Programme Content
Course 1: Introduction to Machine Learning (3 Credits)
Supervised and unsupervised learning
Neural networks and deep learning
Reinforcement learning for network optimisation
ML for signal processing and noise reduction
AI-driven network traffic prediction and anomaly detection
Application of ML in 5G and IoT systems
Learning outcomes
Understand supervised and unsupervised learning, deep learning, and reinforcement learning.
Apply machine learning for signal processing, noise reduction, and network traffic prediction.
Implement AI-driven anomaly detection in communication systems.
Explore ML applications in 5G/6G and IoT networks for performance optimisation.
Course 2: Wireless Communications (3 Credits)
Cellular network evolution (4G, 5G, and 6G)
Channel modeling and estimation
Modulation and coding schemes
Spectrum efficiency and resource allocation
Interference management techniques, IoT/M2M communication protocols
Learning outcomes
Explain cellular network evolution (4G, 5G, 6G) and their technical advancements.
Understand channel modeling, modulation, coding, and spectrum efficiency in wireless networks.
Develop interference management techniques for IoT/M2M communication.
Analyse resource allocation strategies for optimising wireless communications.
Course 3: Selected Topics in Communication Systems and Networking-I (3 Credits)
Classical Security
Quantum entanglement and teleportation in communication
Quantum key distribution (QKD) protocols
Quantum repeaters and networking nodes
Integration of Quantum systems with classical networks
Applications of Quantum communication in secure networks
Learning outcomes
Understand the principles of quantum entanglement and quantum key distribution (QKD).
Analyse the role of quantum repeaters and quantum networking nodes in secure communication.
Explore hybrid classical-quantum networking models for data security.
Study real-world applications of quantum communication in secure networks.
Course 4: Wireless Communication Laboratory (3 Credits)
Basics of MATLAB/Python
To plot BER versus SNR plots for BPSK, QPSK, 16-QAM constellations over Rayleigh fading channel
To plot average capacity versus SNR plot for M-Ary constellations over Rician Fading Channel
To implement OFDM transmitter and receiver
To implement supervised learning detector for wireless channels
To evaluate performance of wireless channels using LDPC codes
To implement ISAC system in MATLAB
To implement QKD protocols
Learning outcomes
Learners will understand key concepts of quantum communication, including entanglement, teleportation, and Quantum Key Distribution (QKD) protocols for secure data transmission. They will explore the role of quantum repeaters and networking nodes in enhancing communication efficiency and analyse the integration of quantum systems with classical networks. Gain expertise in Software-Defined Radios (SDRs) and their applications.
2. Semester 2Course 5: MIMO Wireless Communications (3 Credits)
Introduction to Space-Time Diversity
MIMO Channel
MIMO Information Theory
Error probability analysis
Transmit diversity and space time coding
Linear STBC design
Differential coding for MIMO
Precoding
Multiuser MIMO
Massive MIMO
Recent advancement in MIMO
Learning outcomes
Understand the fundamentals of MIMO systems, spatial multiplexing, and diversity gain
Implement MIMO systems for improved spectral efficiency and network reliability
Develop real-time signal processing techniques for 5G/6G networks
Test and analyse advanced beamforming techniques in wireless communication
Course 6: Selected Topics in Communication Systems and Networking-II (3 Credits)
Key 5G technologies
5G numerology
5G frame structure
Physical downlink shared channel(PDSCH)
Physical Downlink Control Channel(PDCCH)
Tansmit chain
Demoulation reference signal(DM-RS)
Sounding Reference Signal(SRS)
MIMO in 5G
Learning outcomes
Analyse and apply the principles of standards-based wireless system design
Design and implement a 5G-compliant wireless communication system using MATLAB
Evaluate and discuss current trends and emerging technologies in the evolution of 5G networks
Analyse energy-efficient and green communication strategies in wireless networks
Implement advanced beamforming algorithms for better network performance
Course 7: Selected Topics in Information Processing-I (3 Credits)
Advanced quantum error correction codes
Post-quantum cryptographic algorithms
Hybrid classical-quantum communication systems
Fault-tolerant quantum network designs
Quantum-enhanced protocols for secure data transmission
Learning outcomes
Understand advanced quantum error correction codes and post-quantum cryptography
Explore hybrid classical-quantum communication systems for secure data transmission
Design fault-tolerant quantum network architectures for real-world applications
Implement quantum-enhanced security protocols for future communication systems
Course 8: Major Project Part-I (Communication Engineering) (6 Credits)
Practical project work on quantum communication, network security, or advanced wireless systems.
Hands-on experimentation with quantum key distribution (QKD), hybrid classical-quantum systems, and testing of new communication protocols.
Learning outcomes
Apply theoretical concepts to practical projects in quantum communication, network security, or advanced wireless systems.
Gain hands-on experience with Quantum Key Distribution (QKD), hybrid classical-quantum systems, and the testing of new communication protocols.
Enhance problem-solving skills, research capabilities, and technical proficiency in cutting-edge communication technologies.
Total Credits: 27 credits
NOTE: The sessions will be delivered by IIT Delhi faculty and Industry Experts, bought by the Programme Coordinator only.
**Kindly Note: Curriculum is subject to change and modification, as per the requirements of the programme. IIT Delhi and the Programme Coordinator’s decision will be final.
3. DISCLAIMEROnline PG Diplomas are the academic programme of IIT Delhi, and there is no campus placement or assistance provided from IIT Delhi in these programmes.
The evaluation of minor projects is subject to the faculty's discretion, based on academic guidelines and instructional objectives.
Assessment criteria may vary depending on the nature of the project and its alignment with the course framework.
Basics of MATLAB/Python
To plot BER versus SNR plots for BPSK, QPSK, and 16-QAM constellations over a Rayleigh fading channel
To plot average capacity versus SNR for M-ary constellations over a Rician fading channel
To implement an OFDM transmitter and receiver
To implement a supervised learning detector for wireless channels
To evaluate the performance of wireless channels using LDPC codes
To implement an ISAC system in MATLAB
To implement and visualise QKD protocols using the VisualQKD Pro simulator
Software Tools-Visual QKD Pro Simulator
VisualQKD Pro is an advanced, easy-to-use simulation platform that brings Quantum Key Distribution (QKD) to life through real-world-accurate modeling and intuitive visualization. Designed and validated within IIT Delhi’s academic ecosystem, it mirrors practical QKD behavior—matching noise effects, QBER trends, and eavesdropping scenarios with high fidelity. Backed by multiple research white papers, VisualQKD Pro simplifies complex quantum concepts and makes learning immersive, while remaining powerful enough for serious research. Its clean interface, actionable analytics, and exportable results make it a versatile tool for education, training, and R&D in quantum communication and quantum computing.
Software Tools- VisualML Lab Pro
VisualML Lab Pro is a powerful yet remarkably easy-to-use AI/ML platform that transforms data analysis into a fully visual experience. With its drag-and-drop pipeline designer, integrated visualizations, and comprehensive library of ML algorithms and deep-learning methods, the tool enables experts to quickly interpret raw data patterns and select the most suitable algorithm for optimum learning performance and model behavior. At the same time, its intuitive interface and real-time visual feedback significantly accelerate the learning curve for beginners, helping them grasp complex concepts without extensive coding. Visual ML Pro effectively bridges practical engineering and machine-learning theory, making it an ideal companion for education, research, and industry-ready AI development.
