Communications Catalogue

Please note that not all courses listed in this catalogue and/or on ACORN/ROSI will be offered each year.

 

I. Fundamentals

ECE537H1  Random Processes
Professor A. Leon-Garcia

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
Professor F.R. Kschischang

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
Professor W. Yu
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
Professor A. Khisti

This course will provide a mathematical introduction to the principles of statistical learning. We will cover the theory of generalization including PAC learning, VC dimension  and the Bias/Variance decomposition. A number of learning algorithms/techniques for regression, classification and unsupervised learning will be studied from a  mathematical point of view. The course is intended for students doing research in mathematically rigorous disciplines and having a certain level of mathematical maturity. Students primarily interested in learning about machine learning will find ECE1513 more suitable.

Please note that students who have already completed or are enrolled in ECE521H1 or CSC411H1/CSC2515H are not permitted to enroll in ECE1504H. Students are not permitted to take both ECE1513H and ECE1504H
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ECE1505H  Convex Optimization
Professor W. Yu
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.
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ECE1508H Special Topics in Communications: From Quantum Communications to Quantum Internet
Professor H.-K. Lo

This course provides an introduction to quantum communications. While other quantum communication protocols such as quantum bit commitment will be discussed, the focus is on quantum key distribution (QKD), which offers “unconditional security” in communication. Topics include the basics of quantum mechanics and quantum information, protocols of quantum key distribution, security proofs, experimental implementations, quantum hacking and counter-measures. Recent progress in ground to satellite QKD and long distance quantum networks with both trusted and untrusted relays will be mentioned. Time permitting, topics such as quantum repeaters and blind quantum computation may also be explored.
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ECE1508H Special Topics in Communications: Graphs, Codes, and Inference
Professor S. Draper

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 compressive sensing.  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; and threshold analysis via density evolution.  This year the intent will be to abbreviate the focus on error correction to make space to discuss novel topical uses of graphs such as the emerging field of signal processing on graphs (“graph-SP”) and compressed sensing, especially signal recovery via approximate message passing (AMP) which was motivated by the algorithms used to decode graph-based codes.  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.

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ECE1508H Special Topics in Communications:  Cyber-Physical Security of the Smart Grid
Professor D. Kundur
 The electric smart grid promises increased capacity, reliability and efficiency through the marriage of cyber technology with the existing electricity network. This integration, however, creates a new host of vulnerabilities stemming from cyber or physical intrusion potentially leading to devastating physical effects. The security of a system is as strong as its weakest link. Thus, the scale and complexity of the smart grid, along with its increased connectivity and automation make the task of cyber-physical protection particularly challenging. This course introduces students to timely topics in cyber-physical security of modern power systems. Topics include: introduction to communication security practices; power system security and stability; cyber-physical system attacks; distributed control and network adaptation strategies for smart grid resilience.
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ECE1508H  Special Topics in Communications:  Multiuser Information Theory
Professor A. Khisti
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.
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ECE1508H Special Topics in Communications: Network Softwarization: Technologies and Enablers
Professor A. Leon-Garcia

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).
Students must successfully complete both ECE1508H (Special Topics in Communications: Network Softwarization: Technologies and Enablers) as well as ECE1548H in order to qualify for industry internships in the area of network softwarization
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ECE1510H Advanced Inference Algorithms
Professor B. Frey
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.
Prerequisite: ECE521H1 or equivalent.
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ECE1513H Introduction to Machine Learning
Professor A. Khisti

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.

Please note that students who have already completed or are enrolled in ECE521H1 or CSC411H1/CSC2515H are not permitted to enroll in ECE1513H. Students are not permitted to take both ECE1513H and ECE1504H

II. Signal Processing


ECE1511H Signal Processing
Professor D. Hatzinakos
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.
Prerequisites: ECE310H1, ECE431H1, ECE302H1 or equivalent.
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ECE1512H Digital Image Processing and Applications
Professor K.N. Plataniotis
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.
Prerequisites: ECE431H1 or equivalent.
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ECE1514H Spectral Analysis and Array Processing
Professor D. Hatzinakos
Spectrum estimation is an important area of digital signal processing that finds applications in sonar and radar, geophysics and oil exploration, radioastronomy, biomedicine, speech and image processing. This course will cover the basic principles and wide variety of signal processing techniques developed for spectral analysis. Topics include: definitions of power spectrum; conventional spectrum estimation methods; maximum likelihood method of Capon; maximum entropy method; parametric modeling of time series; AR and ARMA spectrum estimation; harmonic decomposition techniques; duality between spectral analysis and array processing; signal and noise subspace methods in array processing. Higher-order-spectral analysis methods and applications.
Prerequisites: ECE310H1, ECE431H1, ECE302H1 or equivalent.
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ECE1517H Biometric Systems
Professor D. Hatzinakos
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.
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ECE1518H Seminar in Identity, Privacy and Security
Professor D. Hatzinakos
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.
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III. Communications


ECE1520H
Data Communications I
Professor A. Khisti
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.
Prerequisites: ECE417H1 or equivalent.
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ECE1521H Detection and Estimation Theory
R. Adve
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.
Prerequisites: ECE302H1 or equivalent.
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ECE1524H Service Provider Networks
A major promise for service providers is to design, operate, maintain and retire innovative network services in a proper manner. To achieve this goal service provider’s infrastructure should be well connected, highly reliable and extremely secure. Moreover, service provider has to offer manageable level of quality for offered services along with a customer-oriented service management platform. The course offers an overview of these key elements and explains how one can build a successful service life-cycle in a multi-service provider world. The course discusses the architecture of a service provider in a broad sense, including topology and infrastructure, data forwarding and signaling architecture, service and QoS architecture, and security architecture. Form operational and design standpoint, the course focuses on two major problems: capacity planning and traffic engineering. Basics of traffic trending and flow analysis in service provider domain will be explained (engineering perspective). The course will discuss the evolution of service provider networks and the requirements for next generation of service providers. To this end the impact of new concepts such as “Cloud Computing” and “Software Defined Networking (SDN)” on service design and network management will be evaluated. Finally, the competitiveness of a service provider considering all above topics will be considered.
Please note that this course is open to ECE M.Eng. students only
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ECE1528H Special Topics in Data Communications: Stochastic Networks
Professor B. Liang
An introduction to analysis, control, and optimization in communication networks using stochastic models. We cover both classical queueing networks and recent advances in stochastic network modeling and optimization. Topics include Jackson and Whittle networks, reversible network processes, Palm probabilities, space-time Poisson models, stochastic geometry, network utility maximization, Lyapunov stability, and stochastic network optimization.
Prerequisite: ECE1500H/ECE537H1 or equivalent 
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ECE1529H Adaptive Systems for Signal Processing and Communications
Professor K.N. Plataniotis
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.
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ECE1531H Quantum Information Theory
Professor H-K. Lo
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.
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ECE1545H Special Topics in Data Communications: Stochastic Network Calculus (formerly ECE1528H)
Professor J. Liebeherr
The network calculus has evolved into an elegant framework for analytical performance evaluation of data networks. It provides methods to determine resource requirements of traffic flows in a network using an envelope description for arrivals and service. Particular strengths of the network calculus are that it can elegantly characterize delays and backlog in a network of multiple nodes, and that it can accurately describe the behavior of a wide variety of link scheduling algorithms. More recently, stochastic extensions of the network calculus have resulted in the discovery of new scaling laws of network delays, and an end-to-end analysis of a network with heavy-tailed traffic. Different from queueing theoretic approaches to network analysis, which often seek an exact characterization of traffic and service, a network calculus analysis seeks to obtain bounds for performance metrics, thus reducing the complexity of the analysis. The course will cover the deterministic and stochastic network calculus, as well as elements of min-plus linear system theory. Applications of network calculus include the design of networks with service differentiation, real-time systems, and wireless networks. The course also covers applications of min-plus system theory for developing methods for bandwidth estimation in wired and wireless networks.
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IV. Networks

 

ECE1541H  Communication Networks I
Professor S. Valaee

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.
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ECE 1542H Communication Networks II
Professor S. Valaee

Topics include: layering, distributed algorithms, network algorithms, shortest path routing, coping with link failures, optimal routing and topology design, fairness, flow control.
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ECE1543H Mobile Communications Systems
Professor E.S. Sousa

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.
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ECE1545H Bridges and Routers
Professor J. Liebeherr

Basic concepts. Data link layer issues: Transparent bridges, learning bridges, spanning tree algorithm. Source routing bridges, interworking with . Networtransparent bridgesk layer issues: Service interface, addressing, address resolution protocol, routing algorithms, routing protocols, QOS issues: Integrated services, RSVP; Differentiated Services, Tag Switching and MPLS.
Prerequisites: ECE460H1 or equivalent.
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ECE1548H Advanced Network Architectures: Network Softwarization: Principles and Foundations
Professor A. Leon-Garcia

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).
Students must successfully complete both ECE1508H (Special Topics in Communications: Network Softwarization: Technologies and Enablers) as well as ECE1548H in order to qualify for industry internships in the area of network softwarization
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ECE1549H Stochastic Networks
Professor B. Liang
An introduction to the modeling and analysis of stochastic networks. We cover both classical Markovian queueing networks and recent advances in network analysis and optimization. Topics include Jackson and Whittle networks, Φ-balance, reversible Markov chains, Kolmogorov criterion, reversible network processes, Kelly and BCMP networks, point processes, Lévy’s formula, Poisson transitions and flows, Palm probabilities, MUSTA property, stationary functionals, Campbell-Mecke formula, Laplace functionals, stochastic geometry, Poisson point processes, marked point processes, Lyapunov stability, network utility maximization, and stochastic network optimization.
Prerequisite: ECE1500H/ECE537H1 or equivalent with continuous time Markov chains.
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ECE1550H  Physics of Information
Professor H.-K. Lo
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
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ECE1551H Mobile Broadband Radio Access Network
This course covers Radio Access Network (RAN) aspects of the 4G Long Term Evolution (LTE / LTE-A) mobile broadband communication systems, as well as gives a preview of the RAN evolution towards 5G. In addition to learning the important aspects of modern mobile networks, such as network design concepts, physical and L2/L3 layers; the 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. The RF concepts of channels, frequency band plans and the same basic network planning will be included as well. Moreover, we will go over the architectural solutions, remote radio heads (distributed radio solutions), and important hardware components in the network. 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.
Prerequisite: ECE316H1
Course Exclusion: ECE1508H “Special Topics in Communications: 4G LTE for Mobile Broadband and Evolution Towards 5G”
Please note that this course is open to ECE M.Eng. students only
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ECE1552H Modern Mobile Air Interfaces
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.
Please note that this course is open to ECE M.Eng. students only
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