Post Graduate Certificate in 5G Technology and IoT

IoT has now become a significant component of urban life giving rise to “smart cities,” which aim to transform present day’s urban conglomerate into citizen-friendly and environmentally sustainable living spaces. These IoT devices generate a huge amount of data which could help in better understanding of operations and other significant aspects of the city life. This course aims to make the participants aware of various data mining and machine learning techniques, various format of datasets on which they can be applied to, implementation in python programing language, and interpretation of results for extracting actionable knowledge. Hands-on experiments on various real-life datasets would also be part of this course to get the participants ready to experiment on their domain related novel datasets.

This course focuses on advanced but essential concepts, techniques, and algorithms needed for understanding and designing modern wireless communication systems. You will begin this course by exploring the basics of wireless channels, followed by the need for multi-antenna systems. You will further see such of the two seminal multi-antenna technologies, namely, MIMO and massive MIMO. The first one is the basis for 4G systems, whereas the second one is the same for 5G systems. You will also learn the multiple access techniques and the basics of mmWave communications.

After completing this course, a learner will be able to:

• Understand the fundamentals of modern wireless communication, such as 4G and 5G systems.
• Learn all the essential theories related to their critical technical advancements.
• Comprehend the challenges in realizing them and the way forward.

The objective of this course is two fold. First it will provide an exposure to 3GPP standards mainly focussing on the 5G New Radio standards. This will discuss in detail the data and control transmit and receive among others. Second part of the course will discuss beyond 5G and 6G research topics, namely intelligent reflecting surfaces, cell-free massive MIMO, among others.

Software defined radio is the backbone of the advanced digital communication systems. This course provides practical insight on the selection, design and interaction between different components of a software-defined-radio. The course will give a detailed description of each hardware component, the criterion of the selection, the limitations posed by each such component and the effect on each component on transmitted signal quality. The course will also cover established industry-approved norms and metrics to understand the SDr performance. In addition, Software-defined-radio based algorithms will be discussed to circumvent some of the limitations of hardware to enhance spectrum as well as power efficiency of the overall communication system.

The goal of this course is to introduce the basic concepts and mathematical principles of sparse signal recovery and compressed sensing. The course starts with a deep-dive into the problem of finding a sparse solution to an underdetermined linear system of equations, and introduces the l0-norm minimization problem, which is the central problem of compressed sensing. We study the existence of unique and stable solutions for this canonical problem. We will also study the popular polynomial-time algorithms for solving this otherwise NP-hard problem. Towards the end of this course, we will see how compressed sensing and the sparse signal recovery problem are related and how these ideas can be applied to solve real-world problems related to wireless communications.