{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129720"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129720","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient characterization and optimization of smart wireless environments","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Ross, Charles"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Peng, Zhen","Bernhard, Jennifer","Schutt-Aine, Jose","Moon, Thomas"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-29","date_published":"2025-04-29","updated_at":"2026-07-22T22:25:05Z","subjects":["Reconfigurable Intelligent Surface","MIMO","Multi-User MIMO","Bayesian Optimization","Channel Model","Ising Hamiltonian","Quantum Annealing","High-Dimensional Optimization","Discrete Optimization"],"languages":["en","eng"],"rights":["Copyright 2025 Charles Ross"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129720","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Zhen","Bernhard, Jennifer","Schutt-Aine, Jose","Moon, Thomas"]},{"key":"dc:creator","label":"Author","values":["Ross, Charles"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-29","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Reconfigurable Intelligent Surface","MIMO","Multi-User MIMO","Bayesian Optimization","Channel Model","Ising Hamiltonian","Quantum Annealing","High-Dimensional Optimization","Discrete Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Charles Ross"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129720"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Charles Ross, accepted the attached license on 2025-04-25 at 10:03.","The student, Charles Ross, submitted this Dissertation for approval on 2025-04-25 at 10:09.","This Dissertation was approved for publication on 2025-04-29 at 09:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21950 on 2025-10-19 at 19:53:58","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Efficient characterization and optimization of smart wireless environments"]}]}],"canonical_facts":{"dc:contributor":["Peng, Zhen","Bernhard, Jennifer","Schutt-Aine, Jose","Moon, Thomas"],"dc:creator":["Ross, Charles"],"dc:date":["2025-04-29","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Charles Ross, accepted the attached license on 2025-04-25 at 10:03.","The student, Charles Ross, submitted this Dissertation for approval on 2025-04-25 at 10:09.","This Dissertation was approved for publication on 2025-04-29 at 09:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21950 on 2025-10-19 at 19:53:58","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129720"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Charles Ross"],"dc:subject":["Reconfigurable Intelligent Surface","MIMO","Multi-User MIMO","Bayesian Optimization","Channel Model","Ising Hamiltonian","Quantum Annealing","High-Dimensional Optimization","Discrete Optimization"],"dc:title":["Efficient characterization and optimization of smart wireless environments"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}