课程教学
Curriculum

Brief course description:

This course starts with a review of Markov chain, random walk, Poisson process, martingale, and limit theorems. The main content includes a few major topics: Markov process (also called continuoustime Markov chain), renewal theory; queueing theory, and Brownian motion.

Brief course description:

This course introduces the basics of information theory and channel coding. It covers Shannon's information measures; Entropy rate of a stationary process; the source coding theorem; Kraft inequality; Huffman code; redundancy of a prefix code; rate-distortion theory and universal data compression. It also covers the capacity for different channel models (e.g., discrete memoryless channels and Gaussian channels), finite field theory, design and analysis of error correcting codes (with a focus on linear block codes), introduction to network information theory.

Brief course description:

This course focuses on convex optimization and its applications to communication systems, information theory, and signal processing. The first part of this course aims to give students the tools and training to recognizing and solving convex optimization problems. Topics include convex sets, functions, and optimization problems including least-squares, linear and quadratic programs, semidefinite programs, and other problems. The second part of the course will be on algorithms for solving convex optimization problems, especially those in communication systems and others. Optimality conditions, duality theory, and interiorpoint methods are presented.

Brief course description:

This course introduces basic principles and advanced techniques in wireless communications. Topics to be covered include physical characteristics of radio channels, channel capacity, modulation and channel coding, MIMO and space-time processing, OFDM and multicarrier systems, spread spectrum and CDMA, opportunistic scheduling and diversity schemes.

Brief course description:

This course introduces the evolution of wireless communication networks towards 5G, including the network architectures, and emerging techniques in radio access networks, transport networks, and core networks, as well as the applications of 5G. The further evolution beyond 5G and 6G and the potential key technologies are also overlooked.

Brief course description:

This course introduces basic theory, methodologies and tools for machine learning, and the intelligent communications driven by machine lerning. It covers supervised learning (support vector machine, neural network and kernel methods), unsupervised learning (ensemble, dimension reduction and deep learning), and reinforcement learning. It also covers the application of machine learning in intelligent communications, such as the intelligent design in radio access networks, transport networks, and core networks.  

Brief course description:

This course provides a technical introduction of fundamental concepts of artificial intelligence (AI). Topics include: fuzzy logic and applications; fuzzy expert systems; fuzzy query; fuzzy data and knowledge engineering; fuzzy control; genetic algorithms and programming and their applications; parallel genetic algorithms; island model and coevolution; genetic programming. The applications of AI in communications, such as AI for B5G wireless transmissions, resource management, and mobility management, are introduced. 

Brief course description:

Dynamic Programming is a fundamental tool widely used to model and solve various engineering problems. This course is developed to study the popular concepts and techniques of dynamic programming. The contents include Principles of Optimality; Dynamic Programming Algorithm; Deterministic Dynamic Programming Problems; Stochastic Dynamic Programming Problems with Perfect and Imperfect Information; Approximate Dynamic Programming and Infinite Horizon Problems. The applications of dynamic programming in communications and networks are also introduced.

Brief course description:

This course covers the basis of image processing and computer vision. We will first cover principles of image formation, operations to alter images, feature extraction and other image processing methods to turn images into abstract descriptions. We will then turn to computer vision topics that discuss how to perceive the structure and semantics of the world, including multi-view geometry, structure from motion, and visual localization and recognition. We will also touch upon related topics in machine learning which are widely used in computer vision. The challenges for transmitting image/video over communications networks and potential technical solutions will be discussed.

Brief course description:

This course will teach optical fiber and transmission characteristics, optical sources (lasers and light-emitting diodes) and transmitters, photodetectors and optical receivers, optical passive and active components: couplers, filters, switches, modulators, EDFA and Raman amplifiers, etc., optical system design, lightwave systems and networks: undersea systems, optical multia-access network design, SONET/SDH, fiber-in-the-loop, passive optical networks, optical network management. In particular, the requirements of optical communications and networks for supporting 5G and Beyond Networks and the potential solutions will be covered in this course.

Brief course description:

This course will teach topics on Internet protocols, applications and systems. It will also provide various case studies on topics such as MPLS, VPN, VoIP, 3G/4G, P2P networking, IETF processes, or recent trends in industry. It also covers the network protocol designs for 5G and Beyond.

Brief course description:

This course introduces the basics of microeconomics, game theory, and mechanism design, with applications in wireless communication networks and Internet. The detailed topics include market mechanisms, consumer surpluses, profit maximization, welfare maximization, pricing, strategic form games, dominator strategy equilibria, Nash equilibrium, Bayesian games, repeated game, social choice functions, incentive compatibility, the revelation theorem, auction design, and network externality.

Brief course description:

This course aims to provide students an understanding in the operating principles and hands-on experience with mainstream Big Data Computing systems. Open-source platforms for Big Data processing and analytics would be discussed.

Brief course description:

This course teachs the fundamental theory and applications of network coding theory. Detailed topics are as follows: Examples of network coding; Acyclic networks: linear network codes and desirable properties, existence and construction, static network codes; Cyclic networks: convolutional network codes; Relations between network coding and classical algebraic coding theory.

Brief course description:

This course describes the market of the Internet of Things (IoT) and the foundation around IoT including the components, tools, and analysis by teaching the concepts behind the IoT and a look at real-world solutions. The technology used to build these kinds of devices, how they communicate, how they store data, and the kinds of distributed systems needed to support them will be taught.

Brief course description:

This course will explore research, frameworks, and applications in Cloud Computing and Edge Computing. This course will begin with a review of current big data analytics frameworks for cloud computing and edge computing, and then explore frameworks for computing over edge devices and cloud.

Brief course description:

This course teaches foundations and principles of distributed systems and parallel computing, including the concepts, principles, tools, techniques, algorithms, and applications for distributed systems and parallel computing. Topics may include: multi-core, client-server, clusters, clouds, grids, peer-to-peer systems, GPU computing, scheduling, scalability, resource discovery and allocation, fault tolerance, security, and MapReduce.

Brief course description:

This course covers the basic concepts and advanced knowledge about thesystem-on-chip (SoC) design, hardware-software co-specification, co-synthesis, system-on-programmable-chip technologies, and multi-core architectures and embedded systems on a chip.

Brief course description:

This course aims to enhance students’ knowledge in cryptography as well as information security and privacy, in both theoretical and practical ways. The course introduces cryptography at an elementary level, enabling students to appreciate its application to information security and privacy. Applications of cryptography will be discussed, including digital certificate and Public Key Infrastructure (PKI), Virtual Private Network (VPN), wireless communication security, as well as security and privacy issues in online social networks.

Brief course description:

Student will work independently under the supervison of a faculty member on a research project in Information Engineering. The topic and scope of the study is to be agreed between the student and the supervisor. A project report is required at the end of the course.

Brief course description:

Student will work independently under the supervison of a faculty member on a research project in Information Engineering. The topic and scope of the study is to be agreed between the student and the supervisor. A project report is required at the end of the course.

Brief course description:

This course teaches the basics of multi-antenna wireless communication, which is one of the key technologies in future wireless communications such as 5G and beyond. Topics covered in this course include wireless channel modelling, capacity of wireless channels, diversity, spatial multiplexing, multi-antenna uplink, multi-antenna downlink, multi-antenna multicasting, and inter-cell interference management.

Brief course description:

This course introduces techniques, software, applications, and perspectives with massive data. The class will cover, but not be limited to, the following topics: data cleaning and pre-processing, classification, regression, clustering, association and correlation rules, ensemble learning and semi-supervised learning, advanced techniques in distributed file systems such as Google File System, Hadoop Distributed File System, Cloud Store, and map-reduce technology; similarity search techniques for big data such as minhash, locality-sensitive hashing; specialized processing and algorithms for data streams; big data search and query technology; managing advertising and recommendation systems for Web applications. The applications may involve business applications such as online marketing, computational advertising, location-based services, social networks, recommender systems, healthcare services, or other scientific applications.

Brief course description:

This course aims to give a comprehensive introduction to Gaussian process for machine learning and signal processing, covering both the theoretical foundation and the practical implementation. Bayesian learning based on Gaussian process models has aroused a lot of attention in recent years due to their great capability and flexibility in representing small, complex data collected from real applications as well as their unique potential to mimic human being’s continual, life-long learning behavior with learning uncertainties.

The lecture series will be taught by an experienced instructor based on the well-established textbook “Gaussian Process for Machine Learning” written by a few world-class researchers from the Cambridge University, and advanced topics based on recent research papers will also be covered with great details. Live use cases and computer implementations of GP models for machine learning, statistical signal processing, and wireless communications will be showcased for a better understanding of the theory taught in classes.