Curriculum Overview
The Communication Systems program at Electronics Service And Training Centre is designed to provide students with a robust foundation in both theoretical and applied aspects of modern communication technologies. The curriculum spans four years, integrating core engineering disciplines with specialized tracks tailored to emerging industry needs.
Year One: Foundation Building
The first year focuses on establishing a strong base in mathematics, physics, and basic electronics. Students are introduced to fundamental concepts that will be expanded upon in subsequent years. Courses include Mathematics I, Physics for Engineers, Basic Electronics, Programming Fundamentals, and Engineering Graphics.
Year Two: Core Engineering Principles
In the second year, students delve into core engineering principles such as circuit analysis, electromagnetic fields, signals and systems, and data structures. This foundational knowledge prepares them for advanced topics in communication systems.
Year Three: Advanced Communication Systems
The third year introduces advanced subjects like analog and digital communication, probability and random processes, information theory, network protocols, and signal processing. Students also begin exploring elective options to tailor their studies towards specific interests.
Year Four: Specialization and Capstone
The final year emphasizes specialization through advanced electives and culminates in a capstone project. Students may choose tracks such as AI in Communication Systems, Cybersecurity for Networks, Satellite and Space Communications, or IoT-based Solutions.
Course Listing by Semester
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | CS101 | Mathematics I | 3-1-0-4 | - |
| 1 | CS102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CS103 | Basic Electronics | 3-1-0-4 | - |
| 1 | CS104 | Programming Fundamentals | 2-0-2-3 | - |
| 1 | CS105 | Engineering Graphics | 2-0-0-2 | - |
| 2 | CS201 | Mathematics II | 3-1-0-4 | CS101 |
| 2 | CS202 | Electromagnetic Fields | 3-1-0-4 | CS102 |
| 2 | CS203 | Circuit Analysis | 3-1-0-4 | CS103 |
| 2 | CS204 | Data Structures and Algorithms | 3-0-0-3 | CS104 |
| 2 | CS205 | Signals and Systems | 3-1-0-4 | CS201, CS202 |
| 3 | CS301 | Analog Communication | 3-1-0-4 | CS205 |
| 3 | CS302 | Digital Communication | 3-1-0-4 | CS205 |
| 3 | CS303 | Probability and Random Processes | 3-1-0-4 | CS201 |
| 3 | CS304 | Information Theory | 3-1-0-4 | CS303 |
| 3 | CS305 | Network Protocols | 3-1-0-4 | CS203 |
| 4 | CS401 | Wireless Communication | 3-1-0-4 | CS302 |
| 4 | CS402 | Satellite Communication | 3-1-0-4 | CS301 |
| 4 | CS403 | Signal Processing | 3-1-0-4 | CS302 |
| 4 | CS404 | Embedded Systems | 3-1-0-4 | CS204 |
| 4 | CS405 | Optical Fiber Communication | 3-1-0-4 | CS301 |
| 5 | CS501 | Cybersecurity for Networks | 3-1-0-4 | CS401 |
| 5 | CS502 | Quantum Communication | 3-1-0-4 | CS304 |
| 5 | CS503 | AI in Signal Processing | 3-1-0-4 | CS304 |
| 5 | CS504 | Internet of Things (IoT) | 3-1-0-4 | CS404 |
| 6 | CS601 | Advanced Topics in Communication Systems | 3-1-0-4 | CS502 |
| 6 | CS602 | Capstone Project I | 2-0-0-2 | - |
| 7 | CS701 | Capstone Project II | 2-0-0-2 | CS602 |
| 7 | CS702 | Research Methodology | 3-1-0-4 | - |
| 8 | CS801 | Thesis/Capstone | 4-0-0-4 | CS701 |
Advanced Departmental Electives
- Deep Learning for Signal Processing: This course explores how deep neural networks can be applied to enhance signal processing tasks such as noise reduction and feature extraction. Students learn to design and implement custom architectures using TensorFlow and PyTorch.
- Network Security and Cryptography: Covers encryption methods, firewall architectures, and security protocols used in modern communication systems. Students gain hands-on experience through labs involving packet analysis tools like Wireshark and intrusion detection systems.
- RF Circuit Design: Focuses on designing and simulating radio frequency circuits for wireless applications using industry-standard tools like Cadence and Keysight. Emphasis is placed on matching networks, filters, and amplifiers.
- Wireless Sensor Networks: Explores deployment strategies, energy efficiency, and data aggregation in sensor networks used for environmental monitoring and smart cities. Students implement real-time monitoring systems using Arduino and Raspberry Pi platforms.
- Software Defined Radio (SDR): Introduces students to SDR platforms like GNU Radio and their role in modern communication systems. Labs involve implementing communication protocols and analyzing spectrum usage patterns.
- Mobile Network Optimization: Students study techniques for improving network performance in LTE and 5G environments through load balancing and interference management. The course includes case studies from major telecom operators.
- Signal Detection and Estimation: Teaches principles of hypothesis testing and estimation theory used in radar and communication systems. Applications include radar cross-section estimation, parameter estimation in noisy channels, and optimal detector design.
- Quantum Key Distribution: Explores how quantum mechanics can be harnessed for ultra-secure communication. Students engage with experimental setups involving photon polarization and Bell inequality tests.
- IoT and Smart Cities: Examines the integration of IoT technologies in urban planning, traffic management, waste reduction, and public safety. Case studies from cities like Singapore and Barcelona are analyzed to understand implementation strategies.
- Advanced Satellite Communications: Covers satellite link design, space-based networking, orbital mechanics, and signal propagation models. Students simulate scenarios using specialized software tools such as MATLAB/Simulink.
Project-Based Learning Philosophy
The department's approach to project-based learning is rooted in the belief that students learn best when they are actively engaged in solving real-world problems. Projects begin in the third year with mini-projects and evolve into full-scale capstone projects over the final two years.
Mini-Projects
These are typically three-month initiatives assigned at the end of the third year. Each project is designed to reinforce concepts learned in core courses while encouraging interdisciplinary collaboration. Students work in teams under faculty supervision, with milestones set for progress tracking and feedback.
Final-Year Thesis/Capstone Project
The final-year project spans an entire academic year and involves extensive research, design, and implementation phases. Students select a topic from emerging areas or industry challenges, often in collaboration with external partners. A public defense session is held at the end of the year where students present their work to an expert panel.
Selection Process
Students begin selecting projects in the fifth semester, guided by faculty mentors based on academic performance and interest areas. The selection process includes a proposal submission followed by a review meeting with the project committee.