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.