Efficient characterization and optimization of smart wireless environments

Reconfigurable intelligent surfaces (RIS) and Multi-user MIMO (MU-MIMO) have emerged as promising technologies for enhancing the system through- put, channel capacity, and energy efficiency of wireless networks, transforming static propagation environments into dynamic spaces that may adapt in real time. These technologies introduce a large number of tunable parameters, creating a high-dimensional design space that presents significant computational challenges. Fully realizing their potential requires 1) learning the non-linear dependency of these parameters and 2) efficiently optimizing the learned function. In this thesis, we will describe different frameworks that address these challenges. To begin, the equivalence between RIS optimization and Ising Hamiltonians is explored. With this formulation of the problem, RIS may be optimized using quantum and quantum-inspired algorithms. This representation captures a wide range of RIS use cases, including multiple receivers, multipath environments, and distributed RIS. We next consider the challenge of characterizing the functional dependence. Based on the Ising representation, a novel algorithm, tensor contraction with regression (TCR), is used to learn the end-to-end channel. TCR enables rapid convergence, with minimal pilot overhead, to the ideal RIS phase configuration. Next, we explore a Bayesian Optimization (BO) framework equipped with physics-informed dictionary embedding. This approach eliminates the need for explicit channel estimation and enables sample-efficient optimization of RIS configurations. We extend this framework with a fully Bayesian surrogate model using structured priors, facilitating joint optimization of RIS and precoding vectors in MU-MIMO systems. Our method effectively addresses the mixed discrete-continuous nature of the design space, demonstrating superior performance across diverse wireless scenarios while significantly reducing computational complexity compared to conventional approaches.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Ross, Charles

Contributors dc:contributor

• Peng, Zhen
• Bernhard, Jennifer
• Schutt-Aine, Jose
• Moon, Thomas

• Reconfigurable Intelligent Surface
• MIMO
• Multi-User MIMO
• Bayesian Optimization
• Channel Model
• Ising Hamiltonian
• Quantum Annealing
• High-Dimensional Optimization
• Discrete Optimization

Statement dc:rights

• Copyright 2025 Charles Ross

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129720

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129720