Outage Analysis and Bilevel Resource Optimization in RIS-Assisted Clustered UAV-NOMA Networks
Abstract
This paper investigates RIS-assisted downlink UAV communications based on NOMA. Since conventional NOMA becomes difficult to scale as the number of UAVs grows, we develop a cluster-based UAV-NOMA framework in which a BS serves multiple UAV groups with the aid of partitionable RISs. Within this framework, we consider direct, RIS-only indirect, and composite links, and develop an analytical model for the received SNR and outage performance under Nakagami-m and double Nakagami-m fading with imperfect SIC. To enable scalable transmission, we introduce a greedy RIS-aware (GRIS) clustering method, which groups UAVs according to spatial and channel-related features. Based on the resulting clusters and RIS assignment, we formulate an efficiency-reliability bilevel optimization problem that jointly optimizes inter-cluster BS power allocation, intra-cluster NOMA power coefficients, and RIS element assignment. Specifically, the outer efficiency problem maximizes the average throughput across clusters, while the inner reliability problem minimizes the worst-UAV outage probability within each cluster. To solve the formulated problem, we propose a two-level iterative framework consisting of an inter-cluster outer-efficiency solver (IOS) and an intra-cluster inner-reliability solver (I2S), where I2S combines a grid-based progressive search (GPS) for NOMA power refinement with reliability-guided RIS element reassignment, and IOS updates the BS power weights. Numerical results demonstrate the effectiveness of GRIS clustering, while pre-optimization analysis reveals efficiency-reliability trade-offs in power allocation. Baseline comparisons illustrate the ability of IOS-I2S to balance efficiency and reliability, while ablation studies highlight the importance of joint optimization.
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BibTeX
@misc{ghazikor2026outage,
title = {{Outage Analysis and Bilevel Resource Optimization in RIS-Assisted Clustered UAV-NOMA Networks}},
author = {Masoud Ghazikor and Ly V. Nguyen and Morteza Hashemi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30410},
eprint = {2610.30410},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30410},
}
RIS
TY - UNPB
TI - Outage Analysis and Bilevel Resource Optimization in RIS-Assisted Clustered UAV-NOMA Networks
AU - Masoud Ghazikor
AU - Ly V. Nguyen
AU - Morteza Hashemi
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.30410
UR - https://arxiv.org/abs/2610.30410
AB - This paper investigates RIS-assisted downlink UAV communications based on NOMA. Since conventional NOMA becomes difficult to scale as the number of UAVs grows, we develop a cluster-based UAV-NOMA framework in which a BS serves multiple UAV groups with the aid of partitionable RISs. Within this framework, we consider direct, RIS-only indirect, and composite links, and develop an analytical model for the received SNR and outage performance under Nakagami-m and double Nakagami-m fading with imperfect SIC. To enable scalable transmission, we introduce a greedy RIS-aware (GRIS) clustering method, which groups UAVs according to spatial and channel-related features. Based on the resulting clusters and RIS assignment, we formulate an efficiency-reliability bilevel optimization problem that jointly optimizes inter-cluster BS power allocation, intra-cluster NOMA power coefficients, and RIS element assignment. Specifically, the outer efficiency problem maximizes the average throughput across clusters, while the inner reliability problem minimizes the worst-UAV outage probability within each cluster. To solve the formulated problem, we propose a two-level iterative framework consisting of an inter-cluster outer-efficiency solver (IOS) and an intra-cluster inner-reliability solver (I2S), where I2S combines a grid-based progressive search (GPS) for NOMA power refinement with reliability-guided RIS element reassignment, and IOS updates the BS power weights. Numerical results demonstrate the effectiveness of GRIS clustering, while pre-optimization analysis reveals efficiency-reliability trade-offs in power allocation. Baseline comparisons illustrate the ability of IOS-I2S to balance efficiency and reliability, while ablation studies highlight the importance of joint optimization.
ER -