IITK Online PG Program

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: Introduction 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 ● 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 Variables 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 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 Communications	5	Recently, the deep learning techniques have become popular and widely used in industrial applications, autonomous driving, robotics and automation, healthcare, disease diagnosis, and communication engineering. Its impressive image generation ability has found applications 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.	● Introduction and applications of AI, ML and DL ● DL applications and concepts in 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.	● 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 Maximum Likelihood Estimation (MLE) Maximum Aposteriori Estimation (MAP) Linear Least Squares (LLSE) Examples: Gaussian Mixture Modeling (GMM), Hidden Markov Modeling (HMM) ● Detection: Introduction, Neyman Pearson theorem Binary and Multiple hypothesis testing Examples ● Spectrum Estimation: Non-Parametric (Periodogram, Welch methods) 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 SPCOM	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 and analysis of convex optimization problems in signal processing and wireless communications.	● Background on Linear algebra (Inner Product, Norm, EVD, SVD) ● Affine sets, convex sets, cones ● Convex functions, zeroth, first and second 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 and OFDM 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 estimation, etc. Therefore, principles of estimation are naturally of significant interest in research and industry, which will be introduced in this module.	● 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
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 for 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 modern-day networks and the principles of designing reliable networked systems.	● Fundamentals: Computer networks and the Internet Fundamentals of circuit and packet switching ● Tools and Simulations: Network simulation using Netsim Packet capture using Wireshark ● Networking Layers: Application layer, Transport layer, Network layer Routing algorithms Link layer ● Protocols and Error Handling: ARQ protocols Error detection and correction ● Medium Access and Mobility: 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 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, 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: Introduction, Decoding of linear block codes ● Distance Properties: Distance properties of linear block codes, Some linear block codes, Reed-Muller codes ● Convolutional Codes: Introduction, State Diagram & Trellis Diagram Classification and Realization of convolutional encoder ● Decoding of Convolutional Codes: Viterbi decoding, BCJR algorithm ● Turbo Codes: Introduction, Turbo Decoding ● LDPC Codes: Introduction Decoding I: Bit Flipping Algorithm Decoding II: Belief Propagation Algorithm ● Polar Codes: Introduction Decoding I: Successive cancellation decoder Decoding II: Successive cancellation list decode
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 others. 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, Project 1: PCA-Based Clustering ● Linear Regression: Regression applications, Problem formulation and solution, Project 2: PYTHON-Based Regression ● Logistic Regression: Logistic function, Likelihood Maximization, Project 3: PYTHON for Logistic Regression ● Support Vector Machines: SVM application, Maximum margin classifier, Kernel SVM, Project 4: PYTHON Project 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 ● 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 Communication 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 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.	● 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 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.	● Analytical frameworks: Need for 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 and uplink cellular networks ● Advanced modeling: Modeling blockages via Boolean models Modeling of Cyber-Physical Systems ● System-level analysis of emerging technologies: Millimeter Wave (mmWave) and TeraHertz (THz) networks Visible Light Communication systems
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 various parameters, such as scattering matrix, 1 dB compression, third-order intercept point, noise figure, phase noise, 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.	● Parameters used to specify the performance of RF components ● Transmission lines ● Scattering matrix ● 1 dB compression ● Third-order intercept point ● Noise figure ● Phase noise ● RF system block-level description ● System calculations and trade-off in block-level specifications ● RF measurements using vector network analyzer and spectrum analyzer ● Antennas
EE930	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. 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 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.	● 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 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 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.	● 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 ● 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 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.	● Introduction: Basic terminology of an RL framework: 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.	Capstone Project.

Next Generation Wireless Technologies (NGWT)

*Initially Communication System (COMM)