems
○ Representation of Signals
○ Representation of Systems
● Random Signals
○ Random Variables
○ Random Signals
● System Dynamics
○ Numerical Techniques
○ Differential Equations and Markov
Chains
● Monte-Carlo Simulations
● Link Level Simulation
○ Simulation of a communication
channel-I
○ Simulation of a communication
channel-II
○ Wireless Channel -I
○ Wireless Channel -II
● Advanced Link Level Simulation
○ Advanced Modulation
○ MIMO
● System Level Simulation
○ Mobile Ad-hoc Networks
○ Cellular Networks
○ Millimeter wave and THz Networks
EE913
Foundations of
Information
Theory and Data
Compression
5
In this module, we 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.
● Introduction: Entropy, Relative Entropy, Mutual
Information; Information Inequalities
● Block to variable length coding: Kraft’s
inequality.Shannon- Fano coding, Huffman
coding, adaptive Huffman coding;
● Variable to block length coding: Tunstall
coding; Block to block length coding: Typical
sequences;
● Variable to variable length coding-I: Arithmetic
codes, LZ77, LZ78, LZW algorithms.
● Asymptotic Equipartition Property.
● Coding for sources with memory.
● Image Compression: Discrete Cosine
Transform, JPEG.
EE914
Error Control
Codes: Theory
and Practice
5
In this module, students will study the design of
error-correcting codes for applications in
communication systems. In particular, the
students 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.
● Introduction to Error Control Coding
○ Introduction
○ Decoding Strategies
● Linear Block Codes
○ Linear Block Codes: An Introduction
○ Decoding of linear block codes
● Linear Block Codes: Examples & Distance
Properties
○ Distance properties of linear block
codes
○ Some linear block codes
○ Reed-Muller codes
● Convocational Codes
○ Convolutional Codes:An Introduction
○ State Diagram & Trellis Diagram
○ Classification of convolutional encoder
○ Realization of convolutional encoder
● Decoding of Convolutional Codes
○ Viterbi decoding
○ BCJR algorithm
● Turbo Codes
○ Turbo codes: An introduction
○ Turbo Decoding
● LDPC Codes
○ Low-density parity-check codes: An
introduction
○ Decoding of low-density parity check
codes-I: Bit Flipping Algorithm
○ Decoding of low-density parity check
codes-II: Belief Propagation Algorithm
● Polar Codes
○ Polar codes: An introduction
○ Decoding of polar codes-I: Successive
cancellation decoder
○ Decoding of Polar codes-II: Successive
cancellation list decoder
EE915
PYTHON-Based
Machine
Learning
Simulation for
Wireless
Systems
5
As part of the course, Students will participate
and successfully complete several PYTHON-
based projects and case-studies on key ML
techniques such as Linear Regression, Logistic
Regression , Support Vector Machines, Linear
Discriminant Analysis, Principal Component
Analysis and other.Students will also develop the
skills to effectively use integrated development
environments (IDEs) in PYTHON for tackling
more extensive ML projects in the future.
● Introduction to PYTHON libraries,ML
Packages, Principal Component Analysis
(PCA) : Introduction to PYTHON Libraries and
PCA algorithm, Project1: PCA -Based
Clustering
● Linear RegressionRegression applications,
Problem formulation and solution , Project2:
PYTHON-Based Regression
● Logistic Regression : Logistic function,
Likelihood Maximization , Project3: PYTHON
for Logistic Regression
● Support Vector Machines: SVM application,
Maximum margin classifier, Kernel SVM,
Project4: PYTHONProject for SVC
● Naive Bayes: Discrete feature vectors, Naive
Bayes assumption, Calculation of posterior
probabilities, Project 5: Naive Bayes
Classification using PYTHON
● Linear Discriminant Analysis: Multivariate
Gaussian Modeling, Likelihood Ratio test ,
Project 6: PYTHON-based LDA
● Decision Tree Classifiers (DTC): DTC
structure, Mutual Information or Information
Gain, Project 7: Building a Decision Tree
Classifier using PYTHON
● K-Means and Probabilistic Clustering:
Unsupervised learning, K-Means procedure,
Project 8: Clustering Analysis using PYTHON
EE916
Digital
Communication
Systems II
5
In this module, we will cover the fundamentals of
digital communication systems, emphasizing the
physical layer aspects of communications. Our
focus will be on signal design and Communication
through band-limited channels and
Communication over multipath fading channels in
the second part of the module. Theory and
practice of 5G wireless communication systems.
● Communication over Bandlimited
Channels-I
○ Signal Design for Bandlimited
Channels: The Nyquist Criterion for No
ISI
○ Partial-Response Signals
○ Data Detection for Controlled ISI.
● Communication over Bandlimited
Channels-II
○ Probability of Error for Detection of M-
ary PAM Signaling Using Partial
Response Signals
○ Signal Design for Channel with
Distortion
○ Optimum Receiver for Channels with
ISI and AWGN
● Equalization-I
○ Linear Equalization: Zero Forcing
Criterion
○ Linear Equalization: Minimum Mean
Square Error Criterion
○ Decision-Feedback Equalization
○ Equalization at the
Transmitter=Tomlinson- Harashima
Precoding
● Equalization-II
○ Adaptive Equalizer: LMS Algorithm
○ Adaptive Equalizer: RLS Algorithm
● Communication over Fading Channels-I
○ Characterization of Fading Multipath
Channels
○ Signal Propagation Characteristics
○ Types of Fading
○ Simulation of Fading Channels
● Communication over Fading Channels-II
○ Optimum Receivers for Fading
Channels Under Different Conditions
such as
○ Optimum Receivers for Fading
Channels Under Different Conditions
such as
● Synchronization-I
○ Carrier Recovery and Symbol
Synchronization in Signal
Demodulation
○ Maximum-Likelihood Carrier Phase
Estimation
○ Phase-Locked Loop
● Synchronization-II
○ Decision-Directed PLL
○ Non-Decision-Directed Loops
○ Maximum-Likelihood Timing Estimation
○ Non-Decision-Directed Timing
Estimation
○ Joint Estimation of Carrier Phase and
Symbol Timing
EE917
PYTHON-Based
Simulation,
Design and
Analysis of
Wireless
Systems
5
As a part of this module, students will participate
and successfully complete several PYTHON-
based projects on key 4G/5G wireless
technologies such as Multiple-Antenna Systems,
OFDM, MIMO, MIMO-OFDM in significant detail.
Students will also be introduced to various
concepts from a practical perspective, such as
beamforming, channel estimation, optimization,
detection, and bit-error-rate (BER) performance.
In these projects, students will also gain exposure
to a variety of Python libraries and develop the
skills to effectively use integrated development
environments (IDEs) for tackling more extensive
projects in the future.
● Introduction to PYTHON
○ Introduction to PYTHON
Programming and Packages for
Simulation and Analysis of
Communication Systems.
● Wireless channel modeling and digital
system simulation
○ PYTHON-Based Wireless Channel
Modeling and Analysis.
○ PYTHON-Based Digital Comm. System
Simulation and Performance.
● Wireless system simulation and analysis
○ PYTHON-Based Wireless System
Simulation and Performance.
● Multiple antenna systems, beamforming,
diversity and BER performance
○ PYTHON-Based MRC Beamforming for
Multi-Antenna Systems.
○ PYTHON-Based EGC and Selection
Combining for Multi-Antenna Systems.
● MIMO systems – Transceiver design and
Analysis
○ PYTHON - Based MIMO ZF/MMSE
Receive Design
○ PYTHON- Based MIMO ML Receiver
Design.
● MIMO optimization for rate maximization,
MIMO channel Estimation
○ SVD-Based MIMO optimization.
○ PYTHON - Based MIMO channel
estimation – ML and MMSE estimators.
● Orthogonal Frequency Division
Multiplexing (OFDM) Simulation
○ PYTHON-based 4G/ 5G OFDM System
● High-Speed MIMO OFDM technology for 4G
and 5G
○ PYTHON-based Project for Simulation
and Performance of 4G/5G MIMO-
OFDM Technology.
EE920
Wireless
Communication
5
The module has both theoretical and practical
flavours. It aims to explain the fundamental
concepts and insights behind the development of
modern 4G/ 5G wireless communication
technologies such as OFDM, MIMO, and Multi-
user MIMO.
● AWGN channel modeling, SNR concept and
BER performance for BPSK, QPSK and higher
order modulations
● Fading channel models, BER analysis, Deep
fade
● Multiple antenna systems, Beamforming and
diversity concepts
● MIMO Technology, Linear Receivers ZF,
MMSE and performance
● SVD, Precoding/Combining in MIMO, Optimal
Power Allocation, Space Time Block Codes
● Single carrier vs. Multi Carrier implementation,
IFFT/ FFT
receivers in OFDM, Cyclic prefix and circular
convolution
● MIMO OFDM system model, transmission/
reception and
receiver structure
● Wireless channel models, delay spread,
frequency selective/frequency flat channels,
mobility and Doppler modeling
EE921
MIMO Wireless
Communication
5
This module will cover state-of-the-art multiple-
input multiple-output (MIMO) wireless transmitter
and receiver designs which are being used in the
5G cellular systems.
● Review of mathematical basics: Linear algebra
and information theory
● Wireless communication basics: Capacity of
single-antenna wireless channels
● Single-cell single-user MIMO: Full transmit and
receive channel state information (CSI),
Capacity and transceiver design, Receive CSI
alone
● Capacity and transceiver design
● Linear and non-linear ZF/MMSE receivers
● Space-time coding, Diversity concepts
EE922
Simulation-
Based Design of
5G-NR Wireless
Standard
5
Students normally have good theoretical
background in wireless communications systems,
but negligible exposure on the use of this theory
to design practical wireless systems. Many jobs in
the wireless communication industry require
design of standards-based practical wireless
systems. The main objective of this module is to
bridge the gap between the theory and practice of
5G NR wireless communication systems, and
consequently, the gap between the academia and
● 5G-NR transmission structure:
○ use cases – eMBB (enhanced Mobile
Broadband), mMTC (massive Machine
Type Communications) and
URLLC(Ultra-Reliable Low-Latency
Communications)
○ 5G Spectrum,
○ Principles of adaptive modulation and
coding
○ ARQ and HARQ protocols, frame
the industry.
This module will teach:
i) Underlying concepts of 5G NR transceiver
blocks.
ii) How to read the 5G NR standard documents to
understand the transceiver
specifications. Students will then design and
simulate a 5G NR-compliant wireless system in
MATLAB. The module, therefore, involves a
MATLAB coding component, which will also be
considered for evaluation.
structure
● 5G-NR Transport-Channel Processing
○ Notion of transport block (TB),
○ CRC generation for TB, code block
segmentation,
○ LDPC coding ideas, rate matching,
○ crumbling, modulation, baseband
passband representation, Resource
Mapping
● Reference Signal Design 5G-NR Initial
Access
○ Cell-specific reference signal
○ Demodulation reference signal
○ Concept of synchronization signals and
broadcast channels.
EE923
Analysis of
Wireless
Systems
5
This module will cover tools from stochastic
geometry to model and analyze modern wireless
systems being used in 4G and 5G systems. After
completion of the module, the students should be
able to apply mathematical tools from stochastic
geometry in their own research to analyze
modern wireless
Systems.
● Need to analytical frameworks, Poisson point
process, Boolean Models, Campbell theorem,
Probability generating functional, Marked Point
process,
● Performance analysis: SINR and rate
coverage, System level analysis of MANET,
Analysis of downlink cellular network, uplink
networks,
● Modeling blockages via Boolean models,
Modeling of Cyber-Physical Systems,
● System level analysis of millimeter (mmWave)
and TeraHertz (THz) networks,
● System level analysis of Visible light
communication
EE924
Advanced
Modulation and
Multiple Access
for Next
Generation
Wireless
Systems
5
This module will cover modern modulation and
multiple access schemes
that are potential candidates for futuristic
communication systems.
● OFDM
○ Evolution of cellular communications,
Orthogonal
Frequency Division Multiplexing
(OFDM), Peak-to-Average Power Ratio
Reduction in OFDM System,
Phase Noise in OFDM
● FBMC and GFDM
○ Filter Bank Multicarrier Modulation
(FBMC), OQAM,
Block Spread FBMC, Pruned DFT-
Spread
FBMC-OQAM,universal filtered
multicarrier
modulation (UFMC), Spectrally
precoded OFDM (SP-OFDM)
Generalized Frequency Division
Multiple Access
● OTFS
○ Orthogonal Time Frequency and Space
(OTFS),
waveform design for OTFS, OTFS with
index
Modulation, signal detection,
performance evaluation, STBC-OTFS,
SM-OTFS, OTFS-OMA, OTFS-NOMA,
Zak Transform Perspective of OTFS
● NOMA
○ Non-Orthogonal Multiple Access,
Downlink and
Uplink NOMA, MIMO-NOMA,
Cooperative NOMA,
NOMA in HetNets, NOMA in Millimeter
Wave
Communications, NOMA in Cognitive
Radio
Networks, NOMA based D2D
communications
● SCMA, LDSMA and GFMA
○ Sparse Code Multiple Access (SCMA):
Codebook design, Decoder design
Grant-Free SCMA: Collision
Resolution, Low-Density Spreading
Multiple Access
(LDSMA): LDS-CDMA, LDS-OFDM,
MC-LDSMA,
Radio Resource Allocation, Grant Free
Multiple
Access (GFMA): Resource
Configuration, HARQ
Procedure, Contention and Resolution,
UE Activity, Detection
● IDMA, IGMA and PDMA
○ Interleave Division Multiple Access
(IDMA):
Transmitter, Receiver, Performance
Evaluation,
Power Control. Superposition Coded
Modulation (SCM). Random Access,
IDMA in MIMO systems,
Interleave-Grid Multiple Access (IGMA):
Transmission Schemes, Interleaving
and Grid-Mapping Process, Receiver,
Performance Evaluation, Pattern
Division Multiple Access (PDMA):
Uplink, Downlink, Pattern Matrix
Design, Detection Algorithms.
● HDMA, ODMA
○ Holographic-Pattern Division Multiple
Access
○ (HDMA): Reconfigurable Holographic
Surface (RHS), Holographic Pattern
Construction, Multi-User Holographic
Beamforming Performance Analysis,
Super-Sparse On-Off Division Multiple
Access
(ODMA), Random Access vs Multiple
Access
● RSMA
○ Rate Splitting Multiple Access (RSMA):
Downlink,
Uplink, PHY architecture, Resource
Allocation. Multi-cell RSMA, RSMA in
MIMO systems
EE925
RF Systems for
Communication
5
RF front end is an important part of the wireless
communication system. A system designer needs
to understand the performance of various
components, such as transmission lines,
matching systems, filters, amplifiers, oscillators,
antennas, etc. So that the students can be
integrated efficiently. This module introduces
● Parameters used to specify the performance of
RF components,
● transmission lines,
● scattering matrix,
● 1 DB compression,
● third-order intercept point, noise figure, phase
noise, etc.,
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 module
then focuses on block-level descriptions of RF
systems, system calculations, and trade-offs in
block-level specifications to achieve overall
performance. Finally, the module ends with an
exposure to some of the RF measurements using
vector network analyzer and spectrum analyzer.
● RF system block-level description of RF
system,
● System calculations and trade-off in the block
level specifications to achieve the overall
performance RF Measurements using vector
network analyzer and spectrum analyzer,
antennas.
EE930
Detection for
Wireless/
Detection for
Wireless
Communication
and Machine
Learning
5
This module aims to cover principles of detection
theory and algorithms for wireless communication
systems and machine learning (ML) applications.
Concepts in detection lay the foundation for
several procedures in the implementation of
4G/5G wireless systems, especially at the
receiver. Detection techniques play a
fundamental role in the demodulation of the
symbols toward mapping them to a digital
constellation. Furthermore, detection algorithms
also play a vital role in Machine Learning (ML)
applications towards face recognition, fraud
detection etc. Also, decision rules based on
detection theory are used extensively for primary
user discovery in cognitive radio (CR) technology,
slated for use in 5G and beyond networks.
Distributed detection techniques are of significant
interest toward decision-making and learning in
Wireless Sensor Networks (WSNs) that power
● Introduction and Maximum Likelihood
Detection
○ Basics of Detection
○ Maximum Likelihood (ML) Detection
○ Likelihood Ratio Test (LRT)
● Application in wireless systems
○ Binary Hypothesis Testing
○ Probabilities of Detection and False
Alarm
○ Probability of Error
● NP Criterion, Multiple Hypothesis Testing
○ Neyman-Pearson Criterion for Optimal
Detection,
○ Multiple Hypothesis Testing
○ Face Recognition
● MAP Detector, Gaussian Discriminant
Analysis
○ Maximum A Posteriori Probability
○ (MAP) Detection rule
IoT applications in 5G. Hence, principles of
detection theory are of great value for research,
design and implementation of wireless
communication systems and machine learning,
which will be rigorously covered in this module.
○ Probability of Error Gaussian
Discriminant Analysis
● MIMO OFDM
○ Detection in MIMO/ OFDM Systems
○ Bit-Error Rate (BER)
● Bayesian Detection
○ Bayesian detection for random signals
○ Energy Detector and Performance
○ Chi-squared random variables
● Generalized Likelihood Ratio Test (GLRT)
○ Detection with unknown parameters
○ Generalized Likelihood Ratio Test
(GLRT) and performance
● Distributed Detection
○ Principles of Distributed Detection
○ Applications in Sensor Networks and
IoT
EE931
Advanced
Wireless
Transceiver
Processing
Techniques
5
The goal of the module is to present various
advanced techniques for transceiver design in
4G/5G wireless systems. Several algorithms will
be presented such as the Kalman filter and
Adaptive LMS filter for scalar/vector channels.
The Orthogonal Matching Pursuit (OMP) and
Simultaneous OMP
will be presented for sparse parameter and
channel estimation in MIMO OFDM
systems. The Expectation-Maximization (EM)
algorithm will also be described,
which is a cutting edge algorithm with several
● Kalman Filter
○ Principle of Kalman filter,Application of
Kalman filter for time selective 4G/ 5G
channel estimation
● Compressive Sensing
○ Sparse estimation and sparse signal
recovery,
Orthogonal Matching Pursuit (OMP)
and Simultaneous Orthogonal Matching
Pursuit (SOMP)
algorithms for Sparse Estimation in
MIMO OFDM
applications in Machine Learning Channel
Estimation and Bayesian Learning. Block
Diagonalization and Successive Optimization
(SO) will be described for MU-MIMO
Transmission. This will be followed by other state-
of-the-art techniques, such as MUSIC for DoA
estimation, Optimal pilot construction in MIMO
systems and Robust transceiver design
techniques.
● Adaptive Signal Processing
○ Introduction to adaptive signal
processing, applications in wireless,
Steepest Descent, Least Mean
Squares algorithm,Convergence in
mean, MSE
● Expectation-Maximization (EM) algorithm
○ Applications of EM: Unsupervised
learning – Probabilistic clustering,Blind
channel estimation, Sparse Bayesian
Learning for sparse channel estimation
● Multiuser MIMO Techniques
○ Multi-user MIMO Uplink Transmission,
MU MIMO Downlink with Zero-Forcing,
MU MIMO Block Diagonalization and
Successive Optimization
● MUSIC Algorithm for Direction of arrival
estimation
○ Introduction to array processing,Signal
covariance matrix, Multiple Signal
Classification for Direction of Arrival
(DoA) estimation algorithm
● Optimal Pilot Design
○ Pilot-based MIMO channel estimation,
optimal pilot design, pilot design with
prior information
● Robust transceiver Design
○ Channel uncertainty models,Robust
beamformer design
EE932
Introduction to
Reinforcement
5
Reinforcement learning (RL) is a type of machine
learning paradigm where an
● Introduction
○ Basic terminology of an RL framework:
Learning
agent learns how to behave in an environment by
performing actions and receiving feedback in the
form of rewards or penalties. RL algorithms have
proven effective in solving intricate problems
across diverse domains such as robotics, gaming,
finance, and communication
networks. This course aims to provide students
with a solid understanding of the various RL
algorithms, enabling them to apply this knowledge
to their specific research areas.
States, Actions, Reward, Environment,
etc.
● Multi-armed Bandits
○ n-Armed Bandit problem, UCB
algorithm,
Contextual bandits
● Finite Markov Decision Processes and
Dynamic Programming
○ Markov Decision Process, Value
functions,
Bellman expectation equation, Bellman
optimality equation, Policy Iteration,
Value
iteration
● Monte-Carlo and Temporal-Difference
based Tabular methods
○ Monte Carlo prediction and control, TD
prediction, SARSA, Q-Learning
● Function approximation-based methods
○ Q-learning with function approximation,
Policy gradient methods: REINFORCE,
Actor-
critic methods
● Applications
○ Discussion of RL applications
EE999
Project Module
5
The goal of this module is to have the students do
an industrial-relevant project on a topic related to
modern communication systems. The project
topic may include any topics in the general area
of Advance Communication Systems.
Capstone Project
