PhD Oral Exam - Ali Mohebbi, Electrical and Computer Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

In next-generation wireless communication systems, such as 5G and beyond, channel estimation plays a vital role in ensuring robust and high-performance communication. Given the increasing complexity of wireless environments—characterized by high mobility, massive antenna arrays, and rapidly changing channels—accurate channel estimation is crucial for tasks such as beamforming, spatial multiplexing, interference management, signal detection, and equalization. This thesis focuses on developing novel algorithms for estimating doubly selective channels in MIMO systems and is divided into three parts.

The first part introduces three compressive sensing (CS)-based algorithms for channel estimation in millimeter wave (mmWave) hybrid MIMO systems. These algorithms utilize the basis expansion model (BEM) to efficiently capture the channel’s time variations while significantly reducing the number of unknown channel parameters. The first algorithm is adaptable to various training sequence structures, offering flexibility, while the second and third algorithms use specialized training sequences to reduce computational complexity.

The second part shifts focus to MIMO OTFS systems, recognized for their robustness against Doppler effects and delay spreads. A new row-block sparse formulation is introduced for channel estimation in the delay-Doppler domain, allowing for efficient handling of the MIMO channel matrix by grouping non-zero entries into row blocks. A row-block OMP (RBOMP) algorithm is then applied, enhancing both the accuracy and computational efficiency of the estimation process.

The final part presents another novel doubly selective channel estimation scheme for MIMO OTFS systems, leveraging a two-dimensional discrete prolate spheroidal basis expansion model (2D DPS-BEM). This model provides a more efficient representation of the MIMO channel in the delay-Doppler domain, reducing the number of unknown parameters needed for estimation. Unlike existing CS-based methods, which require prior knowledge of the number of propagation paths, this method relies only on the channel's maximum Doppler and delay shifts, significantly lowering computational complexity. Additionally, a low-overhead pilot scheme is introduced to capture temporal channel variations more efficiently, further enhancing estimation performance.