Note: The course catalogues, the SGS Calendar, and ACORN list all graduate courses associated with ECE – please note that not all courses will be offered every year.
ECE537H1 Random Processes
Introduction to the principles and properties of random processes, with applications to communications, control systems, and computer science. Topics include random vectors, random convergence, random processes, specifying random processes, Poisson and Gaussian processes, stationarity, mean square derivatives and integrals, ergodicity, power spectrum, linear systems with stochastic input, mean square estimation, Markov chains, recurrence, absorption, limiting and steady-state distributions, time reversibility, and balance equations.
ECE1501H Error Control Codes
This course provides an introduction to error control techniques, with emphasis on decoding algorithms. Topics include algebraic coding theory: finite fields, linear codes, cyclic codes, BCH codes and decoding, Reed-Solomon codes; iterative decoding: codes defined on graphs, the sum-product algorithm, low-density parity-check codes, turbo codes.
ECE1502H Information Theory
This course deals with fundamental limits on communication, including the following topics: entropy, relative entropy and mutual information: entropy rates for stochastic processes; differential entropy; data compression; the Kraft inequality; Shannon-Fano codes; Huffman codes; arithmetic coding; channel capacity; discrete channels; the random coding bound and its converse; the capacity of Gaussian channels; the sphere-packing bound; coloured Gaussian noise and water-filling; rate-distortion theory; the rate-distortion function; multiuser information theory.
ECE1504H Statistical Learning
Exclusions: ECE421H, CSC411H1/CSC2515H, ECE1513H
This course is designed for students with a background in communication systems and information theory, interested in doing research in machine learning. The first half of the course will focus on one-shot approaches in multiuser information theory and discuss some applications to machine learning. The second half will develop information theoretic bounds on the generalization error in statistical learning. The final course project is expected to be on a topic at the intersection of information theory and machine learning.
ECE1505H Convex Optimization
This course provides a comprehensive coverage of the theoretical foundation and numerical algorithms for convex optimization with engineering applications. Topics include: convex sets and convex functions; convex optimization problems; least-square problems; optimal control problems; Lagrangian duality theory. Karush-Kuhn-Tucker (KKT) theorem; Slater’s condition; generalized inequalities; minimiax optimization and saddle point; introduction to linear programming, quadratic programming, semidefinite programming and geometric programming; numerical algorithms: descent methods, Newton’s method, interior-point method; convex relaxation; applications to communications and signal processing.
ECE1508H Special Topics in Communications: Multiuser Information Theory
This course will focus on a systematic approach for proving coding theorems for a variety of multi-user channels. A few basic techniques will be introduced in the first part of the course and their application to several multi-user source and channel coding problems will be discussed. Topics include: Point to Point Information Theory, Multiple Access Channel, Broadcast Channel, Distributed Source Coding, Information Theoretic Secrecy, Relay Channels and Source and Channel Coding over Networks.
ECE1508H Special Topics in Communications: Advanced Cellular Systems (4G/5G) – Fundamental Principles and Software Radio Implementation
This course will cover the fundamental concepts in the design of state of the art cellular systems including the theory and implementation of the physical layer in software radio.
The course will cover the basic properties of multipath fading channels including channel variation over frequency, time, and space, and models for fading channel simulation. Sate of the art modulation schemes include QAM, SC-FDMA, and OFDM with soft-decision detection. Advanced antenna techniques including receiver and transmitted diversity, beamforming, and MIMO. The concepts of channel matrix rank, transmission layers, precoding, MU-MIMO, and antenna port concepts as used in LTE/5G.
The physical layer in LTE/5G including frame structure, design of sequences, physical signals, physical shared channels, control channels (4G/5G), broadcast, and access channels. Fundamental channels and physical signals in 5G and their evolution from LTE, including demodulation reference signals, channel sounding signals, position reference signals, and phase tracking signals. Error control techniques including CRC, convolutional code, turbo code, polar codes, and LDPC codes, including Hybrid-ARQ, will be discussed. Basic concepts in software radio, including down-conversion, sampling, synchronization, carrier offset estimation and correction, symbol and frame synchronization, channel estimation, extraction of the various channels from a noisy signal.
The course will have a lab component based on Matlab with the LTE Toolbox and analyze and build receivers for generated LTE/5G test signals and off-air received signals.
ECE1508H Special Topics in Communications: Network Softwarization: Technologies and Enablers
This course is one of two companion courses on network softwarization offered simultaneously in the Winter 2018 session. The first course introduces concepts and principles of network softwarization while the second course (this one) focuses on hands on experience with softwarization technologies and enablers. The courses will be offered simultaneously in 4 Universities, namely University of Waterloo, University of Toronto, Université Laval and École des Technologies Supérieures (ETS).
ECE1508H Special Topics in Communications: Cyber-Physical Security of Connected, Autonomous, Shared, and Electrified Vehicles
This course aims to introduce to graduate students the methods, processes, tools, and standards to specify, build and test automotive cyber-physical security. In the first part, we provide a deep overview of hardware while the second part focuses on the software aspects that allow enabling hardware/software co-design. Through lectures and labs, students shall be exposed to cybersecurity principles at different levels for current automotive systems and connected and autonomous vehicles. They will learn best practices for trustworthy cyber-physical systems security (CPSS) in modern vehicular systems, identify and define properties related to safety and cybersecurity associated with current research and industry trends, and will be able to use and adapt different methods, processes, tools, and standards for analyzing and verifying such properties as relevant for different automotive domains.
ECE1508H Special Topics in Communications: Graphs, Codes, and Inference
This course provides an introduction to error-correction, signal processing, and inference on “graphs”. We will start with graph-based error correcting codes and associated decoding methods and continue on to topics such as signal processing on graphs and other graph-based information processing methods. Viewing (and designing) error-correction codes and decoding algorithms from a graph-based viewpoint has revolutionized the theory and practice of error correction in the past 20 years. While the initial focus of the course is on error correction, the techniques used to analyze the performance of these codes, and the algorithmic methods used to decode them, connect to diverse areas of statistical inference including machine learning and statistical physics. Topics in past offerings of the course include turbo, low-density parity-check (LDPC), “fountain”, spatially-coupled, and Polar codes; iterative decoding techniques such as the sum-product algorithm; code design techniques; threshold analysis via density evolution; coded caching; signal-processing on graphs. The error-correction portion of the course is designed to complement ECE 1501H (Error Control Codes), but that course is not a prerequisite. Students in engineering, the computer sciences, and math will find this course interesting.
ECE1508H Special Topics in Communications: Applied Deep Learning
Deep learning has become an integrated part of many engineering designs, and is a fundamental component of many recent fields, such as Natural Language Processing and Computer Vision. This course gives a comprehensive introduction to deep learning by going through various neural network architectures in detail, exploring their applications and learning their implementation.
The main part of the course goes through the fundamental neural network architectures, namely Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). For each architecture, the course explains (1) the type of problems that can be solved by the architecture, (2) its training and hyperparameter tuning, (3) its efficient implementation, (4) the challenges that are faced in its implementation, and (5) the state-of-the-art techniques to overcome the implementational challenges. The course further introduces the well-known deep learning models used frequently in practice and goes through some selected advanced topics, such as Sequence-to-Sequence Models and Auto-Encoders. Most of the learning in the course is acquired through detailed programming assignments and a final project, where the students get the chance to implement and test their own deep learning model for an applied problem.
ECE1508H Special Topics in Communications: Reinforcement Learning
Prerequisites: ECE1508H “Special Topics in Communications: Applied Deep Learning” / ECE1513H / ECE421H1 / CSC311H1 / CSC413H1 / APS360H1 or equivalent course on Machine Learning, with Neural Networks
Exclusions: CSC2547H
This course provides a concrete understanding of reinforcement learning and its applications. The ultimate goal of the course is to develop hands-on skills in deep reinforcement learning, for which fundamentals of reinforcement learning are first discussed and then deep reinforcement learning algorithms are studied. The course is designed in three major parts: the first part gives the students a warm welcome by taking them through the basic definitions and fundamental concepts. The second part explains fundamental reinforcement learning methods by touching the key model-based and model-free techniques and providing deep understanding of these methods. The last part explores deep reinforcement learning, where deep neural networks are employed to efficiently approximate the developed techniques in Part 2. In this part, we take a look into several topics and algorithms, such as function approximation, deep Q-learning, policy gradient methods and proximal policy optimization algorithms.
The first two parts of the lecture can be readily followed and only require basic knowledge in Linear Algebra and Probability. The last part needs an understanding of concepts in machine learning, particularly the concept of supervised learning and neural networks. A primary course in Machine Learning is a prerequisite for this course. Hands-on and analytic skills are developed in this course through three sets of assignments and a final project. These items extensively deal with programming in Python. Basic knowledge in Python programming is hence required for this course.
ECE1510H Advanced Inference Algorithms
Prerequisite: ECE521H1 or equivalent.
Advanced concepts in machine learning and probabilistic inference. An introductory course on inference algorithms or machine learning should be taken prior to this course. Topics covered: Probability models, neural networks, graphical models, Bayesian networks, factor graphs, Markov random fields (MRFs). Structured models, convolutional networks, transformations as hidden variables, bivariate and trivariate potentials, high-order potentials. Exact probabilistic inference, variable elimination, sum-product and max-product algorithms, factorizing high-order potentials. Approximate probabilistic inference, iterated conditional modes, gradient-based inference, loopy belief propagation, variational techniques, expectation propagation, sampling methods (MCMC). Learning in directed and undirected models, EM, sampling, contrastive divergence. Deep belief networks. Applications to image processing, scene analysis, pattern recognition, speech recognition, computational biology.
ECE1513H Introduction to Machine Learning
Prerequisites: STA286H1, ECE302H1 or equivalent
Exclusions: ECE421H, ECE521H1, CSC411H1/CSC2515H, ECE1504H
An Introduction to the basic theory, the fundamental algorithms, and the computational toolboxes of machine learning. The focus is on a balanced treatment of the practical and theoretical approaches, along with hands on experience with relevant software packages. Supervised learning methods covered in the course will include: the study of linear models for classification and regression and neural networks. Unsupervised learning methods covered in the course will include: principal component analysis, k-means clustering, and Gaussian mixture models. Techniques to control overfitting, including regularization and validation, will be covered.
ECE1511H Signal Processing
Prerequisites: ECE310H1, ECE431H1, ECE302H1 or equivalent.
Signal processing techniques using special purpose digital hardware and general purpose digital computers are playing an increasingly important role. The course deals with some introductory and some advanced topics in the area. In particular, it presents the characterization of random discrete time signals. It provides an introduction to traditional and modern statistical discrete time signal processing frameworks, including processing with second-, higher- and fractional lower -order statistics. It discusses sampling and multirate signal conversion; linear prediction and optimum linear filters; least squares methods for system modeling and design; theory and applications of adaptive filters. It also deals with applications in signal and image processing and analysis.
ECE1512H Digital Image Processing and Applications
Prerequisites: ECE431H1 or equivalent.
This course will present the concepts of the main processing techniques for digital image processing. It will cover image enhancement and restoration, digital filtering (linear and nonlinear), local space operators, image analysis, and elements of vision. It will also describe the impact of digital image processing to the more important fields of application.
ECE1517H Biometric Systems
This is an introductory level course for graduate students or practitioners to gain knowledge and hands-on experiences in biometric systems and security applications. Topics include: Introduction to important biometric security technologies and policies, biometric modalities and signal processing, biometric solutions and applications, biometric encryption and cryptosystems, biometrics identity analysis and privacy considerations.
ECE1518H Seminar in Identity, Privacy and Security
This interdisciplinary course examines issues of identity, privacy and security from a range of technological, policy and scientific perspectives, highlighting the relationships, overlaps, tensions, tradeoffs and synergies between them. Based on a combination of public lectures, in-depth seminar discussions and group project work, it will study contemporary identity, privacy and security systems, practices and controversies, with such focal topics as biometric identification schemes, public key encryption infrastructure, privacy enhancing technologies, identity theft risks and protections, on-line fraud detection and prevention, and computer crime, varying between offerings.
ECE1520H Data Communications I
Prerequisites: ECE417H1 or equivalent.
This is an introductory course on digital transmission. Topics include: signal space concepts, signals with memory and Markovian models, power spectrum of digital signals, bandwidth, baseband transmission: intersymbol interference and Nyquist pulse shaping, optimum coherent symbol detection, binary and M-ary modulation, differential and noncoherent demodulation, error rate – bandwidth efficiency comparisons, sequence detection and the Viterbi algorithm. Equalization. Multi-carrier modulation.
Top
ECE1521H Detection and Estimation Theory
Prerequisites: ECE302H1 or equivalent.
This course presents an introduction to the principles and applications of detection and estimation theories. The main thrust is to show how statistical models can be used to provide optimal and suboptimal signal processing structures for digital communication systems operating over noisy channels. Topics covered include: classical detection theory and hypothesis testing, parameter estimation, binary and M-ary digital modulation, detection in coloured noise, coherent and non-coherent structures, detection of random signals in random noise, EM algorithm.
Top
ECE1524H Service Provider Networks
In last decade telecom industry has gone through transformational changes that started with the introduction of the concept of software defined networking or SDN and the emergence of Big Data as well as Machine Learning techniques. With hyper-scalers like Google and Amazon in the horizon, the landscape for traditional Telco service providers are changing. The course is primarily about this change and its profound impacts in telco service providers from different angles, including architecture, service design, business model, security and privacy. The SDN journey starts by network programmability, that is why the first part of will be walking the students through different steps of building a programmable network. Having programmable network we will have to start building intelligence by introducing closed loop control logics, the second part of the course deals with ideas around creating multilayer control logics, where we employ concepts of Big Data and Machine Learning to create innovative services. Given that SDN is meaningless without proper abstraction and interface modeling, we will discuss model driven approach to network management and from there we open the door to discuss orchestration strategies. Nowadays all telco discussions end with 5G; therefore, we explain 5G with the focus on the role of SDN there, followed by some important 5G use cases including smart cities and IoT. In the last part of the course we zoom into software defined security aspects, as well as a discussion on new methods of creating innovative services. the course will be concluded by discussing some operational aspects of SDN and the role of AI and Machine Learning there.
ECE1528H Special Topics in Data Communications: Internet of Things - From Protocols to Applications
The course exposes students to concepts of the Internet of Things and Fog/Edge computing. The course will provide hands-on experience with IoT devices and IoT networking. Learning Objectives: understanding IoT network technologies and standards; of REST paradigm; IoT protocols; IoT system components; fog/edge networking architectures and IoT security concerns and solutions. Exposure to IoT platforms in edge and cloud; programming platforms for sensors and actuators. Ability to: build IoT application on microcontroller; build interfaces between microcontrollers and gateways/edge devices. Design an IoT device to work with a cloud infrastructure and transfer IoT data to the cloud and between cloud providers.
ECE1529H Adaptive Systems for Signal Processing and Communications
The course explores the theoretical and practical procedures for designing adaptive systems. Topics include decision theory, parameter estimation, supervised learning, unsupervised learning, state-space models, adaptive signal detection, channel characterization, iterative detection, forward-backward adaptive algorithms.
ECE1531H Quantum Information Theory
This is a first course on quantum information and communications theory. Topics covered include: (1) basics of quantum mechanics and quantum information, (2) resource model of quantum information processing, (3) entanglement and entanglement distillation protocols, (4) quantum cryptography and security proofs.
ECE1541H Communication Networks I
This course teaches the fundamentals of network performance and analysis. The topics are: traffic modeling for voice, video and data, self-similarity and long range dependence in the internet, queueing systems, large deviations and buffer management, multiple access communications, scheduling and processor sharing, routing and dynamic programming, vehicular networks.
ECE1542H Communication Networks II
Topics include: layering, distributed algorithms, network algorithms, shortest path routing, coping with link failures, optimal routing and topology design, fairness, flow control.
ECE1543H Mobile Communications Systems
This course will cover basic principles in the design of mobile communication systems, included in the various generations of cellular systems from 1G to 5G. The radio propagation environment: basic radio propagation considerations, Rayleigh and Rician statistics, power spectral density, small scale and large scale signal variation, delay spread, Doppler spread, and angular spread, coherence bandwidth, coherence time, and coherence space; MIMO channel modeling. Link issues: modulation techniques including OFDM, diversity, interleaving, forward error correction. Principles of spread spectrum systems and CDMA. System issues: spectral sharing schemes, frequency re-use, noise and interference analysis, call and packet oriented capacity analysis, and basic scheduling approches including proportional fair. Drop oriented network simulation models. Basic aspects of cellular system standards such as GSM, WCDMA, LTE, and 5G new radio. Familiarization with software radio architecture of commercially available systems including RF chip architecture, FPGA and host processing. RF bands covered, local oscillator management and phase lock, RF filtering, down-coversion, IQ sampling, and digital filtering, frequency and phase synchronization, and demodulation. Issues in the implementation of antenna arrays and massive MIMO. The course will have various exercises based on software radio and matlab including the analysis of real off-the air cellular system pilot and synchronization signals.
ECE1548H Advanced Network Architectures (Network Softwarization: Principles and Foundations)
This course is one of two companion courses on network softwarization offered simultaneously in the Winter 2018 session. The first course (this one) introduces concepts and principles of network softwarization while the second course focuses on hands on experience with technology enablers. The courses will be offered simultaneously in 4 Universities, namely University of Waterloo, University of Toronto, Université Laval and École des Technologies Supérieures (ETS).
ECE1549H Stochastic Networks
Prerequisite: ECE537H1 or equivalent with continuous-time Markov chains.
An introduction to stochastic analysis techniques for complex networked systems, covering queueing networks, random graphs, and stochastic geometry. Main topics include Jackson and Whittle networks, reversible network processes, network utility maximization, stochastic network optimization, Erdős-Rényi and Gilbert models, graph evolution and connectivity, random subgraphs, random point processes, Laplace functionals, marked point processes, and Palm probability.
ECE1550H Physics of Information
Part A: Reversible Computation and the Second Law of Thermodynamics
Reversible Computation: motivation, principle and limitations; Moore’s law and energy cost in classical computations (theory and practice); Landaurer’s principle; Maxwell’s demon and its resolution with information theory; Cost of erasure of information from the Second law of thermodynamics.
Part B: Entropy
The concept of entropy in Physics and Information Theory; Subjective (i.e. observer-dependent) nature of entropy; Resolution of Gibbs’ paradox from information theory.
Part C: von Neumman entropy and quantum computation
From classical (Shannon) entropy to quantum (von Neumann) entropy; Quantum computer as an ultimate reversible computer.
Part D: Carnot cycle in a Quantum World
The smallest possible refrigerator
ECE1551H Mobile Broadband Radio Access Network
Prerequisite: ECE316H1
Course Exclusions: ECE464H1, ECE1508H “Special Topics in Communications: 4G LTE for Mobile Broadband and Evolution Towards 5G”
This course covers Radio Access Network (RAN) aspects of the 5G New Radio (NR). Important RF parameters like power flux density, electrical field and various power definitions are introduced and their relationship to regulatory requirements and standards based usage are covered in great detail. Also, various RF impediments such as the noise figure, out of band emissions and ACS/ACLR are introduced. The link budget, receiver sensitivity, channel models and how they relate to 5G systems are explained. Spectrum and RF characteristics of 5G NR are an important part of the course. Moreover, we will go over the architectural solutions, remote radio heads (distributed radio solutions), and important hardware components in the network. Throughout the course, students will get substantial exposure to the practice-based content not commonly found in the textbooks. The course will offer an insight into the important industry standards and initiatives, trials and the global vendor/operator status in terms of product development and network deployments. A large selection of course projects and guest lectures from major infrastructure vendors and operators are intended to complement the material covered in the lectures.
ECE1552H Modern Mobile Air Interfaces
Prerequisites: ECE316H1
This course provides an in-depth coverage of modern mobile air-interfaces, focusing mainly on the fourth (4G) and fifth generation (5G) of cellular networks. Following the introduction to multicarrier transmission, the key elements of layer 1, 2 and 3 of air interfaces of the 4G and 5G systems are covered in detail. Frequency division duplex and time division duplex solutions are compared and contrasted, and the differences between two main frequency ranges (i.e. below and above 6 GHz) are highlighted. Finally, the last segment of the course covers some more advanced topics, such as carrier aggregation, dual connectivity, massive machine type communication and ultra-reliable low latency communication. Students will get the latest updates from the 3GPP standardization process as they become available, and study the impact of these changes on the performance improvement of mobile networks. Additionally, students will be exposed to practical problems that operators and infrastructure vendors are facing on daily basis. Two course projects will help students to supplement the learning material within the area of their own interest. Also, guest lecturers from major infrastructure vendors and operators will be invited to complement the lecture material.