Advanced Multi-Antenna Systems
- Lecture: Weekly, Monday (starts 13.10.25), 4th DS (13:00 – 14:30), in room BAR 0I89/U
- Exercise: Bi-weekly, Friday (starts 24.10.25), 3rd DS (11:10 – 12:40), in room BAR 0I89/U
- Online link: MS Teams
Updates:
ℹ️ Solutions of the first set of tasks are available
ℹ️ Third exercise session on Friday (21.11.25):
- Exercise set : 2 × 2 MIMO analysis
ℹ️ Exercise set 3: MATLAB implementation is Homework for collecting bonus points
Course Objectives
This course provides a comprehensive introduction to Multiple-Input Multiple-Output (MIMO) systems, a foundational technology in modern wireless communications. The objective is to equip students with both the theoretical understanding and practical insights needed to analyze, design, and simulate MIMO transceivers. Topics include signal modeling, spatial multiplexing, diversity techniques, beamforming, hybrid analog/digital architectures, and the role of MIMO in emerging applications such as millimeter-wave communication and integrated sensing and communication (ISAC). Through lectures, tutorials, and optional simulation-based assignments, students will learn how to derive and interpret system models, evaluate performance limits using tools from linear algebra and information theory, and understand key implementation trade-offs in real-world systems.
By the end of the course, students will be able to:
- Formulate and analyze MIMO system models across different architectures.
- Understand the capacity, reliability, and spatial processing capabilities of MIMO.
- Apply mathematical tools (e.g., SVD, matrix decompositions) to optimize transmission.
- Differentiate between point-to-point and multi-user MIMO scenarios.
- Evaluate the role of beamforming and hybrid transceiver designs.
- Recognize current research directions in MIMO-enabled wireless systems and sensing.
Prerequisites
Students are expected to have a solid foundation in key areas of communication engineering and mathematical tools.
- A solid understanding of wireless communications and signals and systems, including signal propagation, communication channel, analog and digital signal analysis in both time and frequency.
- Solid skills in linear algebra, including matrix operations, vector spaces, eigenvalue decomposition, and singular value decomposition (SVD).
- Basics of information theory and probability theory and statistics, including entropy, mutual information, and capacity limits.
- A background of estimation and detection principles.
- Basic understanding of OFDM will be beneficial.
The course will offer a review of the basics for convenience.