RESOURCE ALLOCATION FOR EMERGING APPLICATIONS IN RIS-ENABLED 6G AND BEYOND NETWORKS

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology capable of enhancing the performance of wireless networks by smartly reconfiguring the wireless propagation environment using low-cost reflective elements integrated on planar surfaces. The controllable signal reflections from RISs are highly beneficial for coverage extension and seamless connectivity, especially in complex urban environments, where signal propagation may often be blocked by high-rise buildings and a large number of city infrastructure. In this context, this thesis investigates potential and practical scenarios of RIS-assisted networks in complex urban propagation environments, e.g., smart cities, and the relevant network optimization objectives. In addition, the study presents innovative algorithms, emerging applications, and deployment strategies for RIS-assisted 6G and beyond wireless networks. Thus, contributing to the advancement of RIS-based beamforming and resource allocation for future wireless networks.

Firstly, we investigate user admission maximization in a challenging RIS-aided smart city street scenario. We jointly optimize multislot scheduling, precoding, and RIS-based beamforming to maximize user admission under quality of service (QoS) and base station (BS) power constraints. To solve the resulting problem with affordable complexity, an efficient iterative algorithm that incorporates binary variable relaxation, alternating optimization (AO), and successive convex approximation (SCA) is proposed. Secondly, a significant contribution of this work lies in investigating typical scenarios in complex propagation environments where multiple RISs are deployed in different hotspot areas to overcome blockages between the BS and users. A robust resource allocation design to ensure fair service access to users in different hotspot areas supported by distributed RISs is proposed. Specifically, we maximize the minimum number of served users in proximity to each RIS subject to the available BS power and worst-case QoS constraints. To solve the challenging problem, we leverage tools from binary variable relaxation, mathematical transformations, convex approximation techniques, and the AO algorithm. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Thirdly, we investigate a communication-centric RIS-enhanced orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, where a RIS is deployed to assist communication users in a localized coverage gap. The objective is to optimize the trade-off between communication and sensing while ensuring energy efficiency. We formulate and study two performance metrics: i) maximize the system's sum spectral efficiency and ii) maximize the global energy efficiency subject to the available BS power, RIS phase shift design, sensing subcarrier allocation, and accuracy constraints. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions.

Finally, we summarize the key conclusions of the thesis and suggest potential directions for future research.