The “Probability, Random Processes, and Statistics” course introduces undergraduate students to the fundamental concepts of probability theory, random processes, and statistical methods. Emphasis is placed on theoretical understanding and practical applications in science, engineering, and data analytics. The course combines lectures with hands-on tutorials and project-based learning to solidify students’ grasp of the material. Topics are carefully curated to bridge foundational principles with real-world problem-solving skills.
The "Advanced Wireless Communication" course provides an in-depth understanding of modern wireless systems, covering essential topics like antenna link budgets, multipath propagation, and Rayleigh fading. Advanced techniques such as diversity, equalization, and convolution are explored, along with modulation methods and systems like MIMO and OFDM, which are vital in standards like 5G. In addition, channel codes, which improve communication reliability through error correction, will be briefly covered, giving students a well-rounded view of advanced wireless communication technologies and their practical applications.
In this introductory course on mobile communication systems, students explore essential concepts for understanding modern wireless technologies. Topics include mobile radio channel models, error probability derivations, and equalization techniques like maximum ratio combining. The course covers physical layer principles of UMTS and LTE, including CDM(A), OFDM(A), MIMO, and cellular systems aspects such as cell layout and handover. Students also delve into various radio access systems like WCDMA and OFDM variants, interference phenomena, and mitigation strategies. The curriculum includes understanding duplex methods like TDD and FDD, as well as physical layer mechanisms like power control and MAC scheduling. Emerging technologies such as IP convergence and MIMO diversity techniques are discussed alongside existing systems like UMTS/HSDPA/HSUPA/HSPA Evolution and LTE, including coexistence challenges with other wireless technologies. Additionally, students explore services, applications, and higher-layer considerations such as codecs and quality of service transfer.
The "Coding Theory for Storage and Network" course offers advanced insights into efficient data recovery techniques for distributed data storage and network applications. Topics include the principles of Locally Repairable Codes and Regenerating Codes for optimized data recovery in distributed storage systems. Students explore decoding strategies for Reed-Solomon codes, focusing on list decoding and power decoding techniques that surpass half the minimum distance. Additionally, the course covers Interleaved Reed-Solomon codes and algorithms tailored for correcting burst errors, along with Network Coding principles utilizing Rank-metric codes and Subspace codes for error-correction in network environments. Furthermore, students learn about coding methods designed to correct insertions and deletions, as well as specialized techniques for non-volatile memory, including coding for stuck and partially stuck cells. Prerequisites include a solid foundation in mathematical basics, particularly in linear algebra, while familiarity with the "Channel Coding" lecture is recommended for optimal comprehension of the course material.
The "Channel Coding" course introduces modern coding techniques for communication and storage, requiring no prior knowledge. Topics include Channel Coding Applications, Principles such as Channel Models and Decoding Principles, and Finite Fields covering Groups and Fields. Linear Block Codes are explored with concepts like Encoding and Coset Decoding, along with Reed-Solomon Codes including MDS Codes and List Decoding. BCH Codes delve into Minimal Polynomials and Efficient Decoding, while Convolutional Codes cover State Diagrams and Viterbi Decoding. Reed-Muller Codes introduce Simplex Codes and Plotkin Construction. Lastly, Concatenated Codes are discussed along with their Basic Concepts. This course equips students with essential skills in modern coding approaches for various applications in communication and data storage.
The "Computer Networks and its Lab" course offers a comprehensive exploration of network principles supplemented by practical laboratory exercises. Students delve into topics such as internetworking philosophies, unicast and multicast routing, congestion control, quality of service (QoS), mobile networking, router architectures, network-aware applications, content dissemination systems, network security, and performance issues. Material drawn from research papers, industry white papers, and Internet RFCs enriches the learning experience. In the lab component, students gain hands-on experience configuring and troubleshooting network devices, implementing protocols, and analyzing performance. Prerequisites include a solid understanding of networking concepts. By course completion, students acquire the knowledge and skills needed to design, deploy, and manage robust computer networks.