Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

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Darem, AA, Alkhaldi, TM, Alhashmi, AA, Mansouri, W, Alghawli, ASA and Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X, 2024. Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks. Journal of King Saud University - Computer and Information Sciences: 102238. ISSN 1319-1578

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Official URL: https://doi.org/10.1016/j.jksuci.2024.102238

Abstract

Sixth-generation (6G) communication advancements target massive connectivity, ultra-reliable low-latency communication (URLLC), and high data rates, essential for IoT applications. Yet, in natural disasters, particularly in dense urban areas, 6G quality of service (QoS) can falter when terrestrial networks—such as base stations—become unavailable, unstable, or strained by high user density and dynamic environments. Additionally, high-rise buildings in smart cities contribute to signal blockages. To ensure reliable, high-quality connectivity, integrating low-Earth Orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RIS) into a multilayer (ML) network offers a solution: LEO satellites provide broad coverage, UAVs reduce congestion with flexible positioning, and RIS enhances signal quality. Despite these benefits, this integration brings challenges in resource allocation, requiring path loss models that account for both line-of-sight (LOS) and non-line-of-sight (NLOS) links. To address these, a joint optimization problem is formulated focusing on resource distribution fairness. Given its complexity, a framework is proposed to decouple the problem into subproblems using the block coordinate descent (BCD) method. These subproblems include UAV placement optimization, user association, subcarrier allocation via orthogonal frequency division multiple access (OFDMA), power allocation, and RIS phase shift control. OFDMA efficiently manages shared resources and mitigates interference. This iterative approach optimizes each subproblem, ensuring convergence to a locally optimal solution. Additionally, we propose a low-complexity solution for RIS phase shift control, proving its feasibility and efficiency mathematically. The numerical results demonstrate that the proposed scheme achieves up to 43.5% higher sum rates and 80% lower outage probabilities compared to the schemes without RIS. The low complexity solution for RIS optimization achieves performance within 1.8% of the SDP approach in terms of the sum rate. This model significantly improves network performance and reliability, achieving a 16.3% higher sum rate and a 44.4% reduction in outage probability compared to joint optimization of SAT-UAV resources. These findings highlight the robustness and efficiency of the ML network model, making it ideal for next-generation communication systems in high-density urban environments.

Item Type:	Journal article
Publication Title:	Journal of King Saud University - Computer and Information Sciences
Creators:	Darem, A.A., Alkhaldi, T.M., Alhashmi, A.A., Mansouri, W., Alghawli, A.S.A. and Al-Hadhrami, T.
Publisher:	Elsevier BV
Date:	15 November 2024
ISSN:	1319-1578
Identifiers:	Number Type 10.1016/j.jksuci.2024.102238 DOI S1319157824003276 Publisher Item Identifier 2290154 Other
Rights:	This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	19 Nov 2024 13:15
Last Modified:	19 Nov 2024 13:15
URI:	https://irep.ntu.ac.uk/id/eprint/52611