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Technical University of Munich
Picture of Mahdi Mahvari

M.Sc. Mahdi Mahvari

Technical University of Munich

Chair of Communications Engineering (Prof. Kramer)

Postal address

Theresienstr. 90
80333 München

Biography

I obtained my Bachelor's and Master's degrees in electrical engineering communication systems from Iran University of Science and Technology and Sharif University of Technology, respectively. In my Master's program, I conducted research on coded computing for distributed systems. 

Research Interests

Wireless communication systems increasingly incorporate massive multiple-input multiple-output (MIMO) technology to meet capacity requirements. While massive MIMO shows promising performance, it requires a significant number of devices in the RF chain, power amplifiers, and digital-to-analog converters (DACs). This leads to higher hardware costs and greater power consumption.

A promising approach to address these challenges is to transmit signals with a limited number of amplitude levels, such as binary or few-level (level-constrained) signals. One way to achieve this is to transform conventional high-order constellation symbols into oversampled, level-constrained signals using transmitter precoding.

My current research focuses on the design of such precoders and the evaluation of their performance, particularly in terms of achievable communication rates.

 

The courses and topics that pertain to my research include:

  • Information Theory
  • Wireless Communication
  • Machine Learning in Communication

Teaching

Mobile Communications

Multi-user Information Theory

Machine Learning for Communications

Available Theses

Communication with Coarse Quantization

Description

Motivated by the cell-free and massive MIMO (multiple input multiple outputs) communication scenarios, the number of power amplifiers (PA), digital to analog converters (DAC), etc., is increased. Thus, using coarse quantized transmission reduces the channel's hardware cost and nonlinear effects. More details can be found here.

In this project, we investigate algorithms for mapping modulated data to coarsely quantized signals. We potentially employ linear or non-linear precoding techniques and receiver architectures, comparing our results with classical methods and neural networks.

The student needs an understanding of information theory and communication systems.

Supervisor:

Mahdi Mahvari

Completed Thesis

Channel Estimation in TDD Cell-free Scenario using OTFS Modulation, Master's Thesis, co-supervised with Lorenzo Zaniboni

Comparison of OFDM and OTFS Modulation, Bachelor's Thesis, co-supervised with Lorenzo Zaniboni

Multi-Armed Bandits Algorithms for MIMO Antenna Selection, Master's Thesis, co-supervised with Abdalla Ibrahim

Publications

  1. M. M. Mahvari and G. Kramer, "Stability of Bernstein’s Characterization of Gaussian Vectors and a Soft Doubling Argument," 2023 IEEE Information Theory Workshop (ITW), Saint-Malo, France, 2023, pp. 19-23, doi: 10.1109/ITW55543.2023.10161689.
  2. M. M. Mahvari and G. Kramer, "Stability of Bernstein’s Theorem and Soft Doubling for Vector Gaussian Channels," in IEEE Transactions on Information Theory, vol. 69, no. 10, pp. 6231-6250, Oct. 2023, doi: 10.1109/TIT.2023.3293311.
  3. M. M. Mahvari, M. Kobayashi and A. Zaidi, "Scalable Vector Gaussian Information Bottleneck," 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2021, pp. 37-42, doi: 10.1109/ISIT45174.2021.9517720.
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