Regular Courses

Course objectives and teaching methodology Convex optimization is now routinely being applied to design machine learning (ML) algorithms and wireless communication systems. This course will be an accessible introduction to the field of convex optimization, focussing on powerful and reliable convex optimization techniques. The emphasis of this course will be on practically understanding different convex optimization techniques. At the end of student will be able to recognize, simplify, model and solve optimization problems, and apply these ideas in their fields.

To achieve these objective

• Underlying convex optimization ideas will be taught in the class.

• Students will then implement convex optimization problems in MATLAB/Python. Even though we will pick examples from machine learning (ML), signal processing and wireless communications, students will be encouraged to take problems from their area for these implementation. The course will therefore involve MATLAB/Python coding component, which will also be considered for evaluation.

Prerequisites: Understanding of linear algebra and probability.

Course Website:

• Jan.-Apr. 2026 [link]

• Taught earlier in: Jan.-Apr. 2025

Course objectives and teaching methodology This course will cover state-of-the-art multiple-input multiple-output (MIMO) wireless transmitter and receiver designs which are being used in the 5G cellular systems. The students will learn about

• Challenges involved in developing and incorporating MIMO designs in practical wireless systems.

• Performance improvements MIMO designs provide over single-antenna designs in different operating scenarios.

Prerequisites: None

Course Website:

• Aug.-Nov. 2025 [link]

• Taught earlier in: Aug.-Nov. 2024, Aug.-Nov. 2023 Aug.-Nov. 2021, Aug.-Nov. 2020 Aug.-Nov. 2019, Aug.-Nov. 2018, Jan.-Apr. 2018, Jan.-Apr. 2017

Course objectives and teaching methodology Machine learning is now routinely being applied to design state-of-the-art 5G wireless systems. The objective of this course is to bridge the gap between theory of machine learning and its applications to 5G systems. To achieve the objective

• Underlying machine learning concepts will be taught in the class. The course will not expect any background on machine learning.

• Students will then design and implement various applications of machine learning for 5G wireless systems in MATLAB. The course will involve MATLAB coding component, which will also be considered for evaluation.

Prerequisites: None

Course Website:

• Jan.-Apr. 2023 [link]

Course objectives and teaching methodology The students normally have strong theoretical background in wireless communications systems, but negligible exposure on the use of this theory to design practical wireless systems. And current jobs in the wireless communication industry require design of standards-based practical wireless systems. The objective of this course is to bridge the gap between the theory and practise of 5G wireless communication systems, and consequently also the gap between academia and industry.

To achieve the objective, underlying concepts of 5G transceivers will be taught in the class, and the students will be expected to read the 5G standard documents to understand the transceiver specifications. The students will then design and implement in MATLAB a 5G compliant wireless system. The course will therefore involve a MATLAB coding component, which will also be considered for evaluation. Lastly students will discuss possible evolutionary paths for the 5G standard.

Content of this course is also highly useful while designing the 5G Testbed and the 5G+/6G Standardization projects being done by my group.

Prerequisites: None.

Course Website:

• Taught in: Jan-Apr. 2019, Jan-Apr. 2020, Aug-Nov. 2022 [link]

Course objectives and teaching methodology: This is a second course in communications, mainly dealing with fundamentals of digital communication systems designs which are now ubiquitous e.g., DTH and cellular systems. We will teach digital transmitter and receiver designs, impairments observed by the transmit signals, and how to handle them. We will also briefly cover the fundamental limits of digital communication systems, and how to achieve them.

Prerequisites: EE320.

Course Website:

• Taught in: Jan-Apr. 2025[link]

Introduction to Electronics

Course objectives and teaching methodology:

• The course offers an introduction to the basic principles of electrical circuit analysis and exposes students to electronic devices and analog and digital electronic circuits

• To provide a panoramic view of the relevance and dependence on other branches of engineering and sciences with concepts encountered in electronics

[Important information]- The course will be run completely live, and will be hosted at the Institute MOOKIT.

Prerequisites: None

Course Website:

• Jan.-May. 2022 [link]

Introduction to Electronics

Course objectives and teaching methodology:

• The course offers an introduction to the basic principles of electrical circuit analysis and exposes students to electronic devices and analog and digital electronic circuits

• To provide a panoramic view of the relevance and dependence on other branches of engineering and sciences with concepts encountered in electronics

[Important information]- The course will be run completely live, and will be hosted at the Institute MOOKIT.

Prerequisites: None

Course Website:

• Jan.-May. 2022 [link]