Efficient Resource Allocation in RIS-Assisted UAVs-Enabled B5G/6G Networks Using Digital Twins

Rapid technological advancements have revolutionized how people live, work, and communicate. Given the rapidly growing need for data and bandwidth, replacing existing communication systems with agile and modern alternatives is crucial. This work investigates a novel communication system that combines Uncrewed Aerial Vehicles (UAVs), Digital Twins (DTs), and Reconfigurable Intelligent Surfaces (RIS). RIS, composed of passive metasurfaces, can intelligently manipulate wireless channels with minimal power consumption, enhancing signal quality within its coverage area. UAVs, acting as flying base stations, further augment coverage and capacity. Leveraging the capabilities of UAVs and RIS, a DT of the network is created to address resource allocation challenges proactively. This paper explores efficient resource allocation in this DT-assisted, UAV-aided RIS system using combinatorial optimization techniques. The problem formulation leads to a Mixed-Integer Non-Linear Programming (MINLP) problem, which is inherently non-convex and computationally challenging due to its Nondeterministic Polynomial-time Hardness (NP-hard) classification. An $\epsilon $ -optimal branch-and-bound approach is proposed to address this complexity, optimizing resource allocation and maximizing system throughput. The results of simulations validate that the proposed algorithm significantly enhances resource allocation performance in terms of network throughput and admitted users for various QoS requirements. The result section also presents simulation outcomes comparing the proposed algorithm against other leading methods.