Four different aspects of 5G communication systems: cm-wave/mmWave radio propagation, 5G radio interface considerations, antenna system designs, and circuits and networks. 

1. Channel propagation aspect: Accurate characterization of their spatial multipath channel at millimeter wave bands has gained significant interest both in industry and academia, as it is important for system design and performance analysis of future millimeter wave communication systems. This course will briefly discuss basic principle of channel modeling and channel estimation. Further, the students will have hands-on experience of how to perform and measure cm/mm-wave propagation channels. Another aspect related to channel propagation is over-the-air (OTA) testing of 5G devices, e.g. massive MIMO base stations, and mm-wave phased array systems at base station/mobile terminals. This course will also cover basic principle of MIMO OTA testing, and research challenges of OTA testing for 5G systems.

2. 5G radio interface considerations: This part will cover topics such as waveforms, frame structure, access techniques for URLLC and mMTC services, cell less design, multi-node multi-cell connectivity.

3. Antenna aspect: One of the key enabling techniques in 5G systems is the use of millimeter wave bands along with phased array antennas at both the mobile device and base station. This course will address the millimeter-wave antennas and their interactions with human tissues for next generation communication systems. The topics include:

• Summarize the commonly used methods on 5G phased array antenna designs.

• Introduce challenges in centimeter and millimeter wave phased array for 5G mobile terminals.

• Some examples on solving these challenges in mobile terminals.

• Interactions with millimeter-wave antenna and human tissues (body loss and SAR): material properties, measurements and some results.

4. Circuits and networks: With high carrier frequency and wide bandwidth, there are several technical challenges in the design of circuit components and antennas for mmWave communications, e.g. high transmit power, severe nonlinear distortion of power amplifiers, phase noise and IQ imbalance.

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Networks are ubiquitous in our modern society. The World Wide Web that links us to and enables information flows with the rest of the world is the most visible example. It is, however, only one of many networks within which we are situated. Our social life is organized around networks of friends and colleagues. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to everybody else in the United States and in the world. Economic and financial markets also look much more like networks than anonymous marketplaces. Firms interact with the same suppliers and customers and use Web-like supply chains. Financial linkages, both among banks and between consumers, companies and banks, also form a network over which funds flow and risks are shared. Systemic risk in financial markets often results from the counterparty risks created within this financial network. Food chains, interacting biological systems and the spread and containment of epidemics are some of the other natural and social phenomena that exhibit a marked networked structure.

This section will introduce the tools for the project of networks. It will show how certain common principles permeate the functioning of these diverse networks and how the same issues related to robustness, fragility, and interlinkages arise in several different types of networks.

Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spam filtering to robot navigation. And Probability and inference are used everywhere. For example, they help us figure out which of your emails are spam, what results to show you when you search on Google, how a self-driving car should navigate its environment, or even how a computer can beat the best Jeopardy and Go players! What do all of these examples have in common? They are all situations in which a computer program can carry out inferences in the face of uncertainty at a speed and accuracy that far exceed what we could do in our heads or on a piece of paper.

In this data analysis and computer programming section, you will learn the principles of probability and inference. We will put these mathematical concepts to work in code that solves problems people care about. You will learn about different data structures for storing probability distributions, such as probabilistic graphical models, and build efficient algorithms for reasoning with these data structures.

By the end of this course, you will know how to model real-world problems with probability, and how to use the resulting models for inference.

You don’t need to have prior experience in either probability or inference, but you should be comfortable with basic Python programming and calculus.