Sum-rate maximization in multi-RIS multi-carrier systems through active power allocation and passive beamforming

In this dissertation, we propose a novel approach to solving the sum-rate maximization problem in a multi-RIS environment within an orthogonal frequency division multiplexing (OFDM) framework. Reconfigurable intelligent surfaces (RIS) offer low-cost and energy-efficient solutions for enhancing wireless communication. In this work, we jointly optimize the transmit power at the base station (BS) and the passive beamforming of RIS reflecting elements to maximize the achievable rate at the user’s location. Three RIS reflection model assumptions are considered and analyzed. In Scenario A, we establish upper and lower bounds for the sum-rate maximization problem by introducing both a relaxed and a restrictive version of the reflection model. This allows us to investigate whether these boundary results provide sufficiently accurate insights for RIS beamforming optimization. In Scenario B, we consider a unit-modulus reflection model for each element, assuming uniform reflection amplitude regardless of physical impairments or dependencies such as frequency and angle of incidence. In Scenario C, we adopt a realistic non-unit-modulus reflection model, which accounts for non-uniform amplitude and phase responses across all reflecting elements. For each reflection model, we formulate the corresponding sum-rate maximization problem and develop fixed-point iteration-based algorithms to jointly optimize power allocation and passive RIS beamforming. Approximations are introduced in the algorithm design to enable tractable, suboptimal solutions for the passive array optimization under each scenario. To validate our approach, we benchmark the proposed methods against an exhaustive search procedure, which guarantees the absolute maximum of the objective function. The results demonstrate that the proposed algorithms closely approximate the optimal sum-rate obtained from exhaustive search while requiring significantly lower computational cost. Furthermore, to position our methods relative to established techniques for multi-carrier sum-rate maximization, we compare them with successive convex approximation (SCA) and majorization-minimization (MM)-based solutions. Simulation results confirm that our proposed methods outperform these existing approaches in achievable sum-rate, while maintaining substantially lower complexity than SCA and achieving similar computational cost to MM.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Foroughi, Somayeh

Advisor dc:contributor.advisor

• Shahbazpanahi, Shahram

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/2044

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/2044