ORCID

https://orcid.org/0000-0002-1917-2158

Keywords

Localization, mmWave Beam Prediction, Deep Learning

Abstract

The primary focus of this doctoral dissertation is the investigation of robust massive Multiple Input Multiple Output (MIMO) communication systems. The study centers on two closely related issues: i) single-site user localization, and ii) millimeter Wave (mmWave) beam prediction. The motivation is to develop a system that simultaneously offers users reliable communication and precise localization using a single infrastructure. This dissertation specifically addresses three key challenges in data-driven solution: i) the limited availability of position-labeled training datasets, ii) robustness in time-varying dynamic environments, and iii) the generalization of models to unseen scenarios. The study introduces three localization and two beam prediction frameworks each addressing at least one of the key challenges. For localization, the first framework extracts propagation path information from Channel State Information (CSI) samples and integrates it with the environment map to locate the user, eliminating the need for labeled datasets. The second framework reduces the requirement for labeled training data in conventional data-driven models by training a Gaussian Process Regression model on a small dataset of location-labeled CSIs to predict user locations. The third framework employs deep learning sequence prediction to detect and correct the CSI alterations caused by dynamic environmental changes. In the context of mmWave beam prediction, the first framework utilizes sub-6 GHz CSI samples to predict mmWave beams through a deep neural network in dual antenna systems, emphasizing robustness and generalization in dynamic environments. The second framework leverages multimodal deep learning to fuse sensor data collected from the environment to perform prediction. The framework combines blockage detection and beam prediction to enhance mmWave beam prediction performance in complex, dynamic settings.

Completion Date

2024

Semester

Fall

Committee Chair

Nazanin Rahnavard

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Format

PDF

Identifier

DP0029050

Language

English

Release Date

12-15-2024

Access Status

Dissertation

Campus Location

Orlando (Main) Campus

Accessibility Status

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